Effect of Haemolysis on Result Quality in Biochemistry Laboratory

Determination of acceptable levels of haemolysis when accepting blood samples for

Trace mineral analysis.

by

Bronwyn Claudia CloeteStudent Number: 209214511

To be submitted in fulfilment of the requirements for the degree

BACCALAUREUS TECHNOLOGIAE: DISCIPLINE QUALITY

in the

Faculty of Engineering

CAPE PENINSULA UNIVERSITY OF TECHNOLOGY

Supervisor: M Arderne

DECLARATION

“I hereby declare that this dissertation submitted for the degree Baccalaureus Technologiae

at Cape Peninsula University of Technology, is my own original unaided work and has not

previously been submitted to any other institution or higher education. I further declare that

all sources cited or quoted are indicated and acknowledged by means of a comprehensive list

of references”.

Bronwyn Cloete

Copyright © Cape Peninsula University of Technology

2

DEDICATION

This study is dedicated with much love and appreciation to my friends and colleagues on the

staff of the Western Cape Provincial Veterinary Laboratory. It would have been impossible

to complete this project without you.

3

ACKNOWLEDGEMENTS

WCPVL Staff in Biochemistry Section for their eager support and unconditional assistance,

patience and being so accommodating

Nomphilo Zuma: Technologist Biochemistry

Kuthale Funeka: Technologist Biochemistry

Sarah Groenewald: Biochemistry Housekeeping staff:

WCPVL Staff in supportive role for their assistance and time

Ditsimai Banda: Quality Control and Assurance Manager

Renee Pietersen: Technologist PCR Lab

WCPVL Ground staff for their willingness and helpfulness collecting samples

Chrisjan Jantjies

Paul Slingers

WCPVL Laboratory Management for their support and making human and capital resources

available for this project

Dr. Tertius Gous: Head of Laboratory

Dr. Sophette Gers: Head of Biochemistry section

Dr. Jacob Stroebel

Last but certainly not least, my supervisor Meagan Arderne for her tireless support and

understanding throughout the process of compiling the project proposal and report

4

ABSTRACT

Author: Bronwyn Cloete

Degree: B Tech: Quality

Title: Determination of acceptable levels of haemolysis when accepting blood

samples for Trace mineral analysis.

University: Cape Peninsula University of Technology

Department: Industrial Engineering.

Internal Supervisor: M. Arderne

Date: 21 October 2010

Key Words: Haemolysis; Accurate Results; Quality

The outcome of any process can only be as good as the income provided for that process.

Even if all processes themselves, conducted in any quality environment, are sound, it seems

logical that regardless of the quality maintained during processing, if input quality is not

present namely the sample quality is not sound, then any quality efforts thereafter can be

considered futile.

This applies to all industries, including practices of the Western Cape Provincial Veterinary

laboratory (WCPVL). This paper aims to specifically research the effect of the quality of the

blood samples submitted to the biochemistry section of the WCPVL, on the service they

provide to their clients regarding the outcome in the form of results.

Despite haemolysis known to be a major adversary in clinical laboratories, at present, little

consideration is given to the quality of the results outcomes of the tests carried out in the

section, regardless of samples arriving in a condition suspected of being too haemolysed to

provide accurate results.

This research aims to determine the actual critical level of haemolysis which will actually

invalidate results of analysis carried out on the haemolysed blood. Based hereupon, certain

recommendations can then be made to WCPVL in an attempt to improve the quality of the

service to their clients and the greater agricultural community at large. The imperative aspect

which this paper sets out to demonstrate is to what degree “result quality” would be impacted

by “sample quality” and furthermore exactly how dependent laboratory quality maintained

5

depends on whether or not a sample would be deemed suitable enough to provide a quality

result.

6

TABLE OF CONTENTS

Page

DECLARATION 2

DEDICATION 3

ACKNOWLEDGEMENTS 4

ABSTRACT 5

TABLE OF CONTENTS 7

LIST OF TABLES 10

LIST OF FIGURES 11

GLOSSARY OF TERMS 13

CHAPTER 1: SCOPE OF THE RESEARCH

1.1 INTRODUCTION AND BACKGROUND 15

1.2 RESEARCH PROCESS 15

1.3 BACKGROUND TO RESEARCH PROBLEM 16

1.4 THE RESEARCH PROBLEM STATEMENT 17

1.5 THE RESEARCH QUESTION 18

1.5.1 Primary Research Question 18

1.5.2 Investigative Questions 18

1.6 KEY RESEARCH OBJECTIVES 18

1.7 CHAPTER OUTLINE 19

CHAPTER 2: A HOLISTIC PERSPECTIVE OF A RESEARCH 20

ENVIRONMENT

CHAPTER 3: LITERATURE REVIEW 31

CHAPTER 4: DETERMINATION OF THE EXTENT OF HAEMOLYSIS

ON BLOOD RESULTS IN ORDER TO ELLIMINATE INADEQUATE SAMPLES

4.1 THE SURVEY ENVIRONMENT 38

7

4.2 AIM OF THIS CHAPTER 39

4.3 RESEARCH DESIGN AND METHODOLOGY 39

4.4 DATA COLLECTION 42

4.4.1 Data Collection per protocol analysis methodology 44

4.4.2 Data Collection per laboratory experiments methodology 45

4.4.3 Data Collection per observation methodology 47

4.5 MEASURMENT SCALES 49

4.6 VALIDATION OF DATA 50

4.7 CONCLUSION 50

CHAPTER 5: DATA ANALYSIS AND INTEPRETATION OF RESULTS

5.1 INTRODUCTION 52

5.2 DATA ANALYSIS APPROACHES 52

5.2.1 Haemolysis in relation to time 52

5.2.2 Haemolysis effect in groups 53

5.2.3 Haemolysis effect between groups 53

5.2.4 Demonstration of normal ranges 54

5.2.5 Observation Analysis 54

5.2.6 Protocol Analysis 54

5.3 RAW DATA FINDINGS 55







5.4 INTERPRETATION OF STATISTICAL ANALYSIS 87







5.5 PROBLEMS ENCOUNTERED DURING RESEARCH 91

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5.6 KEY RESEARCH FINDINGS 91

CHAPTER 6: CONCLUSION AND RECOMMENDATIONS

6.1 BACKGROUND 93

6.2 THE RESEARCH PROBLEM RE-VISITED 93

6.3 RESEARCH QUESTIONS RE-VISITED 93

6.4 INVESTIGATIVE QUESTIONS RE-VISITED 93

6.5 KEY RESEARCH OBJECTIVES RE-VISITED 94

6.6 RECOMMENDATIONS 95

6.7 CONCLUSION 97

BIBLIOGRAPHY 98

ANNEXURE A: Survey questionnaire to Laboratory Staff 102

ANNEXURE B: Protocol Analysis Checklist 104

ANNEXURE C: Modified Flowchart of operations in Biochemistry 105

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LIST OF TABLES

Page

Table 2.1: Mean Monthly Specimens received in Biochemistry 21

Table 2.2: Mean Monthly Specimens processed in Biochemistry 21

Table 2.3: Inter-laboratory Comparisons 29

Table 4.1: Demonstration of Observation Method 48

Table 5.1: Comparative Table: Average Haemolysis Readings 55

Table 5.2: Regression Table: Time and Haemolysis 56

Table 5.3: Comparative Table: Average Copper Readings 57

Table 5.4: Regression Table: Haemolysis and Copper 57

Table 5.5: Comparative Table: Average Zinc Readings 58

Table 5.6: Regression Table: Haemolysis and Zinc 59

Table 5.7: Comparative Table: Average Calcium Readings 59

Table 5.8: Regression Table: Haemolysis and Calcium 60

Table 5.9: Comparative Table: Average Phosphorous Readings 61

Table 5.10: Regression Table: Haemolysis and Phosphorous 62

Table 5.11: Comparative Table: Average Magnesium Readings 63

Table 5.12: Regression Table: Haemolysis and Magnesium 64

Table 5.13: Comparative Table: Average Iron Readings 64

Table 5.14: Regression Table: Haemolysis and Iron 65

Table 5.15: Anova Table: Copper 66

Table 5.16: Anova Table: Zinc 68

Table 5.17: Anova Table: Calcium 70

Table 5.18: Anova Table: Phosphorous 72

Table 5.19: Anova Table: Magnesium 73

Table 5.20: Anova Table: Iron 75

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LIST OF FIGURES

Page

Figure 2.1: Pie Chart: Mean specimens processed in Biochemistry 22

Figure 2.2: Quality Management System Training Procedure 24

Figure 2.3 Quality Management System Procurement Procedure 25

Figure 2.4: Flowchart of operations in Biochemistry 27

Figure 3.1: Illustration of operation of a spectrophotometer 35

Figure4.1: Action Research 43

Figure 4.2: Demonstration of application of Protocol Analysis Methodology 45

Figure 5.1: Overview: Average Haemolysis Readings 55

Figure 5.2: Comparative Chart: Haemolysis Readings in groups 55

Figure 5.3: Regression Chart: Time and Haemolysis 56

Figure 5.4: Comparative Chart: Average Copper Readings 57

Figure 5.5: Regression Chart: Haemolysis and Copper 58

Figure 5.6: Comparative Chart: Average Zinc Readings 58

Figure 5.7: Regression Chart: Haemolysis and Zinc 59

Figure: 5.8: Comparative Chart: Average Calcium Readings 60

Figure 5.9: Regression Chart: Haemolysis and Calcium 61

Figure 5.10: Comparative Chart: Average Phosphorous Readings 62

Figure 5.11: Regression Chart: Haemolysis and Phosphorous 63

Figure 5.12: Comparative Chart: Average Magnesium Reading 63

Figure 5.13: Regression Chart: Haemolysis ad Magnesium 64

Figure 5.14: Comparative Chart: Average Iron Reading 65

Figure 5.15: Regression Chart: Haemolysis and Iron 66

Figure 5.16: Effect of Haemolysis on Copper: Fresh 78

Figure 5.17: Effect of Haemolysis on Copper: 3 Days 78



Figure 5.20: Effect of Haemolysis on Zinc: Fresh 79

Figure 5.21: Effect of Haemolysis on Zinc: 3 Days 79



Figure 5.24: Effect of Haemolysis on Calcium: Fresh 80

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Page

Figure 5.25: Effect of Haemolysis on Calcium: 3 Days 80



Figure 5.28: Effect of Haemolysis on Phosphorous: Fresh 81

Figure 5.29: Effect of Haemolysis on Phosphorous: 3 Days 81



Figure 5.32: Effect of Haemolysis on Magnesium: Fresh 82

Figure 5.33: Effect of Haemolysis on Magnesium: 3 Days 82



Figure 5.36: Effect of Haemolysis on Iron: Fresh 83

Figure 5.37: Effect of Haemolysis on Iron: 3 Days 83



Figure 5.40: Graphic Representation of Results of Observation Data collected: 84

Pie Charts: Shortfalls


Pie Charts: Improvement


Pie Charts: Haemolysis Grading

Figure 5.43: Spiderchart: Results of Protocol Analysis 87

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GLOSSARY OF TERMS

Case – A group of specimens submitted to the laboratory for testing purposes, from the same

sender, usually all specimens derived from the same owner or farm, all having the same

submission number, although each specimen has it’s own unique identification number.

Centrifuge – A device for separating components of different densities in a liquid, using

centrifugal force. The liquid is placed in special containers that are spun at high speed

around a central axis. (Concise Oxford Veterinary Dictionary, 1988, pg 153-154)

Control (Group) – The part of a study or experiment against which an experimental

procedure can be compared and it’s effects judged. (Concise Oxford Veterinary Dictionary,

1988, pg 193)

Cuvette – a small glass tube used in spectrophotometry (http://www.your

dictionary.com/cuvette n.d.)

Fibrinogen – Protein precursor from which the insoluable component of blood clots is

formed in the final stage of coagulation. (Concise Oxford Veterinary Dictionary, 1988, pg

314)

Haemolysis – Haemolysis (alternate spelling Haemolysis) is the destruction of red blood

cells (erythrocytes) (Concise Oxford Veterinary Dictionary, 1988, pg 366)

Haemoglobin – One of a group of proteins that occur widely in animals and function as

oxygen carriers in the blood. Alternative names Hgb; Hb (Concise Oxford Veterinary

Dictionary, 1988, pg 365)

In Vitro – Latin: describing biological phenomena that are made to occur outside the living

body (traditionally in a test tube). (Concise Oxford Veterinary Dictionary, 1988, pg 444)

LIMS – Acronym for Laboratory Information System. LIMS is a software system used in

laboratories. This system enables the electronic management of samples, laboratory users,

standards and other laboratory functions such as QA/QC Quality Assurance and Quality

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http://en.wikipedia.org/wiki/QA/QC

http://en.wikipedia.org/wiki/Laboratory

http://en.wikipedia.org/wiki/Management

http://en.wikipedia.org/wiki/Laboratory

http://en.wikipedia.org/wiki/System

http://en.wikipedia.org/wiki/Software

Control, as well as the integration of all laboratory softwares, and instruments. It facilitates

workflow automation, sample planning, and invoicing.

Normal Reference Values – Reference ranges for blood tests are a set of values used by a

health professional to interpret a set of medical test results from blood samples.

Plasma – The fluid component of blood in which blood cells and platelets are suspended.

Plasma is obtained as a clear yellow-to-white liquid when blood is collected into an

anticoagulant and cells are removed by centrifugation. (Concise Oxford Veterinary

Dictionary, 1988, pg 650)

Process Approach – “The process approach is a management strategy. When managers use a

process approach, it means that they manage the processes that make up their organization,

the interaction between these processes, and the inputs and outputs that tie these processes

together” ( http://www.praxiom.com/iso-definition.htm 5 September 2010)

Purposive sampling - This process is the selection of a particular sample on purpose.

Popular with qualitative research, the variables to which the sample is drawn up are

analytically and theoretically linked to the research questions.

(http://www.marketresearchterms.com/p.php 2010)

Serum – The fluid that separates from clotted blood or plasma that has been allowed to stand.

It differs from plasma in lacking coagulation factors. (Concise Oxford Veterinary Dictionary,

1988, pg 747)

Spectrophotometer – Device or instrument measuring light intensity or optical properties of

solutions or surfaces, specifically measuring the intensity as a function of colour or

wavelength.

14

http://www.marketresearchterms.com/p.php

http://www.praxiom.com/iso-definition.htm

http://en.wikipedia.org/wiki/Invoicing

http://en.wikipedia.org/w/index.php?title=Sample_planning&action=edit&redlink=1

http://en.wikipedia.org/wiki/Automation

http://en.wikipedia.org/wiki/Workflow

http://en.wikipedia.org/wiki/Scientific_instruments

CHAPTER 1: SCOPE OF THE RESEARCH

1.1 INTRODUCTION AND BACKGROUND

Quality is critical in diagnostic laboratories, not only due to common economical reasons

shared by most organisations, but the service rendered by a diagnostic laboratory has

transience implications as well. All quality practitioners are aware of the fact that a quality

output of any process can only be obtained from quality input. Therefore in order to obtain

what can be considered a quality standard of results, certain requirements are essential, such

as adequate equipment, QMS, quality reagents, etc. None of these above-mentioned will

make any difference however if the integrity of the actual sample submitted for testing

purposes is not suitable for the particular trace mineral test that is requested by the client.

Thus logically, the quality of all the blood samples submitted for trace mineral analysis in the

Biochemistry Section of the Western Cape Provincial Veterinary Laboratory must be

screened in order to determine if it meets minimum acceptance criteria needed to carry out

the type of analysis requested, without jeopardizing the quality of the results of the analysis.

1.2 RESEARCH PROCESS

Initiation of the Research process began with the identification of the aspect requiring

investigation in the form of research, namely the effect of haemolysis on sample quality in

the laboratory environment at WCPVL Biochemistry Section. The Research process

proceeded by the drafting a problem statement in relation to this topic and the preparation and

submission of a research proposal to address the topic.

The preparation of the research proposal was approached by conducting comprehensive

literature review surrounding the topic of haemolysis and it’s effect on sample and result

outcome. Appropriate quality project management tools were identified and selected for use,

to plan the actual research process. A flowchart is used to graphically illustrate and map the

approach to the research process. Research was conducted in order to ascertain the type of

research needed to address the problem statement, as well as identification of which research

paradigm research, research would fall in. An affinity diagram was used to highlight and

identify all aspects surrounding research, as well as a PERT chart outlining the framework of

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research in the context of a timeframe. All factors not forming part of the actual research but

playing a role in the research process was also given due consideration in the proposal stage.

Following the submission and acceptance of the research proposal, the actual research

commenced with the data collection as indicated in the research proposal, followed by the

analysis of the raw research data, the acquisition of findings based on raw data relating to all

branches of research from this, and thereafter interpretation of the findings. Appropriate

quality tools were also utilised during this stage. Following this, drafting of the Research

Report was able to proceed.

Drafting the Research report involved the re-consideration Research problem statement with

more in-depth consideration given to the research environment. The necessary additional

literature review identified, was conducted and incorporated into the report at this stage.

Finally, the data findings and interpretation thereof, was contextualised and documented for

the purpose of addressing the research problem, coming to a conclusions and making

recommendations on the research problem.

1.3 BACKGROUND TO RESEARCH PROBLEM

Quality control is not only for larger, sophisticated laboratories but necessary for any

laboratory providing test results. For diagnostic laboratories as is the case for WCPVL,

Accuracy, Precision and Reliability thus play critical roles. Accuracy can be defined as the

extent to which measurements agree with the true value of the quantity being measured.

Precision is the reproducibility of measurement and Reliability is the ability of a method to be

both accurate and precise.

As it is practicality impossible for each sample being tested in duplicate, to obtain the true

value, thus methods must be employed to ensure that results obtained by testing a sample

once is statistically near to true value (accuracy). It must also be ensured that a statistically

similar can be obtained on any given specimen if repeated (precision), lastly in addition to the

above-mentioned, methods must be employed to ensure that the above-mentioned are ensured

(reliability). These are the premises of any quality control or assurance program.

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The above-mentioned relates to the problem at hand in the sense that it applies to all samples

submitted for testing purposes to the Biochemistry Section of WCPVL. Although there is an

extensive range of trace minerals to select from, for the purpose of this project, the decision

was taken to investigate the effect of haemolysis on Calcium, Magnesium, Iron, Phosphate,

Copper and Zinc in Sheep blood.

Normal Ranges or Normal Values are the average normal values of a particular trace element

at any given time in the system of an animal, and these play a critical role during the duration

of our project. Normal values for chemistry tests are subject to innumerable variables, such

as geographic area, season, species, breed, sex, age, husbandry practices, level of feeding,

sample handling, interval between sampling and testing, test method used, person performing

the test and quality control practices. Published normal values can be used as guidelines, but

true normal values must be established for each laboratory by repeated testing of normal

animals. As this is impractical for most laboratories it must be decided which set of

published normal values to use. Most sets are fairly consistent with each other.

Calcium: Previous studies indicate that haemolysis results in a slight decrease in serum

calcium levels as the fluid from ruptured cells dilutes the serum. Normal levels in sheep,

12,16mg/dl+-0.28mg (Pratt, 1985: (15))

Magnesium: Previous studies indicate haemolysis elevates results as magnesium is released

from ruptured cells. Normal levels in sheep, 2.5mg/dl +-0.3mg (Pratt, 1985: (15))

Iron: Normal levels in sheep 29.73ug/100ml -39.76ug/100ml (Puls, 1989: (16))

Phosphate (Inorganic Phosphorous): Previous literature indicates that haemolysis should be

avoided as the organic phosphorus within RBC may be hydrolyzed to inorganic phosphorous

resulting in increased serum levels. Normal values in sheep: 5.0-7.3mg/dl (Pratt, 1985: (15))

Copper: Normal values in sheep 0.70-2.00 ppm wet weight (Puls, 1989: (16))

Zinc: Normal values in sheep 20-40 ppm wet weight (Puls, 1989: (16))

1.4 THE RESEARCH PROBLEM STATEMENT

Trace Mineral Results from analysis carried out in the Biochemistry laboratory of WCPVL

are possibly invalid or of poor quality due to levels of sample haemolysis going unscreened

in the section.

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1.5 THE RESEARCH QUESTION

1.5.1 Primary Question

What is the maximum haemolysis level acceptable, as measured in terms of optical density

using a spectrophotometer at 540nm wavelength, in order to accept samples for Trace

Mineral Analysis?

1.5.2 Investigative Questions

Is there a difference in the haemolysis level of samples read on Day 0, Day 3, Day 6, Day

9 and Frozen.

Are unacceptable samples being accepted as suitable in the current system?

What are the shortfalls of the current system?

What are the practical considerations or recommendations that can be made to manage

acceptance procedures of samples?

1.6 KEY RESEARCH OBJECTIVES

The primary objective of our research is:

To determine exact values of acceptable levels of haemolysis when accepting blood samples

for trace mineral analysis in terms of the concentrations of trace minerals present in serum

measured in nanometre (nm) units when read on a spectrophotometer.

Secondary objectives evolving from the primary objective:

To determine practical measurable methods available to identify unacceptable samples.

To determine the effects of haemolysis levels of blood samples at WCPVL in terms of the

quality system of the laboratory and implications thereof.

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To identify preventative measures required to be implemented within biochemistry laboratory

to prevent future acceptance of unsuitable samples.

1.7 CHAPTER OUTLINE

Thus in conclusion, in order to achieve the essential quality components of reliability,

precision and accuracy to WCPVL to uphold their reputation as a quality service provider and

thereby delivering superior customer satisfaction, research into the effect of haemolysis on

quality output is embarked upon.

Chapter 1 identifies the problem by exploring preliminary indicators and surrounding issues,

solicit ting the need for research into the identified problem to be carried out.

Chapter 2 considers the Biochemistry Section of WCPVL holistically, exploring all

contributing factors which play a potential role in the research in order to direct the research

process.

Chapter 3 comprises of a comprehensive literature review on the factor known as haemolysis

including the definition and effects of the factor. It explores the potential implications of

haemolysis as well as what steps can be taken in the form of quality tools to address the

factor.

Chapter 4 extrapolates on the type of research needed to address the haemolysis factor. It

outlines the type of data needed and methodologies needed, so that research can have a

valuable impact and achieve it’s objectives.

Chapter 5 involves data analysis based on the raw data collected. Data findings are attained

and interpreted with the aid of quality tools.

Chapter 6 is considered a conclusive summary, resulting from perusal of the research process,

in which final recommendations are made in order to resolve the initial problem statement.

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CHAPTER 2: A HOLISTIC PERSPECTIVE OF THE RESEARCH ENVIRONMENT

A research environment is defined as a set of tools, systems and processes interoperating, to

facilitate and enhance the research process, within and without institutional boundaries. It

can therefore be said that a research environment includes a combination of the “nature and

the culture” of a particular environment, as well as tangible aspects relating to the

environment, in which any research is conducted.

Viewing the research environment from a holistic perspective, provides the researcher a

opportunity to identify environmental factors impacting on the quality output of the

Biochemistry section at the Western Cape Provincial Veterinary Laboratory, enabling the

researcher to determine the root problem. The factors identified for consideration are those

suspected of having critical influence on the operational side of the research environment.

These independent, yet interrelated components and can be broadly defined as “invalidated

input factors”, providing “inconsequential processing” leading to “unsubstantiated output” of

the research environment. In an attempt to substantiate whether the output process is indeed

of consequence and adds value to the service delivered by the laboratory, the input, process

and output components of the research environment system must analysed. If there are

further subsystems are found to be present, the critical examination continues till within those

until investigations determine if there is a relation between them and their sub-factors which

ultimately influence the service offered by the laboratory.

In general terms “Input” refers to the all requirements necessary for the research

environment. Input can however been segmented into primary input and secondary input.

The primary input of research environment being the samples, leads to the identification of

the “samples”, as the initial focus point when considering the environment holistically.

Over the time period that the project was conducted it was found that an average of 317

sample specimens per month were received and handled by the biochemistry laboratory.

Specimens include blood/serum samples, liver samples (biopsies or larger pieces of tissue,

bone and other biological fluids such as eye fluid or urine)

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Upon further investigation, it was found that on average, 66,56% of the total number of

specimens were blood/serum specimens. Furthermore, Trace mineral analysis and/or Cu and

Zn testing was requested on 67,78% of the total amount of blood samples sent into the

laboratory. It is important to note that this percentage value does not take into consideration

of large amount of organ tissue samples, which also requested that trace mineral analysis be

conducted on them. As this project only involved analysis on blood samples, information

regarding the same testing procedures performed on different sample types is omitted and

excluded for statistical purposes.

It appears evident, considering the proportion of samples on which trace mineral analysis is

required, represents a substantially large amount of the total incoming samples and thus

accurate results needs to be assured. It poses a critical quality problem for any diagnostic

laboratory with reputable quality practices, should a problem be suspected but not addressed.

This could logically lead to potential, proportional and severe consequences. As illustrated

the amount of samples arriving at the section in an unverified condition requires that further

investigation essential, as such a large percentage cannot be ignored or considered isolated

cases.

It was deemed essential to thus determine the following:

Table 2.1: Mean Monthly Specimens received in Biochemistry

Mean Monthly totals in Biochemistry

Mean number of Cases received per month 70

Mean number of Specimens per Case per month 5

Mean number of Blood/Serum samples (specimens) per month 211

Mean Trace Mineral Requests per month (including Cu and Zn) 143

Total number of requests for only Cu and/or Zn 66

Total number of requests for Ca, Phos, Mg, and/or Fe 108

Table 2.2: Mean Monthly Specimens processed in Biochemistry

Mean Sample Processing per month

Amount PercentageType of sample Testing Required

Liver samples Se, Cu, Zn, Fe and Mg testing 62 19.56%

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Blood/Serum

samples

Trace mineral analysis (Ca, Phos, Mg, Fe

etc)211 66.56%

Bone/ Hair, feed or

Multimin samples

Se, Cu, Zn and Trace mineral analysis7 2.2%

Other samples Various 37 11.67%

Figure 2.1 A graphic representation of the tables above is illustrated in the Pie chart

Continuing the consideration of the primary input of the research environment, the type of

sample being suitable for testing purposes required, as well as the quantity of sample required

for testing were deemed the important issues arising. It was determined however that the

most critical factor with potential impact and influence on result output was sample

condition, and all other further investigations focus surrounded this.

Thus following was the determination of are additional input factors related to samples which

potentially influence validity of the process and thus the outcome of results. This has to be

addressed in an adequately scientific manner in order to maintain the validity of all results

stemming from this project. It is found that the best approach in order to contextualise and

gain perspective would be by the employment a “process-based approach" of looking at

system inputs. This approach is based on the knowledge that processes in system are all

interlinked (whether parallel or sequential), it is important that they are all successful and

lastly they cannot be adequately evaluated on a stand-alone basis without regard of their

relationship to each other, as the output of one process feeds the input of the next process in

22

some of other way. The use of a decision chart was thus used to extrapolate issues in able to

contextualise them holistically

The nature and culture of any research environment is directly related to personnel within the

research environment. Personnel forms the backbone of the research environment, must

therefore be also considered a critical component also due for scrutiny. Recognizing the

inconsistency of human behaviour, stresses the important role the institution where the

research is being conducted, has to play in providing an integrity-rich environment. Research

institutions need to provide personnel with education and skills training, guiding procedure

and policies, as well as support systems and tools required to conduct the research.

An essential question which arises is, “Does the research environment makes provision for,

and provide facilitates and practices which characterize integrity, such as peer reviews and

the promotion of self-evaluation efforts among staff in terms of the research being

conducted.” Prompted by this a further question which then presents itself is “Does the

research environment make provision for an ethical working environment framework with a

staff component able to provide a service of the highest standard when measured in terms of

empathy, assurance, reliability, responsiveness (indicators of service quality) in the

laboratory.”

In an attempt to gain perspective and understanding of these issues, focus is shifted to the

current Quality Management System operations employed in the laboratory. Thus, leading to

the examination of QMS documentation with reference to the Human Resource component of

the laboratory. A standard operating procedure was found to be in place with regard to

training. This procedure forms part of the validation component that is in operation in this

research environment.

23

Figure 2.2: Quality Management System Training Procedure

Yet another important secondary input factor identified, involves considering tangible aspects

of the research environment. This included infrastructure such as equipment (fridges to

preserve samples and reagents before actual testing can be carried out, centrifuge to spin

blood down immediately, pipettes and pipette tips, Testing equipment such the Atomic

Absorption Spectrophotometer and Vitros blood chemistry apparatus used to perform reliable

tests, balances and infrastructure of perform quality test. It is found that these are integral

components of the research environment and it needs to be ensured that they are available

and in adequate working order.

Similarly, another essential tangible input factor of the environment is quality reagents are

just as critical in the maintenance of a good research environment. Quality reagents should

thus always be available and of suitable grade for testing purpose. It is found that both

procurement as well as maintenance of equipment in addition to procurement and testing of

quality reagents used to perform testing procedures needs to be confirmed. Thus an

overlapping occurs yet again between laboratory function and QMS function. Quality

Reagents however remains a laboratory function, despite overlapping and interrelation with a

24

QMS. A QMS procedure is in place however to guarantee the procurement of necessary

system input in terms of quality reagents and employs the use of a critical supplier’s list.

Figure 2.3 Quality Management System Procurement Procedure

Quality Management System practices are explored within the context of Biochemistry

research environment and attention is focussed on such as the quality control measures and

quality assurance systems. The following was deemed necessary to be established regarding

the environment:

If all the necessary Standard Operating Procedures (SOP’s) for analysis/testing

procedures have been developed and are available.

If all necessary support documentation as well as records available for Standard

Operating Procedures were available

If adequate customer feedback procedures and mechanisms, relating to all inputs

leading to results in place?

Does environment allow for optimal staff functioning e.g. troubleshooting, can staff

decide sample unsuitable for quality result.

Frontline Activity

Quality Management System Procurement Procedure

Laboratory Function: Track and monitor stock Requisition necessary requirements: use Z15

form Requisition on basis of

Certificate of analysisPrice CompetitivenessSANNAS AccreditationTransport and delivery

Obtain 3 written quotes Confirm stock upon arrival at section Identify Suppliers Schedule Calibration and Service of

instruments

Administrative staff follow Procurement Procedure:

Procedure available at Admin Function

Administration Clerk issues order numbers

Stock Received by Administrative function

Evaluation of suppliers and service providers

Suppliers must be able to meet requirements

Service Providers must be accredited and be able to

demonstrate this

Suppliers on List of Critical Suppliers

Procedural requirements: Minimum of 3 Written Quotes Needed Records must be maintained Sole Agent must provide evidence Orders over R10 000 must be linked to

Tradeworld Orders over R50 000 must go out on

Tender

Hidden Factory Activity

25

The final consideration regarded as important when considering Input Factors in

understanding the research environment, is whether Management demonstrates support to the

Biochemistry section in order to achieve their objectives. It was found to be demonstrated by

the manner management ensures requirements for the section are provided to fulfil function

and objectives. A further indication of management support can be said to be demonstrated

by them showing full support to the initiation, preparation for, as well as the carrying out of

the research for this project in order to make quality improvement to the laboratory.

The secondary part of the exploration into the research environment involves consideration of

the actual Testing Processes involved. The testing processes refer to the main Critical

processes conducted for which the previous system inputs feed into, and which in turn deliver

the results or final product required by the customer.

A process, otherwise known as a “value-added activity” transforms an input to an output, and

can be defined as “A sequence of interdependent and linked procedures which, at every

stage, consume one or more resources (employee time, energy, machines, money) to convert

inputs (data, material, parts, etc.) into outputs. These outputs then serve as inputs for the next

stage until a known goal or end result is reached” (Businessdictionary.com, 2010: online)

Within the Process system of the research environment it is also found that there are at least

two subsystems, namely Primary processes which involve main processes providing the

results required by the customers and also secondary processes, which are those support

processes required with the preparation of samples and administrative processes associated to

the actual testing procedures.

The complete set of processes employed by the Biochemistry research environment can be

said to be called a system or process system. A system is defined as a set of detailed

methods, procedures, and routine established or formulated to carry out a specific act,

perform a duty or solve a problem. (Businessdictionary.com, 2010: online)

The systems employed for the purposes of this research are considered to be the following:

A) Sample registration and preparation

B) Sample processing

C) Validation and Issuing of Results

26

D) QMS related systems

The schematic Fig. 2.4 gives a general overview of the processes involved. Minor processes

are performed which make up the different processes indicated on the schematic, which in

turn make up the system required to achieve section objectives, and ultimately management

and customer satisfaction.

Fig 2.4: Flowchart of operations in Biochemistry

It can be said that the most important aspect to address when exploring this part of the

research environment is the validity of all the processes identified. It is thus important to

identify confirmed methods whereby validity can be established.

Validate is defined as “To declare or make legally valid; To establish the soundness of;

corroborate” (thefreedictionary.com, 2010: online). Validation provides assurance that

results are accurate, thus of good quality. Thus contextualising definition in terms of the

Quality Management SystemAll systems in Biochemistry

Reception: Samples arrive at LabSample information captured

Sample Analysis:Verifiable analytic method according to SOP.

Controlled conditionsUse of Controls and Standards

Critical Suppliers

Quality Management Documents and Records

Satisfied Service Customer

Technologist reviews resultIssues it for release from Biochem

Validation: Checks performed to see if SOP followedControls and Standards in spec

Veterinarian reviewsCompiles with results from other labs

Issues report

Samples delivered to Biochemistry Section

Sample Reception at BiochemistrySamples information recorded.

Biochemistry lab number assigned.Test Allocation.

Samples stored under ideal conditions until testing

Record keeping

YES

NO: Corrective action involves

redo

27

research conducted refers to establishing documented evidence which provides a high degree

of assurance that a specific process will consistently produce a result product meeting its pre-

determined specifications and quality attributes.

Thus validation can also be seen as a function to ensure accurate results as well as process

capability in a system. Juran defines process capability as "the measured, inherent

reproducibility of the product turned out by a process."(Juran, 1988: page 158) The procedure

of validation thus involves determining a set of methods or tools to be used to evaluate if the

expected results of a process conforms with a set of pre-determined acceptance criteria, and

using these to verify the outcome of a process. A validated process is generally considered to

be a stable process, and validation approaches for processes can range from an “auditing

type” approach to utilization of simple statistical testing or tools approach or even

comparative studies with a previously validated party.

It was determined that no formal auditing process was employed at the WCPVL, however an

informal audit of procedures at the WCPVL had been conducted in preparation for planned

accreditation. The results of this audit, reflected non-conformances indirectly related to the

processes involved in this study, such as stock room procedures, which would not have a

direct bearing or effect on the actual processes or resulting outcome thereof. However,

attempts to procure documented evidence of process validation or process soundness from the

general laboratory QMS were unsuccessful, despite having a seemingly good quality system

instituted and in operation.

The Biochemistry section, had however participated in inter-laboratory testing with three

other laboratories, two of which (Nutrilab: University of Pretoria and CSIR) are reputed

laboratories maintaining SANNAS compliant procedures. The results of the inter-laboratory

testing, was found to be very successful in terms of their comparisons.

28

Table 2.3: Inter-laboratory Comparisons

WCPVL internal positive control – Inter-laboratory Testing Data

Sample ID: LC2009

Cu ( mg/kg) Fe (mg/kg) Zn (mg/kg) Mn (mg/kg)

WCPVL values 266 543 202 9.64

Nutrilab (UP) 254 584 221 10.3

Elsenburg Soil lab 215 528.5 180 8.83

CSIR 293.71 550.15 220.37 9.58

Furthermore the following positive evidence was found regarding the quality of the

operations carried out in the biochemistry section namely:

A comprehensive Work Instructions in the form of SOP’s (Standard Operating

Procedures) were in place for all core processes and well as support processes in the

Biochemistry section.

Support Documentation in the form of worksheets, forms and records were in place

and effectively used.

The system made provision for effective identification and traceability of all samples

processed in the system, including the use of an IT based LIMs (Laboratory

Information Management System)

A Robust system of Controls and well as Reference Standards were included with

each testing process/procedure carried out.

The residual considerations of the Process System as a factor in understanding the

Biochemistry research environment from a holistic perspective involved exploring how

Corrective and Preventive issues with regard to processes would be addressed. It was found

to be noteworthy that although detailed procedures with regard to all other requirements were

in place and working effectively, there was not a specific procedure dealing with issue of

corrective and preventive action in the research environment in the system. Thus despite

having comprehensive QMS procedures effectively operating with regard to the operational

processes in the system, no definite way was identified to give guidance and direction in the

event of unforeseeable or unavoidable errors which could potentially occur in the system,

such as equipment breakdown despite maintenance or human error. On the whole the system

appears to be sound however.

29

Evolving from all the above-mentioned considerations, the holistic perspective and

understanding of the research environment aims or directs the researcher to the Output

Factors. Output Factors which form the third and last crucial component in the critical

examination of the research environment, are thus directly impacted upon, and directly

dependent on Input and Process Factors.

Explorations into this last factor reveals that adequate support processes for the

documentation and the timeous distribution of result were in place, in terms of policy,

procedure and infrastructure. A SOP for issuing results from the section adequately covers

all significant issues involved including the relevant authorities needed for issuing results, as

well as timelines in which results should be issued. In addition the utilisation of the LIMS

system facilitates results issuing and provides the necessary back-up required, should there be

a re-issue required.

Thus in conclusion, it can be seen that the most essential and most relevant aspect for

consideration relating to the Output factor is whether the output can be assured or whether it

cannot be assured, and ultimately it “almost demands”, unsubstantiated output is a critical

issue and needs to be addressed

All factors as examined in this holistic manner are found to form part of the research

environment known as Biochemistry. The factors not only lead to, but provides perspective

and insight as well, into the most pertinent and relevant statement about the research

environment, which is Trace Mineral Results from analysis carried out in the Biochemistry

laboratory of WCPVL are possibly invalid or of poor quality due to levels of sample

haemolysis going unscreened in the section.

30

CHAPTER 3: LITERATURE REVIEW

Saibaba, Vijaya Bhaskar, Srinivasa Rao, Ramana and Dakshinamurty (1998: online) has

stated that a number of interferences affect the analytical accuracy when conducting analysis

of body fluids in a clinical chemistry laboratory. For the purposes of diagnostic laboratories

it becomes critically important for the technologist or chemical analyst to be constantly aware

of this factor. As an integral part of the quality assurance program of the laboratory, it is

recommended that such factors be corrected. Furthermore they defined an interference

(1998: online), as being when a substance present in a sample has an effect which changes or

alters the correct value of the result of the analyte. (Saibaba et al., 1998: online)

Thomas (2010: online) contends that haemolysis is an important interference factor with

regard to determination of the normal trace mineral levels in serum. The influence of

haemolysis therefore cannot be ignored and must be considered when accepting samples and

the issuing of outgoing results. (Thomas, 2010: online)

Grafmeyer, Bondon, Manchon, and Levillain (1995: online) concluded that the most common

interference factor effecting validity of results was found in 34.5% of cases was haemolysis,

followed by total bilirubin interfering in 21.7% of cases while direct bilirubin and turbidity

seem to interfere less at around 17% (The influence of bilirubin, haemolysis and turbidity on

20 analytical tests performed on automatic analysers. results of an inter-laboratory study.

(Grafmeyer et al., 1995: online)

Hidiroglou (1983: online) states that average normal levels in sheep with regard to Calcium is

12.11 +-0.69mg/100ml (Hidiroglou, 1983: online) and similarly Pratt, Paul W (1985: (15)),

states that average normal levels in sheep for mineral calcium is 12,16mg/dl+-0.28mg . (1985:

(15)). Thus supporting the view that although more than one set of published normal values

are available for laboratories to use, and most sets are fairly consistent with each other,

however published normal values should only be used as a guideline considering normal

values are subject to variables such as species, breed, age etc. (Pratt, 1985: (15))

Haemolysis is defined as the breakdown of red blood cells and the release of haemoglobin

and intracellular contents into the plasma, and according to Guder. (1986: online)

31

Furthermore Guder states that at any preanalytical stage, the release of blood cell constituents

into plasma (serum) i.e. haemolysis can occur. Haemolysis occurring in vivo may be as a

result of disease (1986: online) whereas physical (mechanical destruction, freezing,

hyperosmotic shock), chemical (detergents), or metabolic causes (increased fragility due to

inherited diseases, depletion of glucose in specimen, metabolic inhibitors of enzymes) may

cause haemolysis occurring in vitro. Plasma concentrations exceeding 300mg/L, results in

the haemolysis of red blood cells being observable to the naked eye. (Gruder, 1986: online)

According to Arzoumanian (2004: online), “Hemolysis is the breakage of the red blood cell’s

(RBC’s) membrane, causing the release of the hemoglobin and other internal components

into the surrounding fluid. Hemolysis is visually detected by showing a pink to red tinge in

serum or plasma.1 Hemolysis is a common occurrence seen in serum samples and may

compromise the laboratory’s test parameters. (2004: online) Hemolysis can occur from two

sources:

• In-vivo hemolysis may be due to pathological conditions, such as autoimmune hemolytic

anemia or transfusion reaction.

•In-vitro hemolysis may be due to improper specimen collection, specimen processing, or

specimen transport.” (Arzoumanian, 2004: online)

From literature available, it was found that Thomas (2010: online) concurs with saying when

haemolysis is present in a sample, the possibility exists of the discharge of intracellular

constituents into the plasma/serum that may of occurred, consequently analytical results are

often false possible according to Thomas. (2010: online)

Thus it can be said that the presence of haemolysis in samples will have a direct effect and

impact of the quality standards in the laboratory where the tests are being conducted, by

being the reason for erroneous results being produced by analysis carried out on inadequate

samples. (Thomas, 2010: online)

Erroneous results have detrimental implications to clinical laboratories in terms of quality.

Lippi,, (2009: online) states that a major worldwide concern for all clinical laboratories is in

vitro haemolysis as through affecting test results it seriously impacts on patient care and the

laboratory’s reputation. (Lippi, 2009: online)

32

Furthermore it can be said that haemolysis also poses a problem in terms improvement of

quality relating to monetary benefit. In a study conducted by Ong, Chan, Lim (2009: online)

it was found that a cost saving occurred with a reduction in sample hemolysis from 19.8%

(before) to 4.9% (after) (P <.001). This further translated into a cost savings of SGD$834.40

(USD$556.30) per day at the emergency department and SGD$304,556 (USD$203,037) per

year. (Ong et al., 2009: online)

Spencer, and Rogers (1995: online) suggests that between quality improvement and

haemolysis a direct link exists. Although it is physically possible to produce results at a

remarkable speed and accurately within decimal point ad infinitum, it becomes redundant if

the specimen is unsuitable.

Trying to eliminate unsuitable specimens such as haemolysed specimens can thus be seen to

be part of a Quality Improvement Process (QIP) and Spencer and Rodgers (1995: online)

proposed a 4 step system in order to do so.

Step 1 involves defining the problem. Step 2 involves gleaning root causes of the problem.

Step 3 involves implementing countermeasures and Step 4 Checking results and maintaining

gains. Such a laboratory quality assurance plan includes several monitors which would assist

a Quality team track the effects of its recommendations. (Spencer et al., 1995: online)

Johnson and Besselsen states the outcome of studies performed on animals can be influenced

by a plethora of variable (2002: online) e.g. genetic variables, environmental variables,

infectious agents etc. Thus the critical need arises, in order to recognize the presence of

unwanted variables as well as minimize the impact of extraneous variables, to use control

animal groups, which are animals sharing the same or similar characteristics. (Johnson et al.,

2002: online)

Similarly a control group as defined by Oxford Concise Veterinary Dictionary is (Oxford

Concise Veterinary Dictionary, 1988, page 193) “The part of a study or experiment against

which an experimental procedure can be compared and it’s effects judged. In animal

experimentation, control animals are subjected to exactly the same conditions” In other

words to conclusively demonstrate a particular factor, the animals used as part of a control

group should be of the same breed, same age, gender and kept under similar conditions

(Oxford Concise Veterinary Dictionary, 1988, page 193)

33

Rising levels of free hemoglobin in serum indicates ongoing haemolysis and thus a typical

measure of hemolysis is the level of free hemoglobin in serum as stated by Na Na, Jin

Ouyang, Youri, Taes and Joris, Delanghe (2005: online) whereby they contend that

hemoglobin is a marker which can be used to indicate hemolysis is free hemoglobin in serum.

(Na Na et al., 2005: online)

Walters, Williams, Hazer, and Kameneva, (2007: online) argue that the baseline degree of

hemolysis present in blood is determined by the level of free hemoglobin in plasma/serum.

(Walters et al, 2007: online)

Studies conducted by Yucel, and Dalva (1992: online), on the effect of In Vitro Hemolysis on

25 Common Biochemical tests also contends that the concentration of free hemoglobin in

serum measured spectrophotemetrically indicates the level of haemolysis present in blood

(Yucel et al, 1992: online)

Henry, Cannon, and Winkelman, asserts that Spectrophotomic methods can be used to read

hemoglobin levels. (Henry et al., 1974 (6))

Spectrophotometers are standard research tools, used in chemistry laboratories, utilizing the

relationship absorption of light and colour as principle for the way it works. (Hoydt, n.d.:

online)

Raphael states that for analytical purposes a spectrophotometer is used to identify and

quantify a substance by determine the extent of the absorption of light energy.

Spectrophotometry investigates a particular substance’s unique pattern of absorption of light

energy, that is, which sections of the available range of wavelengths are most strongly

absorbed as a means of identification. Furthermore Raphael states that oxyhemoglobin

absorbs light strongly at 540 and 578nm. (Raphael, 1983: (17))

Rieser (1997: online) contends the main components of the modern spectrophotometer are:

A light bulb, to produce white light

Defraction grating, to “break-up” white light

Slit, to allow only narrow wavelength bands to enter

34

(Rotation of grating selects portion of spectrum illuminating sample)

Sample chamber, holds cuvette

Display mechanism

Figure 3.1: Illustration of the operation of a spectrophotometer. (Reiser, 1997: online)

This is an instrument for measuring the wavelengths in light. It is also used to compare wavelengths. Using a light source, shine the light into a sample. The sample then absorbs the light.

A detector (the machine that is used in this practice) measures how much light is absorbed. The detector converts that amount into a number and plotted on the corresponding chart for the

experiment

Fraser (n.d.: online) proposes that a research environment is defined as a set of tools, systems

and processes interoperating, to facilitate and enhance the research process, within and

without institutional boundaries. (Fraser, n.d.: online) Thus a research environment includes

a combination of the “nature and the culture” of a particular environment, as well as tangible

aspects relating to the environment, in which any research is conducted (Fraser, n.d.: online)

In order to achieve the most valuable impact out of research being conducted and obtain

optimal outcomes the research environment should be thoroughly examined in a holistic

perspective to ascertain all factors potentially playing a role. Ruiz-Marrero (2009: online)

contends that the holistic view, also known as holism, is an “interdisciplinary vision which

conceives every natural system as an integrated whole, which cannot be understood if broken

down into its constituent components” (Ruiz-Marrero, 2009: online)

Furthermore Ruiz-Marrero states that the sum of all parts is not the same as the whole in the

holistic view. (Ruiz-Marrero, 2009: online). Wikipedia defines holism (from ὅλος holos, a

Greek word meaning all, whole, entire, total) is the “idea that all the properties of a given

system (Wikipedia, 2010: online) It refers to physical, biological, chemical, social, economic,

mental, linguistic, etc. components and cannot be determined or explained by its component

parts alone. Instead, the system as a whole determines in an important way how the parts

behave.” (Wikipedia, 2010: online)

35

In additional to holistically considering the research environment Jiju Mike, Andreas, (1998:

169 - 176) contends the use of statistical quality control techniques is an essential part of the

search for effective quality control and can lead to quality improvement id applied correctly.

(Jiju et al, 1998: 169 - 176)

Arsham (1994: online) purports that statistical skills enable the user to intelligently collect,

analyze and interpret data relevant to their decision-making. (Arsham, 1994: Online)

Statistical concepts and statistical thinking allows:

solve problems in a diversity of contexts.

add substance to decisions.

reduce guesswork.

(Arsham, 1994: online)

Inferential statistics has two goals as stated by Allpsych Online (2004: online). The first goal

of inferential statistics, which is often referred to as an estimation, is to determine what might

be happening in a population based on a sample of the population. (Allpsych Online, 2004:

online). The second goal is known as a prediction and is stated to be to determine what might

happen in the future based on the sample population response. (Allpsych Online, 2004:

online). Thus illustrating that only a sample population is needed with the use of inferential

statistics, instead of the entire population, which is required for descriptive statistics.

(Allpsych Online, 2004: online).

According to The Quality Assurance Project,(QAP), (n.d.: online) quality improvement

involves applying methods most appropriate in order to close the gap between expected

levels of quality and current levels of quality. (The Quality Assurance Project, n.d.: online)

Thus to understand and address system deficiencies, quality management principles and tools

are applied to enhance strengths and improve processes. (The Quality Assurance Project, n.d.:

online)

Gate to Quality (n.d.: online) asserts that “Any tool or technique that can be used for

improving the process/product quality, help in analyzing the current situation, help in

gathering information or help in bringing small or big change (towards improvement) in the

organization can be called a Quality tool or technique.” (Gate to Quality, n.d.: online)

36

Furthermore, Statistical Process Control, (or the use of statistical tools/techniques,), control

charts, scatter diagrams, regression, check lists and check sheets, flowcharts, pie charts,

affinity diagraphs, spider charts and brainstorming are all examples of quality tools. (Gate to

Quality, n.d.: online)

Based on the quality assurance project’s (QAP) above-mentioned contention relating to the

use of quality tools, QAP also states that a flowchart is one of the important tools used to

improve quality (The Quality Assurance Project, n.d.: online), QAP defines a flowchart is a

graphic representation of how a process works, showing, at a minimum, the sequence of

steps. A flowchart helps to clarify how things are currently working and how they could be

improved.

A flowchart, also known as a process map, helps organisations improve the efficiency of their

systems asserts Snow (2005: (1)). According to Snow, process maps can be used in a number

of ways to analyse performance, including the evaluation of the current situation, the

identification of break-downs in the current system such as duplication of effort, gaps,

bottlenecks etc. Thus “a process map can be utilised to identify strengths and weaknesses of

a system, in carrying out it’s purpose” leading to the satisfaction of customers and

stakeholders and ultimately quality improvement. (Snow, 2005: (2))

Sayer, and Williams defines Spider charts, (also known as radar charts,) as being radial plots

of several performance measures in a single display. (Sayer, et al, 2007: (192)) Spider charts

are effective contends Sayer et al, at showing the performance characteristics in a single

graph. As they communicate information rapidly because of their graphical and visual

nature, spider charts are popular. (Sayer et al, 2007: (192)) Performance can be graphically

observed, as well as the observation the relative value of different inputs is enabled by spider

charts, thus logical comparisons can be made between strategies or approaches and instantly

see strengths and weaknesses between alternatives. (Sayer et al, 2007: (192))

37

CHAPTER 4: DETERMINATION OF THE EXTENT OF HAEMOLYSIS ON

BLOOD RESULTS IN ORDER TO ELLIMINATE INADEQUATE SAMPLES

4.1. THE SURVEY ENVIRONMENT

In the context of this project the survey environment refers where sampling data was obtained

and includes the animal testing groups identified to form the foundation on which the

research is carried out in the project.

The predetermined animal control group of one year old ewes husbanded at the WCPVL

were selected for the purpose of obtaining samples. The ewes make up a sub-population of

the entire population of control animals kept for the purpose of laboratory testing. This

specific sub-population was selected for research to be conducted on them due to them

sharing similar characteristics in terms of gender, age and general health condition. Due to

the fact that they are not used for breeding purposes as yet, it was determined that their trace

mineral and Cu and Zn levels would stay relatively stable as they wouldn’t be undergoing

any extraordinary stresses due to a gestation. Furthermore the treatment of these ewes in all

other aspects of husbandry would be consistent with each other in terms of their housing,

diets, anthelminthic treatment, other supplementary treatments and general handling.

Normal Reference values are noted to be a guideline, and thus differ from animal group to

animal group. The laboratory uses a general set of accepted normal reference values.

Interestingly though it was found that the normal values of the sheep husbanded at WCPVL,

was out of range of the normal values used for sheep in the wider area of the Western Cape.

More extensive testing would be required to determine the precise normal range; however the

deviation from the set of normal values does not deviate to the extent that it can be

considered unsuitable.

Reasons for the deviation are believed to be related to feed conditions (diet), climate in the

Stellenbosch region, nutritional supplements as well as anthelminthic medications and others

supplements given to all the sheep husbanded at WCPVL. In addition, it is a known fact that

the copper soil levels in the Stellenbosch region are typically low. For this reason a

procedure is in place at WCPVL, whereby laboratory sheep husbanded at the laboratory, are

dosed with a multimin supplement every 6-8 weeks. Thus ultimately a decision was taken to

38

accept to the current accepted normal range values used by the laboratory for the purposes of

this project as it did not have a significant influence on the actual outcome of the results

obtained for the research.

4.2. AIM OF THIS CHAPTER

Scientific research being conducted thus was necessary to select research methodology which

will best suit and facilitate this type of research required. In the initial stages of this project it

was established that the research conducted in both research paradigms would be employed,

as well as which methods would be employed.

To obtain the most comprehensive understanding however, regarding why the particular

research design and methodology selected, is deemed to be most effective for the purposes of

this project, can be best demonstrated by tracing backwards. The process of tracing back

provides a rationalization instrument for the researcher to be able to illustrate why the

research methodology selected is the most appropriate to address the research problem. The

research question and problem statement are first examined in order to ascertain what the

exact research requirements are. Once requirements to address the research question and

problem statement are adequately established, the investigation sets out to provide guidance

as to which of the available data collection methodologies are most appropriate to gather

information as project data. These data collection methodologies are seen to be analytical

approaches to obtain evidence. They are in turn, classified according to paradigm in which

they are used and associated to the type of research (in the form of a methodology) they are

utilised in. The outcome of this is identification of the Research Methodologies they can be

classified as in as well as in which paradigm they fall in, either Quantitative, or Qualitative or

both as in the case of this project.

4.3. RESEARCH DESIGN AND METHODOLOGY

The process of tracing backward is initiated by initially identifying the primary objective of

the research question based on the research problem statement. This is done in order to be

able to resolve the research question in the most effective manner. The methodology (group

of methods) most suited to collect data in order to achieve the research objective is hereby

identified. Support data collection methodologies are explored and the determination of the

39

type of research methodology these data collection methodologies would be classified as.

This leads to the further establishment whether the research methodology in turn would be

defined as, being either Qualitative or Quantitative or both depending the context in which it

is used for research.

It appeared evident from the objective that laboratory experiments or testing would form a

crucial component of the research and thus it was established on the outset that any research

methodology selected for use had to be selected or designed around the laboratory testing.

Laboratory testing as a data collection methodology is considered to fall within the

quantitative paradigm and used to gauge the extent of the manipulation of one variable affects

another variable. This is also known as casual research whereby controlled laboratory

analysis provide quantitative data used to investigate the relationship the independent

variable (namely haemolysis) on dependent variables (namely time and trace mineral levels).

This primary quantitative data is directly analysed using various statistical methods.

The resultant data collected using the laboratory testing method would have a futile impact on

research however, if laboratory testing was utilised as a stand-alone method. To add value to

the data collected via laboratory testing it is essential to incorporate the use of support

methodologies. The methodologies identified to provide significance to the data collected via

laboratory testing are Protocol analysis and Observation

Data collected utilising the Protocol Analysis Method provides the framework of the research

design and enables the data to be coherent and significant. Protocol analysis involves

identifying the mental processes in problem solving, with the objective of ascertaining

behaviour and thinking processes involved in a particular situation. (Watkins, 2008: (23)).

This definition is most relevant when protocol analysis is conducted involving human

participants in the research environment. This research project involves sample participants

and thus although the same principles apply as when obtaining data from human participants,

the means of obtaining the data differs. Focus of the data (or types of information) obtained

however, needs to remain consistent in terms of relevance to the study. Data collected via

this methodology is purely qualitative and in this phase of data collection, the relevant factors

influencing the research conducted in the research environment were extrapolated and then

expounded. The importance and weight of factors was determined and this data is used.

40

Furthermore supportive data was collected through Observation enables a quality impact in

the industry and research environment that it was conducted, through the research. The

particular type of observation method employed namely participant observation provides a

manner in which the researcher is fully involved (Watkins, 2008: (23)) with analysis

revolving around the haemolysis effect, in order to understand the outcome and ultimately

this is key to the interpretation of data as well in ultimately addressing the research problem.

Observation involves the researcher associating the dependent variables to the independent

variation and then collecting data by evaluating and making observations on the different

relationships or interactions detected. It is notable that this methodology involves gathering

data in both qualitative and quantitative form, involving witnessing and measurements.

The extension evolving from identifying these data collection methodologies is the

classification of the data collection methodologies according to the type of research that they

fall under as well as the appropriate research paradigms that they are components of. When

using data collection methodologies in the manner as formerly described are considered to be

the Action Research Method. Action Research is described by Dick as a methodology which

has dual aims of action (to bring about change in some organisation) and research (to increase

understanding on the part of the researcher) (Dick, 1993: online) or in other words “a way of

doing research and working to solve a problem at the same time” This method was

developed to allow researcher and participants of the research, to work together and analyse

systems with the view to changing them, in other words, to achieve specific goals. (Dick,

1993: online)

Considering the above-mentioned it is thus considered that specific to this project, the Action

research methodology used is considered to fall neither precisely within the qualitative nor

quantitative paradigm, arguing that it is more a tool for change than true research. Thus

although Action Research method being used cannot be considered pure to only one

paradigm, it demonstrates however the use techniques involved in both research paradigms.

As an illustration of how this is done, it can be said Quantitative research tending to be

destructive in nature set out to test theory proposed by this project while Qualitative research

tending to be inductive in nature, is used simultaneously to generate theory.

Furthermore as it was found the pinnacle point the research question revolved around the

actual effect of independent variable, or factor of haemolysis, as well the extent of the

41

relationship of various potential dependent variables (results) on the independent variable, the

data collection methods of Protocol analysis and Observation (to an extent) are classified or

are found to be encompassed by Field Experiments Research type and Experimental Studies

Research Type classification. Both of these research methodologies involve the manipulation

of variables in a controlled laboratory environment in order to obtain a result and are

qualitative in nature.

Thus in conclusion, by tracing back it is demonstrated that this research both Quantitative, as

well as the Qualitative. Quantitative research is objective, deductive, generalised able and

data is found mainly in the form of numbers, while Qualitative research is subjective,

inductive, not generalised able and data is commonly in the form of words. Both are

systematic in nature is a defining principle of any research however, and both effectively

used to achieve research objectives.

4.4 DATA COLLECTION

In addition to understanding why each data collection methodology identified was selected as

most suitable for this research, it is also deemed important to demonstrate the application of

the methodologies in the context of the research.

In order for this to be done, an understanding of the following concepts is essential:

Purposive sampling is the technique used to identify the samples which would be required.

In this instance of purposive sampling, samples were identified for selection not by reference

to sample type or size, rather however in reference to the Animal Control Group identified as

sample targets due to their similar characteristics. A control group, or Animal control group

with reference to this research is defined as “The part of a study or experiment against which

an experimental procedure can be compared and it’s effects judged.” (Concise Oxford

Veterinary Dictionary, 1988, pg 193)

A sample is defined as a limited quantity of something which is intended to be similar to and

represent the larger amount of that thing(s) or population. The things could be countable

objects such as people, animals or individual items available as units for sale. (Wikipedia,

2010: online)

42

Unit of Analysis has been defined by the Research Methods Knowledge Base as “The major

entity being analysed in a particular study” (Research Methods Knowledge Base, 2006:

online). Thus, the unit of analysis of a particular study is said to be the “what” or the

“whom” being studied and from which data is obtained. Thus it is considered that the

selection of what the unit of analysis will be for a particular study is determined by the

interest of that particular study in exploring or explaining a certain phenomenon, namely the

subject of that study.

A variable is defined as a symbolic name associated with a value and whose value may be

changed. (Wikipedia, 2010: Online) The terms “dependent variable” and “independent

variable” are used to distinguish between two types of quantities being considered, separating

them into those available at the start of a process and those being created by it, where the

latter (dependent variables) are dependent on the former (independent variables) (Wikipedia,

2010: Online). A positivistic study calls for an independent and dependent variable to be

stated. The independent is the variable that can be manipulated to predict the values of the

dependent variable, thus the dependent variable is the one whose values can be predicted by

the independent variable. (Watkins, 2008: (54))

The application of the Data collection methods in context of the research process is

demonstrated with the aid of the following schematic:

Figure 4.1: Action Research (Ross et al., 1999: online)

This illustration of the research process provides a means to view the research process from a

perspective whereby it can be seen where and how data collection is inaugurated.

1. Identify the problem: Project proposal written

43

http://en.wikipedia.org/wiki/Variable


2. Discussion of the problem: Protocol Analysis Data Collection Method

3. Review Literature: Holistic Perspective of the Research Environment, Project

Proposal Data Collection level, however could also be required after Protocol

Analysis complete

4. Re-define the problem: Investigative questions addressed

5. Select Method: Laboratory Testing and Observation Data Collection Method

6. Implement change: Involving conducting research analysis and conclusions to guide

the direction of the change

7. Cyclical: Return to step one if necessary

This entire process is cyclical and can be seen as a modification of Deming’s PDCA Cycle

4.4.1 Data collected per Protocol Analysis Methodology:

As the unit of analysis in this project are the serum samples thus, protocol analysis

methodology takes the form of collecting data surrounding them, thus it is understood the use

of this method enables the research to obtain data involving the analysis of the procedures

entailing the samples.

These results of the analysis of these procedures provide an insight into the research

environment as well as the guiding direction as to how to aspects of the research are to be

approached

The protocols employed by the section are investigated with reference to their relation to the

research environment. Thus ultimately protocol analysis involved collecting data in the form

of qualitative or phenomenological informative on situational issues involving the research

environment and how if at all, it impacts on samples e.g. sample quality, treatment of samples

and the consequences that can thus be anticipated from samples.

The situational issues involving the research environment were identified as the Process

factors in the research environment (as discussed in Chapter 2) and were listed as the systems

employed for the purposes of this research being the following:

A) Sample registration and preparation

B) Sample processing

C) Validation and Issuing of Results

D) QMS related systems

44

Protocol Analysis done on the process factors is demonstrated in the schematic Fig 4.2, and

broken down into categories associated to, and addressing each of the 4 above-mentioned

systems

Figure 4.2: Demonstration of application of Protocol Analysis Methodology

Through the process of validation of the systems, relevant information on the systems being

employed was selected, gathered, and then evaluated as part of addressing research objectives

4.4.2 Data collected per Laboratory Experiments Methodology:

The bulk of the data collected for this research was collected via Laboratory experiment data

collection methodology. This involves the collection of pure quantitative data, in the form of

results from actual laboratory testing procedures (analysis) carried out on the unit of analysis,

for the purpose of testing theory proposed by this project. The quantitative nature of data

enabled the theory proposed by the project to be tested by statistical means which is

understood to be one the most credible methods by which to test theory for quality purposes

45

The procedure for obtaining the data involved actual laboratory testing procedures including:

Optical Wavelength Spectrophotometer Readings

Vitros Blood Chemistry Analysis readings

Atomic Absorption Spectrophotometer readings

Support testing involves use of a blood stirrer

The type of data collected via this methodology, provides the core from which primary

research question, as well as the first two investigative questions, are directly addressed by

the research conducted. Thereafter the two remaining investigative questions are addressed

by evaluating the same data and considering it against data collected using other data

collection methodologies.

The application of this data involves adopting systematic approach in addressing the research

questions. Thus the primary research question is the first to be addressed. A range of

haemolysis level values is established for each pre-determined sample group by analysis of

each sample in the group. Thereafter a range of trace mineral values is established, also by

the analysis each sample in the different sample groups. Finally it must be established

whether or whether not, correlation exists between haemolysis level and trace mineral range.

Using this data gathered by this collection method, in addition to data collected via other

methodologies, a minimum acceptance level can be established.

The approach to addressing the first investigative question, involves establishing via

statistical means establishing whether or not a quantitative difference exists in results

obtained between the different groups based on the dependent variable. The approach to

addressing the second investigative question involves the establishment of an Upper Control

Limit based on further statistical analysis of the data collected.

The final two research questions are addressed with primary consideration to the information

supplied through data collected using the two other data collection methods identified, based

however on the results provided by data collected by Laboratory Experiments Data collection

method.

46

4.4.3 Data collection per observation method:

Watkins states that “Observation serves as a data collection methodology for research

methods falling within the context of either the positivistic or phenomenological research

paradigm”. (Watkins, 2008 (23)) A challenge posed for this research was presented in an

attempt to contextualize it for the purpose of the project. Ultimately it was understood that

data collection per observation method, provided complementary data which enabled

practical value to be inserted into the research. Thus the observation data methodology

primarily said to fit more suitability in the qualitative research paradigm for this project,

however it was also found to have a quantitative aspect to it. The data collected via this

methodology provides the cohesion required to be able to perceive the research as a network

from which the resolution to the research problem statement could be extracted.

Data collection per this method, takes place in two phases, namely initial data was collected

to ascertain irresolution associated around the factor of haemolysis in the unit of analysis i.e.

Observation method used to collect data determining relationship to the impact of time on

the samples. This data collected via observation method forms the basis on which further

data later collected via laboratory experiments methods was analysed (i.e. analysis to Trace

mineral levels in relation to the impact of haemolysis in the unit of analysis.) The initial data

collected by this observation provided a reflection of dependencies and interactions between

factors playing a role in the research environment.

The first phase of observation data collection is done by manipulating variables and recording

the outcome of this. In the first instance the independent (phenomenological) variable is

identified as “duration in days” or time and the dependent (positivistic) variable being level

of haemolysis. The result or data obtained in the form of observation was made of the

appearance of the sample. As the data was collected for a scientific project, the manner in

which this data was validated was by means of laboratory testing which provided a

qualitative measurement to be associated to the data.

The two sets of data collected via laboratory experiments methodology and observation

methodology was then evaluated to determine whether a correlation existed, and the result of

this is used to address the last investigative question and to a lesser extent the second

investigative question. It also would form the basis for the development of a colour index for

the laboratory to be used as reference and a quality tool.

47

The procedural steps involved, during which data was collected via this methodology are as

follows:

Table 4.1: Demonstration of Observation Methodology

STEPS DAY DETAIL OF PROCEDURE

1 0 50 Fresh blood samples drawn on, and allowed to clot for approximately 2 hours

2 0 10 Fresh blood samples shaken on a blood stirrer for 10 minutes and then

centrifuges a 3000rpm for 10 minutes. Serum drawn off and stored in a 4oC

refrigerator until testing began

3 0 10 Blood samples were placed in -20oC freezer, allowed to freeze, removed and

thawed, then shaken on a blood stirrer for 10 minutes and then centrifuges a

3000rpm for 10 minutes. Serum drawn off and stored in a 4 degree refrigerator

until testing began

4 3 10 Blood samples allowed to stand and haemolyse at 25oC incubator, to maintain

consistence and imitate room temperature for 3 days. Samples removed from

incubator, shaken on a blood stirrer for 10 minutes before being centrifuged at

3000rpm for 10 minutes. Serum drawn off and stored in a 4oC refrigerator until

testing began





testing began





testing began.

Repeated 10 times

Conditions for the above-strictly monitored and controlled within the laboratory

The second phase of observation data collection involved data gathered through the means of

a survey conducted on a panel of staff. The method was used in this phase in order to obtain

information which will drive any change or quality improvement in the laboratory. The

48

survey involved obtaining their opinions with regard to input and process factors in the

system. The data collected from this is evaluated and applied when addressing the 3 rd and 4th

investigative question.

4.5 MEASUREMENT SCALES

In order to analysis the data, it is important to determine the appropriate measurement scales

according to which the data will be evaluated. The qualities or characteristics of the collected

data i.e. magnitude, equal intervals, absolute zero, determine what scale of measurement is

being used and therefore point towards statistical procedure which would best to use to

analyse the data.

Supported by knowledge that the objective of this research would be to result in a quality

improvement to the research environment, it is understood that inferential statistics can be

employed as a tool in this regard. The rationale behind using inferential statistics lies in the

actuality that the “conclusions from inferential statistics extend beyond the immediate data”

(Watkins, 2008 (164)), as well as inferential statistics can be used to “make judgement calls

of the probability that an observed difference between groups is a dependable observation, or

an observation that may have happened by change during study”.(Watkins, 2008 (164))

Thus to execute the statistical analysis required, it was determined the following scales of

measurement would be used:

Ratio Scale: This scale contains all three data qualities, and therefore it was

determined to use this scale for the analysis of data collected via Laboratory

Experiments Methodology. This scale was also used on the data collected by the first

phase of the Observation Method.

Ordinal Scale: This level, or scale, has magnitude only. Data at this level can be

looked at as any set of data that can be placed in order from greatest to lowest, but

where there is no absolute zero and no equal intervals. Thus this scale was used in the

analysis of data collected by the second phase of Observation Method. A popular

application of this type of scale is known as the Lickert’s scale.

Nominal Scale: The lowest level or scale of measurement, representing only names

(list of words) and therefore has none of the three qualities. This measurement scale

49

is primarily employed during the analysis of data collected via Protocol data

collection method.

It is important that the level of measurements for the variables involved during research be

considered when deciding upon which statistical analysis procedure to use during data

analysis. In addition, the researcher should also base the choice of the statistical analysis

procedure selected on the assumptions of that procedure. Lastly the researcher needs to

consider the interpretability and substantive meaning of the statistics being computed, as it

can be said that no substitute exists for informed sound judgement when choosing a statistical

test for analysing data. (Virginia Agricultural and Mechanical College, n.d.: Online)

4.6 VALIDATION OF DATA

The process of validation provides credibility to the research conducted therefore reputable

validation techniques have to be identified and applied in order to the research the be

acceptable as a means to resolving the research problem

Thus in association to data, it is essential to establish that:

The actual data collected during the research process is validated

The application of the methods used in order to collect the data is validated

The application of the methods used in order to analyse the data collected is validated

Validation took place by investigating and extracted information in the operational system,

which is seen as a means to verify or confirm technical correctness of procedures used to

obtain data, surrounding the samples. In this was the data was validated

4.7 CONCLUSION

Thus in summary, this chapter emphasizes the importance of most applicable research

methods (including data collection methods), being selected for application for this project.

Extrapolation of research requirements leads to the selection of Action Research as the

research methodology of choice falling within both the quantitative and qualitative research

paradigm. The most suitable data collection methodologies were found to be Laboratory

Experiments, which would form the base or foundation for the research being carried out,

50

Protocol Analysis, which would provide the research with a guiding direction, and lastly

Observation analysis which could be said to be the component providing the research with

the cohesion necessary to insert practical value in to all other components of the research,

with the objective of ultimately addressing the research question.

The following chapter demonstrates how the application of the above-mentioned data

methodologies, is successfully utilised to gather data, and analyse the data, in order to

develop logical and practical conclusions for ultimate and definitive quality improvement

purposes

51

CHAPTER 5: DATA ANALYSIS AND INTEPRETATION OF RESULTS

5.1 INTRODUCTION

This chapter outlines analysis and interpretation of results, providing a description of which

methods were selected to be able to determine the following:

The 10 Animals were identified by numbers and the animal ID’s were:

103 114 119 123 129

104 117 120 124 141

There were 8 duplications (replications) of the same testing procedure carried out on each of

the laboratory animals in the sample group. Four batches of blood (tubes of blood) were

obtained from each animal at one given sampling, and all treated in the same way except for

the independent variable i.e. time. Thus the only difference between four samples from the

same sample animal were centrifuged at different times

The battery of laboratory tests conducted on each serum sample consisted of:

Spectrophotometric Reading to determine Haemolysis

Copper (Cu) Analysis

Zinc (Zn) Analysis

Calcium (Ca) Analysis

Phosphorous (Phos) Analysis

Magnesium (Mg) Analysis

Iron (Fe) Analysis

The mean result values from each animal obtained from the results of the above-mentioned

analysis

5.2 DATA ANALYSIS APPROACHES

5.2.1 Haemolysis in relation to time

52

It was critical to establish is a relationship between haemolysis and time existed, and if so,

what the nature of the relationship was deemed to be, as it known that the most common

cause of haemolysis. The mean value of haemolysis per animals and for all replications was

calculated per group. Control charts are best used to demonstrate the range of haemolysis

and regression and correlation is the tool selected to establish if, and what type of relationship

exists.

5.2.2 Haemolysis effect in groups

It was necessary to establish if the haemolysis level in samples in a particular time group had

an effect on the trace element levels of that particular time group. For each of the 4 groups,

data was collected per animal, per pre-determined trace element (Cu, Zn, Ca, Phos, Mg and

Fe). It was deemed meaningful observe the difference in the trace mineral level value in

question, knowing that all other aspects of the sample, conditions were maintained the same.

To demonstrate what the results found, control charts were developed for each element using

the means obtained throughout the duration of the project. Since these chart are not

demonstrating a process with the aim of detecting variation in the process but rather intended

to reflect a range of biological values being researched, it was decided that Normal Control

Values being used as UCL and LCL would add more value. The control charts demonstrated

the effect of haemolysis (and time) on each of the trace elements.

Furthermore it was established if it could be said that a relationship exists between that

independent variable (haemolysis/time) and the independent variable (trace element

involved). The statistical tool of regression and correlation was done to demonstrate this

5.2.3 Haemolysis effect between groups

Research then set out to determine if it could be statistically proven that a difference exists

between the Time (Days) Groups. This was done by means of hypothesis testing between the

groups of a particular trace element, for every element. With consideration for all the

variables, as well as the replication involved during the process of data collection, it was

determined that the use of 2 Factor Anova with replication would be the most suitable

statistical tool to employ. It was decided to state the Null hypothesis as No difference exists

between the Time Groups, and with hypothesis testing determine whether this was actually

the case

53

5.2.4 Demonstration of normal ranges

For completeness of research project, it is found necessary to illustrate the effect of

haemolysis on the samples by comparing it’s effect on the groups. This is determined to be

best illustrated by means of charts reflecting the mean results obtained in each group per trace

element analysed.

5.2.5 Observation Analysis

First phase: This involved gathering phenomenological data and positivistic data on the

initial raw samples and probing the relationship of the two factors. The colour intensity of

each sample was observed and rated according on an interval scale of plusses, ranging from

zero or none to 5 plusses (+ + + + +). The simple light intensity test using a

spectrophotometer set at wavelength 540nm, was then conducted on the samples and the

results of these compared in order to ascertain whether an association could be drawn.

Second phase: In order to attach additional quality value and further substantiate the

laboratory results obtained, data was collected from sources deemed competent in the field.

Their opinions on factors influencing the research environment was obtained and statistically

evaluated to attach importance to the factors. Statistical evaluation took the form of

developing pie charts to illustrate relative weights. Furthermore, opinions were also gathered

on potential course of modifications that can be made to the system in order to implement a

quality improvement. The results of this analysis, was then evaluated against the results

obtained from the laboratory experiments phase of the research project. See Annexure A:

Survey to Laboratory Staff

5.2.6 Protocol Analysis

Protocol analysis data collected analysed by means verification and validation of the data

within the operational system of the Biochemistry Research Environment.

Data was collected from the pertinent sources identified in the laboratory, as a means to test

the system in addition to gathering data on expected sample behaviour. This use of checklist

of key questions was employed to do this. See Annexure B: Protocol Analysis Checklist.

Findings were evaluated by means of charting in order to determine the effectiveness of the

systems in place and furthermore determine their strengths and weaknesses

54

5.3 RAW DATA FINDINGS


Figure 5.1: Overview Chart: Average Haemolysis Readings

This chart reflects the average haemolysis readings of samples obtained from each individual animal in the

animal control group. The different lines reflect the different manipulation investigated,, i.e. Fresh, Day 3, Day

6 and Day 9

Figure 5.2: Comparative Chart: Haemolysis Readings in groups

This chart reflects the upward trend found when comparing the combined average haemolysis readings of the

different manipulations investigated, i.e. Fresh, Day 3, Day 6 and Day 9

55

Table 5.1: Comparative Table: Average Haemolysis Readings

Day Average Haemolysis Reading

0 0.377038

3 0.561425

6 0.795138

9 1.116775

Table 5.2: Regression Table: Time and Haemolysis

Relationship between Haemolysis as a dependent variable of the independent variable Time

Regression Statistics

Multiple R 0.992143

R Square 0.984347

Adjusted R Square 0.976521

Standard Error 0.048907

Observations 4

ANOVA

df SS MS FSignificance

F

Regression 1 0.300842 0.300842 125.7731 0.007857

Residual 2 0.004784 0.002392

Total 3 0.305626

Coefficients Standard Error t Stat P-value Lower 95%Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 0.344655 0.040919 8.422885 0.013804 0.168595 0.520715 0.168595 0.520715

X Variable 1 0.081764 0.007291 11.21486 0.007857 0.050395 0.113133 0.050395 0.113133

Figure 5.3: Regression Chart: Time and Haemolysis

The chart below graphically demonstrate the regressive relationship between the independent variable time and

the dependence of the variable haemolysis on it

56


5.3.2.1 Effect of Haemolysis on Copper (Cu) Readings (Group Comparison)

Table 5.3: Comparative Table: Average Copper Readings

Figure 5.4: Comparative Chart: Average Copper Readings

This chart reflects the average copper readings obtained from samples in the different sample groups, Fresh,

Day 3, Day 6 and Day 9

Table 5.4: Regression Table: Haemolysis and Copper

Relationship between Copper as a dependent variable of the independent variable Haemolysis


Multiple R 0.985948

R Square 0.972093



Observations 4

ANOVA


F

Regression 1 0.013452 0.013452 69.66601 0.014052

Residual 2 0.000386 0.000193

Total 3 0.013838


Lower 95.0%

Upper 95.0%

Intercept 0.426462 0.019212 22.19775 0.002023 0.343799 0.509124 0.343799 0.509124

X Variable 1 0.209798 0.025136 8.346617 0.014052 0.101648 0.317949 0.101648 0.317949

Averages Fresh Day 3 Day 6 Day 9

Ave Readings 0.513663 0.528525 0.6018 0.659863

LCL 0.8 0.8 0.8 0.8

UCL 1.3 1.3 1.3 1.3

Normal Range Cu: 0.8 1.3

57

Figure 5.5: Regression Chart: Haemolysis and Copper

The chart below graphically demonstrate the regressive relationship between the independent variable

haemolysis and the dependence of the variable copper on it

5.3.2.2 Effect of Haemolysis on Zn (Zn) Readings (Group Comparison)

Table 5.5: Comparative Table: Average Zinc Readings

Average Fresh Day 3 Day 6 Day 9

Ave Zn Readings 0.9613 0.926363 1.185413 1.137988

LCL 0.7 0.7 0.7 0.7

UCL 1.3 1.3 1.3 1.3

Normal Range Zn: 0.7 1.3

Figure 5.6: Comparative Chart: Average Zinc Readings

This chart reflects the average zinc readings obtained from samples in the different sample groups, Fresh, Day 3,

Day 6 and Day 9

58

Table 5.6: Regression Table: Haemolysis and Zinc

Relationship between Zinc as a dependent variable of the independent variable Haemolysis


Multiple R 0.985948

R Square 0.972093



Observations 4

ANOVA


F

Regression 1 0.013452 0.013452 69.66601 0.014052

Residual 2 0.000386 0.000193

Total 3 0.013838


Lower 95.0%

Upper 95.0%

Intercept 0.426462 0.019212 22.19775 0.002023 0.343799 0.509124 0.343799 0.509124

X Variable 1 0.209798 0.025136 8.346617 0.014052 0.101648 0.317949 0.101648 0.317949

Figure 5.7: Regression Chart: Haemolysis and Zinc


haemolysis and the dependence of the variable zinc on it

59

5.3.3.3 Effect of Haemolysis on Calcium (Ca) Readings (Group Comparison)

Table 5.7: Comparative Table: Average Calcium Readings


Ave Ca Reading 2.571375 2.4785 2.177625 2.009125

LCL 2 2 2 2

UCL 3 3 3 3

Normal Range: Ca: 2 3

Figure: 5.8: Comparative Chart: Average Calcium Readings

This chart reflects the average calcium readings obtained from samples in the different sample groups, Fresh,


Table 5.8: Regression Table: Haemolysis and Calcium

Relationship between Calcium as a dependent variable of the independent variable Haemolysis


Multiple R 0.982234

R Square 0.964784



Observations 4

ANOVA


F

Regression 1 0.197545 0.197545 54.79284 0.017766

Residual 2 0.007211 0.003605

60

Total 3 0.204755


Lower 95.0%

Upper 95.0%

Intercept 2.882058 0.083015 34.71742 0.000829 2.524874 3.239241 2.524874 3.239241

X Variable 1 -0.80397 0.108611 -7.40222 0.017766 -1.27128 -0.33665 -1.27128 -0.33665

Figure 5.9: Regression Chart: Haemolysis and Calcium


haemolysis and the dependence of the variable calcium on it

5.3.2.4 Effect of Haemolysis on Phosphorous (Phos) Readings (Group Comparison)

Table 5.9: Comparative Table: Average Phosphorous Readings

Average Fresh Day 3 Day 6 Day 9

Ave Phos readings 2.4985 3.0575 3.393375 3.501375

LCL 0.9 0.9 0.9 0.9

UCL 2.55 2.55 2.55 2.55

Normal Range Phos: 0.9 2.55

61

Figure 5.10: Comparative Chart: Average Phosphorous Readings

This chart reflects the average phosphorous readings obtained from samples in the different sample groups,

Fresh, Day 3, Day 6 and Day 9

Table 5.10: Regression Table: Haemolysis and Phosphorous

Relationship between Phosphorous as a dependent variable of the independent variable Haemolysis


Multiple R 0.914041

R Square 0.835471



Observations 4

ANOVA


F

Regression 1 0.509751 0.509751 10.15595 0.085959

Residual 2 0.100385 0.050192

Total 3 0.610135


Lower 95.0%

Upper 95.0%

Intercept 2.192394 0.309744 7.078079 0.019382 0.859672 3.525117 0.859672 3.525117

X Variable 1 1.291469 0.405251 3.18684 0.085959 -0.45218 3.035122 -0.45218 3.035122

62

Figure 5.11: Regression Chart: Haemolysis and Phosphorous


haemolysis and the dependence of the variable phosphorous on it

5.3.2.5 Effect of Haemolysis on Magnesium (Mg) Readings (Group Comparison)

Table 5.11: Comparative Table: Average Magnesium Readings


Ave Mg Reading 0.8475 0.898375 0.915125 0.917125

LCL 0.7 0.7 0.7 0.7

UCL 1.23 1.23 1.23 1.23

Normal Range Mg: 0.7 1.23

Figure 5.12: Comparative Chart: Average Magnesium Reading

This chart reflects the average magnesium readings obtained from samples in the different sample groups, Fresh,


63

Table 5.12: Regression Table: Haemolysis and Magnesium

Relationship between Magnesium as a dependent variable of the independent variable Haemolysis


Multiple R 0.837505

R Square 0.701414



Observations 4

ANOVA


F

Regression 1 0.002217 0.002217 4.698236 0.162495

Residual 2 0.000944 0.000472

Total 3 0.003161


Lower 95.0%

Upper 95.0%

Intercept 0.833834 0.030036 27.76151 0.001295 0.704601 0.963067 0.704601 0.963067

X Variable 1 0.085177 0.039297 2.167542 0.162495 -0.0839 0.254258 -0.0839 0.254258

Figure 5.13: Regression Chart: Haemolysis and Magnesium


haemolysis and the dependence of the variable magnesium on it

5.3.2.6 Effect of Haemolysis on Iron (Fe) Readings (Group Comparison)

Table 5.13: Comparative Table: Average Iron Readings

Averages of all Fresh Day 3 Day 6 Day 9

Ave Fe Readings 41.04 46.70813 53.34225 61.244

LCL 29.73 29.73 29.73 29.73

64

UCL 39.76 39.76 39.76 39.76

Normal Range: Fe: 29.73 39.76

Figure 5.14: Comparative Chart: Average Iron Reading

This chart reflects the average iron readings obtained from samples in the different sample groups, Fresh, Day 3,

Day 6 and Day 9

Table 5.14: Regression Table: Haemolysis and Iron

Relationship between Iron as a dependent variable of the independent variable Haemolysis


Multiple R 0.998672

R Square 0.997345



Observations 4

ANOVA


F

Regression 1 226.7503 226.7503 751.3324 0.001328

Residual 2 0.603595 0.301798

Total 3 227.3538


Lower 95.0%

Upper 95.0%

Intercept 31.17379 0.759526 41.04377 0.000593 27.90582 34.44177 27.90582 34.44177

X Variable 1 27.23823 0.993717 27.41044 0.001328 22.96261 31.51386 22.96261 31.51386

65

Figure 5.15: Regression Chart: Haemolysis and Iron


haemolysis and the dependence of the variable iron on it


5.3.3.1 Haemolysis and Copper

Table 5.15: Anova Table: Copper

Copper (Cu) Anova: Two-Factor With Replication

SUMMARY Fresh 3Days 6 Days 9 Days Total

103

Count 8 8 8 8 32

Sum 5.141 5.653 5.372 8.005 24.171

Average 0.642625 0.706625 0.6715 1.000625 0.755344

Variance 0.036672 0.043833 0.104031 0.143967 0.09541

104

Count 8 8 8 8 32

Sum 3.713 3.359 4.387 4.631 16.09

Average 0.464125 0.419875 0.548375 0.578875 0.502813

Variance 0.055808 0.066352 0.120976 0.075361 0.076109

114

Count 8 8 8 8 32

Sum 4.564 4.554 5.485 5.821 20.424

Average 0.5705 0.56925 0.685625 0.727625 0.63825

Variance 0.042544 0.052616 0.075783 0.107636 0.067959

117

Count 8 8 8 8 32

Sum 4.212 4.534 4.731 5.112 18.589

66

Average 0.5265 0.56675 0.591375 0.639 0.580906

Variance 0.042044 0.055746 0.039459 0.059391 0.046117

119

Count 8 8 8 8 32

Sum 4.393 4.297 4.867 5.17 18.727

Average 0.549125 0.537125 0.608375 0.64625 0.585219

Variance 0.093026 0.07422 0.078053 0.163625 0.09437

120

Count 8 8 8 8 32

Sum 4.269 4.514 5.202 5.896 19.881

Average 0.533625 0.56425 0.65025 0.737 0.621281

Variance 0.099381 0.041934 0.035165 0.080208 0.064456

123

Count 8 8 8 8 32

Sum 3.404 3.099 3.847 3.61 13.96

Average 0.4255 0.387375 0.480875 0.45125 0.43625

Variance 0.049953 0.041805 0.069811 0.053079 0.049687

124

Count 8 8 8 8 32

Sum 3.149 3.236 4.017 3.792 14.194

Average 0.393625 0.4045 0.502125 0.474 0.443563

Variance 0.055768 0.051434 0.106872 0.060655 0.064197

129

Count 8 8 8 8 32

Sum 4.549 5.178 5.733 5.695 21.155

Average 0.568625 0.64725 0.716625 0.711875 0.661094

Variance 0.071468 0.046857 0.062313 0.088174 0.064417

141

Count 8 8 8 8 32

Sum 3.699 3.858 4.503 5.057 17.117

Average 0.462375 0.48225 0.562875 0.632125 0.534906

Variance 0.058469 0.033155 0.090226 0.083118 0.064546

Total

Count 80 80 80 80

Sum 41.093 42.282 48.144 52.789

Average 0.513663 0.528525 0.6018 0.659863

Variance 0.058882 0.055088 0.075073 0.102906

ANOVA

Source of Variation SS df MS F P-value F crit

Sample 2.865743 9 0.318416 4.531019 1.39E-05 1.913399

Columns 1.107071 3 0.369024 5.251162 0.001538 2.636845

Interaction 0.521312 27 0.019308 0.274748 0.999907 1.525695

Within 19.67691 280 0.070275

Total 24.17104 319

67

Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Copper (Cu) between Groups: Fresh, 3 Days, 6 Days, 9 Days

Null Hypothesis: µ1 = µ2 = µ3 = µ4

With Alternate Hypothesis: µ1 ≠ µ2 ≠ µ3 ≠ µ4

Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis stating that the effect of haemolysis on the trace mineral Copper (Cu) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Copper (Cu) is different between groups.

5.3.3.2 Haemolysis and Zinc

Table 5.16: Anova Table: Zinc

Zinc (Zn) Anova: Two-Factor With Replication


103

Count 8 8 8 8 32

Sum 8.664 8.006 8.745 10.115 35.53

Average 1.083 1.00075 1.093125 1.264375 1.110313

Variance 0.522766 0.13822 0.453 0.367628 0.34405

104

Count 8 8 8 8 32

Sum 8.393 7.823 9.144 8.906 34.266

Average 1.049125 0.977875 1.143 1.11325 1.070813

Variance 0.732534 0.218973 0.497457 0.270829 0.3925

114

Count 8 8 8 8 32

Sum 6.829 6.807 8.239 8.346 30.221

Average 0.853625 0.850875 1.029875 1.04325 0.944406

Variance 0.282059 0.167603 0.386919 0.286294 0.262343

117

Count 8 8 8 8 32

Sum 9.193 8.935 10.545 10.477 39.15

Average 1.149125 1.116875 1.318125 1.309625 1.223438

Variance 0.49744 0.24205 0.504953 0.482653 0.398575

119

Count 8 8 8 8 32

Sum 7.214 7.837 9.726 8.445 33.222

Average 0.90175 0.979625 1.21575 1.055625 1.038188

Variance 0.239446 0.214143 0.57448 0.349583 0.324987

120

Count 8 8 8 8 32

68

Sum 7.91 8.176 11.606 9.457 37.149

Average 0.98875 1.022 1.45075 1.182125 1.160906

Variance 0.231943 0.339421 1.111483 0.481089 0.523055

123

Count 8 8 8 8 32

Sum 7.645 6.221 9.403 9.358 32.627

Average 0.955625 0.777625 1.175375 1.16975 1.019594

Variance 0.386027 0.160835 0.153893 0.382036 0.272747

124

Count 8 8 8 8 32

Sum 7.014 5.992 9.086 8.55 30.642

Average 0.87675 0.749 1.13575 1.06875 0.957563

Variance 0.289164 0.171681 0.461036 0.323181 0.305438

129

Count 8 8 8 8 32

Sum 7.211 5.834 9.753 8.414 31.212

Average 0.901375 0.72925 1.219125 1.05175 0.975375

Variance 0.321768 0.256084 0.854584 0.372641 0.441482

141

Count 8 8 8 8 32

Sum 6.831 8.478 8.586 8.971 32.866

Average 0.853875 1.05975 1.07325 1.121375 1.027063

Variance 0.238468 0.36862 0.303739 0.577476 0.34693

Total

Count 80 80 80 80

Sum 76.904 74.109 94.833 91.039

Average 0.9613 0.926363 1.185413 1.137988

Variance 0.341258 0.219272 0.483907 0.35281

ANOVA


Sample 2.343324 9 0.260369 0.684543 0.722803 1.913399

Columns 3.936134 3 1.312045 3.449527 0.017096 2.636845

Interaction 1.539829 27 0.057031 0.149941 1 1.525695

Within 106.4994 280 0.380355

Total 114.3187 319

Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Zinc (Zn) between Groups: Fresh, 3 Days, 6 Days, 9 Days



Result: Since the calculated F test statistic does not exceed the F critical value obtained, it can therefore be said that there is significant evidence to accept the Null hypothesis, stating that the effect of haemolysis on the trace

69

mineral Zinc (Zn) in all the groups is equal. The alternate hypothesis , is to be rejected, stating that the effect of haemolysis on the level of trace mineral Zinc (Zn) is different between groups.

5.3.3.3 Haemolysis and Calcium

Table 5.17: Anova Table: Calcium

Calcium (Ca) Anova: Two-Factor With Replication


103

Count 8 8 8 8 32

Sum 21.91 20.97 18.33 17.41 78.62

Average 2.73875 2.62125 2.29125 2.17625 2.456875

Variance 0.010555 0.035555 0.050098 0.189198 0.119325

104

Count 8 8 8 8 32

Sum 19.71 18.66 16.53 15.23 70.13

Average 2.46375 2.3325 2.06625 1.90375 2.191563

Variance 0.008341 0.019279 0.022227 0.049284 0.072059

114

Count 8 8 8 8 32

Sum 20.96 20.34 16.95 15.98 74.23

Average 2.62 2.5425 2.11875 1.9975 2.319688

Variance 0.004571 0.009021 0.087698 0.108879 0.120752

117

Count 8 8 8 8 32

Sum 21.68 20.61 17.79 15.46 75.54

Average 2.71 2.57625 2.22375 1.9325 2.360625

Variance 0.013514 0.027084 0.048884 0.670279 0.267193

119

Count 8 8 8 8 32

Sum 19.79 18.86 16.51 14.31 69.47

Average 2.47375 2.3575 2.06375 1.78875 2.170938

Variance 0.017541 0.015279 0.058141 0.049041 0.104918

120

Count 8 8 8 8 32

Sum 20.3 19.49 15.55 14.03 69.37

Average 2.5375 2.43625 1.94375 1.75375 2.167813

Variance 0.010936 0.017255 0.058055 0.121227 0.157914

123

Count 8 8 8 8 32

Sum 21.34 20.9 19.12 18.55 79.91

Average 2.6675 2.6125 2.39 2.31875 2.497188

Variance 0.006621 0.013193 0.022629 0.041955 0.041156

124

Count 8 8 8 8 32

70

Sum 18.56 17.99 16.1 15.33 67.98

Average 2.32 2.24875 2.0125 1.91625 2.124375

Variance 0.0092 0.011012 0.019964 0.065712 0.052187

129

Count 8 8 8 8 32

Sum 21.12 20.39 18.78 17.64 77.93

Average 2.64 2.54875 2.3475 2.205 2.435313

Variance 0.0144 0.009984 0.02245 0.077229 0.057826

141

Count 8 8 8 8 32

Sum 20.34 20.07 18.55 16.79 75.75

Average 2.5425 2.50875 2.31875 2.09875 2.367188

Variance 0.006879 0.013527 0.029784 0.01287 0.046543

Total

Count 80 80 80 80

Sum 205.71 198.28 174.21 160.73

Average 2.571375 2.4785 2.177625 2.009125

Variance 0.02426 0.030193 0.059459 0.154145

ANOVA


Sample 5.320782 9 0.591198 11.37274 3.58E-15 1.913399

Columns 16.38042 3 5.460139 105.0354 1.41E-45 2.636845

Interaction 1.300212 27 0.048156 0.926366 0.573893 1.525695

Within 14.55546 280 0.051984

Total 37.55687 319

Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Calcium (Ca) between Groups: Fresh, 3 Days, 6 Days, 9 Days



Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis, stating that the effect of haemolysis on the trace mineral Calcium (Ca) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Calcium (Ca) is different between groups.

5.3.3.4 Haemolysis and Phosphorous

Table 5.18: Anova Table: Phosphorous

Phosphorous (Phos) Anova: Two-Factor with Replication

71


103

Count 8 8 8 8 32

Sum 17.34 22.22 25.13 26.16 90.85

Average 2.1675 2.7775 3.14125 3.27 2.839063

Variance 0.195421 0.25825 0.100813 0.134829 0.344506

104

Count 8 8 8 8 32

Sum 19.59 26.39 27.14 30.1 103.22

Average 2.44875 3.29875 3.3925 3.7625 3.225625

Variance 0.085841 0.137155 0.152336 0.21265 0.371471

114

Count 8 8 8 8 32

Sum 17.9 22.97 26.5 27.12 94.49

Average 2.2375 2.87125 3.3125 3.39 2.952813

Variance 0.103364 0.090755 0.148221 0.083514 0.312634

117

Count 8 8 8 8 32

Sum 18.48 23.21 25.05 23.03 89.77

Average 2.31 2.90125 3.13125 2.87875 2.805313

Variance 0.097571 0.025184 0.108527 1.417727 0.466852

119

Count 8 8 8 8 32

Sum 26.66 31.21 32.84 34.66 125.37

Average 3.3325 3.90125 4.105 4.3325 3.917813

Variance 0.51745 0.421784 0.271371 0.164307 0.452366

120

Count 8 8 8 8 32

Sum 20.73 26.59 28.9 30.04 106.26

Average 2.59125 3.32375 3.6125 3.755 3.320625

Variance 0.30167 0.244398 0.176879 0.111629 0.396419

123

Count 8 8 8 8 32

Sum 17.07 20.28 23.06 22.76 83.17

Average 2.13375 2.535 2.8825 2.845 2.599063

Variance 0.07477 0.072943 0.056079 0.019 0.143583

124

Count 8 8 8 8 32

Sum 25.56 29.16 31.57 34.38 120.67

Average 3.195 3.645 3.94625 4.2975 3.770938

Variance 0.082057 0.180029 0.159227 0.138936 0.295686

129

Count 8 8 8 8 32

Sum 17.44 20.77 25.69 26.46 90.36

Average 2.18 2.59625 3.21125 3.3075 2.82375

Variance 0.083229 0.064084 0.251241 0.069536 0.32514

72

141

Count 8 8 8 8 32

Sum 19.11 21.8 25.59 25.4 91.9

Average 2.38875 2.725 3.19875 3.175 2.871875

Variance 0.068127 0.079543 0.035441 0.036029 0.166571

Total

Count 80 80 80 80

Sum 199.88 244.6 271.47 280.11

Average 2.4985 3.0575 3.393375 3.501375

Variance 0.309853 0.333908 0.264159 0.463318

ANOVA


Sample 55.60667 9 6.178519 35.14559 3.56E-41 1.913399

Columns 48.81083 3 16.27028 92.55105 1.22E-41 2.636845

Interaction 3.497787 27 0.129548 0.736913 0.827906 1.525695

Within 49.2234 280 0.175798

Total 157.1387 319

Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Phosphorous (Phos) between Groups: Fresh, 3 Days, 6 Days, 9 Days



Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis, stating that the effect of haemolysis on the trace mineral Phosphorous (Phos) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Phosphorous (Phos) is different between groups.

5.3.3.5 Haemolysis and Magnesium

Table 5.19: Anova Table: Magnesium

Magnesium (Mg) Anova: Two-Factor With Replication


103

Count 8 8 8 8 32

Sum 6.81 7.15 7.35 7.69 29

Average 0.85125 0.89375 0.91875 0.96125 0.90625

Variance 0.004384 0.007484 0.004584 0.006927 0.006921

104

Count 8 8 8 8 32

73

Sum 6.64 7.19 7.35 7.32 28.5

Average 0.83 0.89875 0.91875 0.915 0.890625

Variance 0.002114 0.002755 0.004327 0.0028 0.004032

114

Count 8 8 8 8 32

Sum 6.67 7.1 7.19 7.39 28.35

Average 0.83375 0.8875 0.89875 0.92375 0.885938

Variance 0.00237 0.003993 0.005727 0.00897 0.00587

117

Count 8 8 8 8 32

Sum 7.46 7.84 7.73 7.16 30.19

Average 0.9325 0.98 0.96625 0.895 0.943438

Variance 0.001021 0.001514 0.001027 0.135057 0.032417

119

Count 8 8 8 8 32

Sum 6.87 7.27 7.53 7.49 29.16

Average 0.85875 0.90875 0.94125 0.93625 0.91125

Variance 0.006013 0.006127 0.011184 0.010741 0.008798

120

Count 8 8 8 8 32

Sum 6.96 7.47 7.43 7.61 29.47

Average 0.87 0.93375 0.92875 0.95125 0.920938

Variance 0.003371 0.00437 0.004927 0.006127 0.005209

123

Count 8 8 8 8 32

Sum 6.44 6.82 7.19 6.97 27.42

Average 0.805 0.8525 0.89875 0.87125 0.856875

Variance 0.000943 0.001621 0.008641 0.00447 0.004745

124

Count 8 8 8 8 32

Sum 6.41 6.8 6.95 7.1 27.26

Average 0.80125 0.85 0.86875 0.8875 0.851875

Variance 0.001355 0.004686 0.005612 0.008336 0.005577

129

Count 8 8 8 8 32

Sum 6.8 7.12 7.3 7.41 28.63

Average 0.85 0.89 0.9125 0.92625 0.894688

Variance 0.002029 0.004114 0.004564 0.009512 0.005426

141

Count 8 8 8 8 32

Sum 6.74 7.11 7.19 7.23 28.27

Average 0.8425 0.88875 0.89875 0.90375 0.883438

Variance 0.001879 0.002612 0.003384 0.00257 0.002965

Total

Count 80 80 80 80

Sum 67.8 71.87 73.21 73.37

74

Average 0.8475 0.898375 0.915125 0.917125

Variance 0.003508 0.004773 0.005438 0.018059

ANOVA


Sample 0.222583 9 0.024731 3.148083 0.001237 1.913399

Columns 0.252903 3 0.084301 10.73076 1.07E-06 2.636845

Interaction 0.088156 27 0.003265 0.415609 0.996016 1.525695

Within 2.199688 280 0.007856

Total 2.76333 319

Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Magnesium (Mg) between Groups: Fresh, 3 Days, 6 Days, 9 Days



Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis, stating that the effect of haemolysis on the trace mineral Magnesium (Mg) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Magnesium (Mg) is different between groups.

5.3.3.6 Haemolysis and Iron

Table 5.20: Anova Table: Iron

Iron (Fe) Anova: Two-Factor With Replication


103

Count 8 8 8 8 32

Sum 315.64 371.63 435.82 514.35 1637.44

Average 39.455 46.45375 54.4775 64.29375 51.17

Variance 11.83446 25.77963 125.5091 307.742 194.7519

104

Count 8 8 8 8 32

Sum 320.46 388.16 379.58 464.47 1552.67

Average 40.0575 48.52 47.4475 58.05875 48.52094

Variance 65.47414 53.8294 20.52985 244.3262 129.0043

114

Count 8 8 8 8 32

Sum 359.51 400.14 458.57 522.9 1741.12

Average 44.93875 50.0175 57.32125 65.3625 54.41

Variance 53.78201 89.47199 183.7488 288.2475 200.2001

117

75

Count 8 8 8 8 32

Sum 328.35 368.39 417.46 516 1630.2

Average 41.04375 46.04875 52.1825 64.5 50.94375

Variance 188.5319 173.4337 70.14959 457.4839 280.1746

119

Count 8 8 8 8 32

Sum 384.9 423.95 437.05 523.42 1769.32

Average 48.1125 52.99375 54.63125 65.4275 55.29125

Variance 161.3263 200.6669 152.0538 309.3921 227.2262

120

Count 8 8 8 8 32

Sum 299.47 350.75 438.87 475.05 1564.14

Average 37.43375 43.84375 54.85875 59.38125 48.87938

Variance 3.15337 12.3668 229.4049 103.355 156.6832

123

Count 8 8 8 8 32

Sum 329.99 378.78 467.9 496.44 1673.11

Average 41.24875 47.3475 58.4875 62.055 52.28469

Variance 77.67024 85.31002 348.1657 157.1175 223.1825

124

Count 8 8 8 8 32

Sum 343.57 384.24 460.07 516.76 1704.64

Average 42.94625 48.03 57.50875 64.595 53.27

Variance 67.96503 86.39751 266.5112 212.2411 215.2866

129

Count 8 8 8 8 32

Sum 327.58 355.01 406.17 459.06 1547.82

Average 40.9475 44.37625 50.77125 57.3825 48.36938

Variance 166.3284 131.8225 160.9571 189.144 187.1626

141

Count 8 8 8 8 32

Sum 273.73 315.6 365.89 411.07 1366.29

Average 34.21625 39.45 45.73625 51.38375 42.69656

Variance 8.490884 15.015 15.95763 89.67888 72.29997

Total

Count 80 80 80 80

Sum 3283.2 3736.65 4267.38 4899.52

Average 41.04 46.70813 53.34225 61.244

Variance 84.65213 89.87889 156.1351 228.4447

ANOVA


Sample 3892.942 9 432.5491 3.083928 0.001514 1.913399

Columns 18188.31 3 6062.77 43.22549 5.5E-23 2.636845

Interaction 1004.254 27 37.19461 0.265185 0.999935 1.525695

Within 39272.56 280 140.2592

76

Total 62358.07 319

Conduct Anova to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Iron (Fe) between Groups: Fresh, 3 Days, 6 Days, 9 Days



Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis, stating that the effect of haemolysis on the trace mineral Iron (Fe) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Iron (Fe) is different between groups

77

5.3.4 Demonstration of normal ranges

5.3.4.1. Effect of Haemolysis on CopperFigure 5.16: Effect of Haemolysis on Copper: Fresh

Figure 5.18: Effect of Haemolysis on Copper: 6 Days



78

5.3.4.2. Effect of Haemolysis on Zinc

Figure 5.20: Effect of Haemolysis on Zinc: Fresh

Figure 5.22: Effect of Haemolysis on Zinc: 6 Days



79

5.3.4.3. Effect of Haemolysis on Calcium

Figure 5.24: Effect of Haemolysis on Calcium: Fresh

Figure 5.26: Effect of Haemolysis on Calcium: 6 Days



80

5.3.4.4. Effect on Haemolysis on Phosphorous

Figure 5.28: Effect of Haemolysis on Phosphorous: Fresh

Figure 5.30: Effect of Haemolysis on Phosphorous: 6 Days



81

5.3.4.5. Effect of Haemolysis on Magnesium

Figure 5.32: Effect of Haemolysis on Magnesium: Fresh

Figure 5.34: Effect of Haemolysis on Magnesium: 6 Days



82

5.3.4.6. Effect of Haemolysis on Iron

Figure 5.36: Effect of Haemolysis on Iron: Fresh

Figure 5.38: Effect of Haemolysis on Iron: 6 Days



83


With reference to the appearance of the initial samples collected after centrifugation took

place it was observed that there was indeed on average, a difference in the intensity between

the different group from Fresh being almost colourless to the Frozen samples being

exceedingly dark. Upon measurement of these samples the spectrophotometer reading

corroborated this observation.

The means and frequency of responses to a brief survey, given to a panel of laboratory staff

considered to be competent in the biochemistry section, were determined to be important in

order to ascertain focus area for improvement. Given a lickets scale of six potential answers,

Statistical analysis conducted on feedback revealed the following:

Figure 5.40: Graphic Representation of Results of Observation Data collected:

Shortfalls: Range of responses in the form of answers obtained from the panel of laboratory staff competent in Biochemistry

Question 1 Question 2


Question 5

Pie chart for QUES1

5: 33.33 %

6: 66.67 %

Pie chart for QUES2

SD: 33.33 %

MA: 66.67 %

Pie chart for QUES3

CD: 33.33 %

MD: 66.67 %

Pie chart for QUES4

CD: 33.33 %

MD: 66.67 %

Pie chart for QUES5

CD: 33.33 %

MD: 66.67 %

84


Improvement: Range of responses in the form of answers obtained from the panel of laboratory staff competent in Biochemistry



Question 5

The staff was also provided with pictures of samples in the form of a blind test (samples not

identified), and requested to associate + values to them based on colour intensity. The means

and frequencies of these responses were also statistically analysed revealing the following:

Pie chart for QUES1

MA: 33.33 %

CD: 66.67 %

Pie chart for QUES2

CD: 33.33 %

MD: 33.33 %

CD: 33.33 %

Pie chart for QUES3

MA: 100 %

Pie chart for QUES4

SA: 33.33 %

MA: 33.33 %

CD: 33.33 %

Pie chart for QUES5

MA: 66.67 %

CD: 33.33 %

85


Haemolysis Grading: Range of responses in the form of answers obtained from the panel of laboratory staff competent in

Biochemistry

Tube 1: Fresh Sample Tube 2: Three Day old Samples

Tube 3: Six Day old Sample Tube 4: Nine Day old Sample

Tube 5: Frozen Sample


The spider chart below illustrates the focal areas of the protocol analysis as determined by the

affinity diagram. (Affinity diagram identified major categories, spider charts demonstrate the

depth/importance of the investigation required in all). Thus graphical representation was

developed based on the answers from the checklist drawn up to test the systems.

Pie chart for TUBE1

0: 100 %

Pie chart for TUBE2

+: 100 %

Pie chart for TUBE3

++: 40 %

+++: 60 %

Pie chart for TUBE4

++: 20 %

+++: 60 %

++++: 20 %

Pie chart for TUBE5

++++: 20 %

+++++: 80 %

86

Figure 5.43: Spiderchart: Results of Protocol Analysis

5.4 INTERPRETATION OF STATISTICAL ANALYSIS


Based on investigation of regression and correlation analysis it was determined that a linear

positive relationship exists between the independent variable of time with haemolysis being a

dependent variable of this. Furthermore the charts provide illustrating the trend of an

increase of level of haemolysis in samples with the progression of time. Further tests are

deemed necessary to indicate the impact on quality


Statistical examination of the effect of haemolysis effect on individual trace elements

revealed the following

5.4.2.1 Haemolysis vs. Copper

An increase in copper levels in serum samples was detected with the increase in haemolysis

levels. Upon further investigation involving regression and correlation, it was determined

September 22, 2010

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Survey Sample - 8 Categories

Operational Systems in Biochemistry Environment

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LegendPre-analytical

FactorsEquipment

Factors

Process Factors External Factors

Support Requirements Defined: Documentation and Records

Process Structure: Logical, sequential,

comprehensive

Process Requirements Defined: Documentation and Records

Corrective Action/

Preventative Measure in

system

Control Measures in system

Traceabilty and adequate Identification of samples in system

Calibration in system

Effectivity

Notes:

This chart illustrates the results of protocol analysis on the operational environment of biochemistry laboratory in relation to the various systems which comprise of it namely:

Pre-analytical Systems Core Process Systems Equipment Factor Systems External Systems input

A graphic representation is given, enabling the user to identify the strengths as well as the weakesses in each of the pre-defined systems

87

that a positive linear relationship existed between the independent variable of haemolysis in

relation to dependent variable of copper.

5.4.2.2 Haemolysis vs. Zinc

The association between the independent variable haemolysis and dependent variable does

not appear to be as firm as the relationship between haemolysis and copper mean values,

based on examination of the chart, despite evidence of a leaning towards the same trend as

the prior. However upon further investigation involving regression and correlation it was

established that a linear positive relationship exists between haemolysis and zinc.

5.4.2.3 Haemolysis vs. Calcium

By observing charted mean values, it was detected with the increase in haemolysis levels led

to the subsequent decrease in calcium levels in serum samples. Upon further investigation

involving correlation analysis, it was determined that a negative linear dependence

relationship existed between the independent variable of haemolysis in relation to dependent

variable of calcium.

5.4.3.4 Haemolysis vs. Phosphorous

An increase in phosphorous levels in serum samples was observed with the increase in

haemolysis levels in the samples. Upon further investigation involving regression and

correlation, it was determined that a positive linear relationship existed between the

independent variable of haemolysis in relation to dependent variable of phosphorous.

5.4.3.5 Haemolysis vs. Magnesium

It was observed that with the increase of haemolysis levels, magnesium levels were also

found to increase, based on the charted mean values. Upon further investigation involving

regression and correlation, it was determined that a positive linear relationship existed

between the independent variable of haemolysis in relation to dependent variable of

magnesium.

5.4.3.6 Haemolysis vs. Iron

An increase in iron levels in serum samples was detected with the increase in haemolysis

levels. Upon further investigation involving regression and correlation, it was determined

88

that a positive linear relationship existed between the independent variable of haemolysis in

relation to dependent variable of iron.


Interpretation of Anova Hypothesis testing, based on

Null hypothesis:µF = µ3 Days = µ6 Days = µ9 Days

With Alternate Hypothesis: µF ≠ µ3 Days ≠ µ6 Days ≠ µ9 Days,

For all groups, with the exception of Zinc, it was found that it could be statistically proven that there was a difference between the effect of haemolysis on trace mineral levels in the different groups.

5.4.4 Demonstration of Normal Ranges

Interpretation of these charts reveal where mean values for trace minerals in the different time

groups and trace mineral groups, can be found in relation to recommended normal values

used by the laboratory. It was found that:

Mean copper mineral levels were significantly out of recommended normal range, as

expected due to known environmental factor of soil copper levels in the Stellenbosch

area being traditionally low. Copper levels were elevated however with the upward

progression of haemolysis in samples, however this elevation was detrimental to

result quality output even though it was out of recommended normal range.

Mean zinc mineral levels stayed within recommended normal range, however a trend

was observed of the mean zinc levels being elevated with the upward progression of

haemolysis levels. Thus it can be said that uncontrolled haemolysis in samples would

have a detrimental effect to result quality output.

Mean calcium trace mineral levels were initially found to be within the recommended

normal range, however with the upward progression of haemolysis, the mean calcium

levels were found to significantly decrease, thus uncontrolled haemolysis in samples

would have a detrimental effect to result quality output. Group 9 mean calcium levels

were out of recommended normal range.

The mean phosphorous trace mineral level of the fresh group was found to be within

recommended normal range, however with the upward progression of haemolysis the

mean trace mineral value steadily moved upwards and significantly out of

recommended normal range.

89

Mean magnesium trace mineral levels stayed within recommended normal range,

however an upward trend was detected in relation to the upward progression of

haemolysis.

The mean iron trace mineral level of the fresh group was only slightly out of

recommended normal range. It was determined that this could also be as a result of

environmental factor impacting on research, however the significant observation to be

made during interpretation, is considered to be the steady and marked upward trend of

mean iron levels with the upward progression of haemolysis occurrence in samples.

Mean iron levels of groups Day 3, 6 and 9 are considered to be significantly out of

recommended normal range.


Interpretation of the first phase of observation analysis ensues the relationship between the

colour intensity and spectrophotometer readings obtained of those samples. Data collected is

found to substantiate and corroborate this positive linear relationship.

Interpretation of the second phase of observation analysis is found to be the significant

finding that the opinions, based on the responses obtained from survey group were all very

similar. In the 3 instances where opinions were sought, the mean results is demonstrated to

all fall with the 15.8th percentile or within 1 standard deviation of each other. The

importance of this is the guidance and direction it provides with regard to a quality

improvement to be made to the system


The results illustrated by application of the protocol analysis data collected into a spider

chart, indicates the areas in the operational system in biochemistry which need to be address

in order to make a quality improvement in the section. As can be seen from the chart,

Process factors for the Pre-analytical system, Core process system and Equipment factors

were logical and comprehensive. Furthermore QMS documentation, support systems,

traceability, maintenance and adequate control measures were found to be effectively

operating in these systems. The results of the spider chart indicate that the most important

system to focus on, in order to attain quality improvement in the section, would be the system

comprised of external factors impacting on sample processing within the biochemistry

section.

90

5.5 PROBLEMS ENCOUNTERED DURING RESEARCH

Frozen blood could not be read spectrophotometrically, as the spectrophotometer could not

read such high values. As further testing on the samples (in terms of the effect of

haemolysis) would be futile since the level of haemolysis could not be detected, the

batch/group of samples was discarded by project plan.

It was found that normal ranges of the animal control group used for this project were out of

range of the recommended normal control ranges. This development was not completely

unexpected, as it is a typically known fact that environmental and handling factors has

influence normal ranges and it is also a known fact that WCPVL is situated in a copper

deficient area thus sheep are routinely dosed with Multimins copper supplement due to low

Cu in soil. The animal control group received supplementation on the 5th February 2010 and

approximately every 6-8weeks thereafter.

5.6 KEY RESEARCH FINDINGS

Data Analysis reveals the following key research findings:

The Quality Management System in operation in the Biochemistry Section is predominantly reliable and the basis for good quality processes occurring within the section. It appears as if the aspect of corrective action could require consideration however it is accepted that with nature of an environment such as biochemistry in addition to the good quality procedures already implemented in the section, in the event of an unforeseeable non-conformance occurring, the only appropriate corrective action would take the form of a Re-do. The event of samples being redone, results in the cost of analysis increasing twofold

The most detrimental effect on the quality of the system in operation in Biochemistry takes the form of external factors which influence the system.

Haemolysis levels have an influence on the range of trace mineral levels occurring in samples and can be demonstrated when compared to recommended normal reference values

A linear relationship is found to exist between haemolysis levels and trace mineral levels

The extent of the relationship is found to be different for each of the different trace elements, thus the actual effect of the extent of haemolytic impact differed from element to element

91

In line with the individual effect of the haemolytic impact on a particular element, this effect was found to be progressive over time, in all cases.

A linear positive relationship is found to exist between time before centrifugation takes place and haemolysis level.

92

CHAPTER 6: CONCLUSION AND RECOMMENDATIONS

6.1 BACKGROUND

The absence of the event of screening blood samples sent to the Western Cape Provincial

Veterinary Laboratory in order to determine their suitability for analytical purposes, has been

a quality concern for management of the laboratory in recent times. Thus it was undertaken

to investigate the impact of factors suspected of possibly impacting the quality of results

delivered by the Biochemistry section, as a result of unscreened blood being analysed by the

section.

6.2 THE RESEARCH PROBLEM RE-VISITED

Trace Mineral Results from analysis carried out in the Biochemistry laboratory of WCPVL

are possibly invalid or of poor quality due to levels of sample haemolysis going unscreened

in the section.

The exercise of processing samples in the Biochemistry section would prove have a futile

purpose, if minimum requirements for sample suitability to provide adequate results cannot

be determined. A known factor believed to influence trace mineral values is the presence of

haemolysis in samples. Haemolysis, resulting from the lyses of red blood corpuscles in blood

serum, delivers a visual effect, which can be optically observed in samples. Optical

observation, as a stand-alone method is not however, scientifically acceptable in order to

validate and accept results in terms of reliability neither quality. It is necessary thus to

determine a method to substantiate optical observation, such as the use of a light

spectrophotometer and furthermore determine at critical level or value above which samples

can no longer be accepted for trace mineral analysis.

6.3 PRIMARY RESEARCH QUESTION RE-VISITED

6.3.1 Primary Question

93

What is the maximum haemolysis level acceptable, as measured in terms of optical density

using a spectrophotometer at 540nm wavelength, in order to accept samples for Trace

Mineral Analysis?

6.4 INVESTIGATIVE RESEARCH QUESTIONS RE-VISITED

6.4.2 Investigative Questions

Is there a difference in the haemolysis level of samples read on Day 0, Day 3, Day 6, Day

9 and Frozen.

Are unacceptable samples being accepted as suitable in the current system?

What are the shortfalls of the current system?

What are the practical considerations or recommendations that can be made to manage

acceptance procedures of samples?

6.5 RESEARCH OBJECTIVES RE-VISITED

To determine exact values of acceptable levels of haemolysis when accepting blood samples

for trace mineral analysis in terms of the concentrations of trace minerals present in serum,

measured in nanometre (nm) units when read on a spectrophotometer.

Secondary objectives evolving from the primary objective:

To determine practical measurable methods available to identify unacceptable samples.

To determine the effects of haemolysis levels of blood samples at WCPVL in terms of the

quality system of the laboratory and implications thereof.

To identify preventative measures required to be implemented within biochemistry laboratory

to prevent future acceptance of unsuitable samples.

94

6.6 RECOMMENDATIONS

Based on literature reviewed in addition to data analysis and

interpretation, the following recommendations are made in order to effect a quality

improvement in the Biochemistry section:

It can be said that the presence of haemolysis in samples has a direct effect and impact of

the quality standards in the laboratory where the tests are being conducted, resulting in

erroneous results being produced by analysis carried out on inadequate samples. (Thomas,

2010: online) Erroneous results have detrimental implications to clinical laboratories in

terms of quality. Lippi,, (2009: online) states that a major worldwide concern for all clinical

laboratories is in vitro haemolysis as through affecting test results it seriously impacts on

patient care and the laboratory’s reputation. (Lippi, 2009: online)

Spencer, Rogers (1995: online) suggests that between quality improvement and haemolysis a

direct link exists. Although it is physically possible to produce results at a remarkable speed

and accurately within decimal point ad infinitum, it becomes redundant if the specimen is

unsuitable. Walters, Williams, Hazer, and Kameneva, (2007: online) argue that the baseline

degree of hemolysis present in blood is determined by the level of free hemoglobin in

plasma/serum. (Walters et al, 2007: online)

It is thus recommended that samples received by the section for trace element analysis must

be screened for suitability for analysis.

Trying to eliminate unsuitable specimens such as haemolysed specimens can thus be seen to

be part of a Quality Improvement Process (QIP) and Spencer and Rodgers (1995: online)

proposed a 4 step system in order to do so. The implementation of countermeasures as Step

3, is further described by Spencer et al., (1995: online), with the goal of providing uniformity

in operation through creating an easy-to-use visual aid for grading hemolysis. By the

collation of information in addition to this information being formatted, a user-friendly chart

of acceptable levels of hemolysis for specific tests is developed for use in specific testing

procedures. (Spencer et al., 1995: online)

95

The recommendation is thus also made that the use of a colour chart be employed as a

practical measure in order to screen samples received for trace mineral analysis at WCPVL.

Spectrophotometers are standard research tools, used in chemistry laboratories, utilizing the

relationship absorption of light and colour as principle for the way it works. (Hoydt, n.d.:

online) As Henry, Cannon, and Winkelman assert that Spectrophotomic methods can be to

read hemoglobin levels. (Henry et al., 1974 (6)) in addition to Raphael (1983: (17)) affirming

that for analytical purposes a spectrophotometer is used to identify and quantify a substance

by determine the extent of the absorption of light energy, the recommendation is thus made

that if any uncertainty surrounding the acceptability of a particular sample avails, a

spectrophotometric test may be done to establish that the sample does not exceed the

maximum acceptance level of 0.377nm

A flowchart, also known as a process map, helps organisations improve the efficiency of their

systems asserts Snow (2005: (1)). According to Snow, process maps can be used in a number

of ways to analyse performance, including the evaluation of the current situation, the

identification of break-downs in the current system such as duplication of effort, gaps,

bottlenecks etc. Thus “a process map can be utilised to identify strengths and weaknesses of

a system, in carrying out it’s purpose” leading to the satisfaction of customers and

stakeholders and ultimately quality improvement. (Snow, 2005: (2))

A recommendation is thus made, that the following revision be made to the workflow

procedure in Biochemistry: See Annexure C: Modified Workflow Flowchart

The post analysis detection erroneous results involves Re-do. Although cost considerations

are not ranked as highly by the laboratory management as the transience implications due to

the Laboratory being the Department of Agriculture’s essential diagnostic service provider, in

any business it, cost remains important. Logically and unarguably, re-sampling, and the re-

processing of samples are associated to cost implications for the laboratory. Literature

reviewed reveals that Ong, Chan, Lim (2009: online) found a cost saving occurred with a

reduction in sample hemolysis from 19.8% (before) to 4.9% (after) (P <.001). This further

translated into a cost savings of SGD$834.40 (USD$556.30) per day in a study conducted by

them, and SGD$304,556 (USD$203,037) per year. (Ong et al., 2009: online). In terms of

WCPVL, the cost of a trace element analysis test conducted by the laboratory is R39.85 and

96

the cost of Cu or Zn analysis is also R39.85. Based on average amount of 211 samples per

month, it is thus determined that a potential total savings of R8408.35 on average per month,

dependent on the amount of unsuitable samples submitted, will be achieved by the laboratory.

Thus it is considered, as purported by Ong, Chan, Lim (2009: online), haemolysis poses a

problem in terms improvement of quality relating to monetary benefit, and thus

recommendation is made to implement the above-mentioned

recommended quality improvement measures in order to attain cost

benefit for the laboratory.

6.7 CONCLUSION

In conclusion, it can be said that the ultimately objective set out to be achieved, of

establishing an acceptance level in terms of haemolysis level, in order to assure quality of

results issued by the Biochemistry section has been successfully accomplished through the

research conducted and can be identified as a value not exceeding 0.377nm

It is believed that this critical value, associated to an indicator presented on an acceptance

colour chart, will fulfil a very beneficial role in assuring quality results thereby ensuring

quality practices are followed in the laboratory and ultimately customer satisfaction. By the

implementation of recommendations made by this research, it will enable the diagnostic

institute known as the Western Cape Provincial Veterinary Laboratory to build on their

existing reputation of being frontrunner in terms of the diagnostic quality of results they

deliver. This improvement action undertaken serves as confirmation of importance WCPVL

places on the accuracy, precision and reliability of their diagnostic problem, thereby truly

fulfilling their role of delivering an outstanding first-rate and high quality service to the

department of agriculture and agricultural community at large, in the Western Cape Province.

97

BIBLIOGRAPHY

1. Allpsych Online Virtual Classroom, 2004, Research Methods

http://allpsych.com/researchmethods/measurementscales.html [17 September 2010]

2. Arsham, Hossein, 1994, Statistical thinking for Managerial decisions,

http://home.ubalt.edu/ntsbarsh/Business-stat/opre504.htm#rrstatthink [3 October

2010]

3. Arzoumanian, Lena, October 2003, What is Haemolysis, Tech Talk, Volume 2, No. 2;

http://www.bd.com/vacutainer/pdfs/techtalk/TechTalk_Jan2004_VS7167.pdf [9

February 2010]

4. Businessdictionary.com, n.d

http://www.businessdictionary.com/definition/process.html, [8 August 2010]

5. Businessdictionary.com, n.d

http://www.businessdictionary.com/definition/system.html, [8 August 2010]

6. Concise Oxford Veterinary dictionary. 1st ed. 1988. Oxford: Oxford University Press

7. Dick, B. 1993, How to conduct and report action research, Action Research Theses

(Thesis Resource Paper) http://www.scu.edu.au/schools/gcm/ar/art/arthesis.html [16

April 2010]

8. Fraser, Dr. Michael, n.d. Research Technologies Service, Supporting virtual research

environments, http://users.ox.ac.uk/~mikef/rts/vre/img2.html [26 August 2010]

9. Gate to Quality. n.d. Quality Tools. http://www.gate2quality.com/quality/quality-

tools.html [6 October 2010]

10. Grafmeyer D, Bondon M, Manchon M, and Levillain P. 1995. The influence of

bilirubin, haemolysis and turbidity on 20 analytical tests performed on automatic

analysers. Results of an interlaboratory study. Eur J Clin Chem Clin Biochem. 1995

Jan; 33(1):31-52. [Abstract in the PUBMED database, ref no. 7756439]

11. Guder, W.G, 1986, Haemolysis as an influence and interference factor in clinical

chemistry. Journal of clinical chemistry and clinical biochemistry Vol 24 No. 2; 125 -

126 http://www.reference-global.com/doi/abs/10.1515/cclm.1986.24.2.125 (11 [12

February 2010]

12. Henry, R. J, Cannon, D. C, & Winkelman, J. W. (eds). 1974. Clinical Chemistry

Principles and Techniques. Medical Department, Harper & Row Publishers

98

http://www.reference-global.com/doi/abs/10.1515/cclm.1986.24.2.125%20(11

http://www.gate2quality.com/quality/quality-tools.html%20%5B6

http://www.gate2quality.com/quality/quality-tools.html%20%5B6

http://users.ox.ac.uk/~mikef/rts/vre/img2.html%20%5B26

http://www.businessdictionary.com/definition/process.html



http://www.bd.com/vacutainer/pdfs/techtalk/TechTalk_Jan2004_VS7167.pdf

http://home.ubalt.edu/ntsbarsh/Business-stat/opre504.htm#rrstatthink

http://allpsych.com/researchmethods/measurementscales.html%20%5B17

13. Hidiroglou M, 1983, Blood Plasma Minerals and Vitamins and Acid-Base Status of

Sheep Raised under fluctuating or constant Environment. Journal of Dairy Science

Vol 66 No. 1 67-72 http://jds.fass.org/cgi/reprint/66/1/67.pdf [11 February 2010]

14. Hoyt, n.d., Spectrophotometers and the standard curve.

http://www.ccbcweb.net/archive/pdf/spec20_lab_ex.PDF [1 May 2010]

15. Jiju Antony Mike Kaye, Andreas Frangou, 1998, A strategic methodology to the use

of advanced statistical quality improvement techniques, The TQM Magazine, Vol. 10

Iss: 3, pp.169 – 176 http://www.emeraldinsight.com/journals.htm?

articleid=841840&show=html [Abstract in Emerald database, ref no.

10.1108/09544789810214765]

16. Johnson, Paula D, Besselsen, David G. 2002. Practical Aspects of Experimental

Design in Animal Research. Institute for Laboratory Animal Research (ILAR) Journal

V43(4) http://dels-old.nas.edu/ilar_n/ilarjournal/43_4/v4304Johnson.shtml [11 March

2010]

17. Juran, Joseph, 1988, Juran’s Quality Control Handbook, Texas: Mcgraw-Hill.

18. Lippi,Prof. G, June 2009, In Vitro Haemolysis; Causes, Prevalence, Effects,

Measurement and Solutions. http://www.specimencare.com/resource.aspx?

IDX=11331 (13 February 2010)

19. Market Research, Glossary of Terms, http://www.marketresearchterms.com/p.php

[05 October 2010]

20. Na Na, Jin Ouyang, Youri, E. C. Taes and Joris, R. Delanghe. Clinical Chemistry

Online Journal, 2005, 51(9) pg 1754-1755.

http://www.thefreelibrary.com/Serum+free+hemoglobin+concentrations+in+healthy+

individuals+are...-a0209353244) [16 April 2010]

21. Narayanan S, Guder WG, n.d. Preanalytical Variables and Their influence on the

Quality of Laboratory Results, The Journal of the International Federation of Clinical

Chemistry and Laboratory Medicine,

http://www.ifcc.org/ejifcc/vol13no1/1301200107.htm [11 February 2010]

22. North Central Regional Educational Laboratory n.d. Glossary

http://www.ncrel.org/sdrs/areas/issues/content/cntareas/science/sc7gloss.htm [14

April 2010]

99

http://www.ncrel.org/sdrs/areas/issues/content/cntareas/science/sc7gloss.htm%20%5B14

http://www.marketresearchterms.com/p.php

http://www.specimencare.com/resource.aspx?IDX=11331

http://www.specimencare.com/resource.aspx?IDX=11331

http://dels-old.nas.edu/ilar_n/ilarjournal/43_4/v4304Johnson.shtml%20%5B11

http://dx.doi.org/10.1108/09544789810214765

http://www.emeraldinsight.com/journals.htm?articleid=841840&show=html

http://www.emeraldinsight.com/journals.htm?articleid=841840&show=html

http://www.ccbcweb.net/archive/pdf/spec20_lab_ex.PDF%20%5B1

http://jds.fass.org/cgi/reprint/66/1/67.pdf%20%20%5B11

23. Ong, M.E., Chan, Y.H., Lim, C.S., 2009, Reducing blood sample haemolysis at a

tertiary hospital emergency department. The American Journal of Medicine Vol122

pp1054.e1-6 [Abstract in Medline database, ref. no. 19854334]

24. Pratt, Paul W., 1983. Laboratory procedures for animal health technicians. Santa

Barbara: American Veterinary Publications, Inc

25. Praxiom Research Group Limited, ISO 9000, 9001 and 9004 Quality Management

Definitions, http://www.praxiom.com/iso-definition.htm [05 October 2010]

26. Puls, Robert, 1989. Mineral levels in Animal Health (Diagnostic Data) –, Second

Edition, Privately published

27. Raphael, Dr. Stanley, Bryant, Hyde, Inwood, Mellor, Spencer, Thomas, 1983. Lynch’s

Medical Laboratory Technology – Fourth Edition, W. B. Saunders Company

28. Research Methods Knowledge Base,

http://www.socialresearchmethods.net/kb/unitanal.php [15th April 2010]

29. Rieser, Tehilla, 1997, How does a spectrophotometer work? Columbia University

Summer Research Program for Science Teachers,

http://www.scienceteacherprogram.org/chemistry/Tehilla97.html [1 May 2010]

30. Ross, J., Chadwick, S., 1999, University of Luton, Faculty of healthcare and social

sciences, Research Home Page

http://www.fortunecity.com/greenfield/grizzly/432/Research.htm [21 April 2010]

31. Ruiz-Marrero, Carmelo, 2009, Biotechnology and Biosafety: A holistic view. Food

First Institute for Food and Development Policy

http://www.foodfirst.org/en/node/2345 [15 August 2010]

32. Saibaba, K. S. S., Vijaya Bhaskar, M, Srinivasa Rao, P. V. L. N., Ramana, G. V.,

Dakshinamurty, K. V., 1998 ,Interferences in Clinical Chemistry Analysis. Indian

Journal of Clinical Sciences, 13(2) 55-62,

http://www.springerlink.com/content/mx4051667207877h/fulltext.pdf?page=1 [10

February 2010]

33. Sayer, Natalie J., Williams, Bruce, 2007, Lean for Dummies, For Dummies

Publications

34. Spencer, L., Rogers, L.C., October 1995, Haemolysis meets QIP – Quality

Improvement Process. Medical Laboratory Observer

http://findarticles.com/p/articles/mi_m3230/is_n10_v27/ai_17477162/ [13 February

2010]

100

http://findarticles.com/p/articles/mi_m3230/is_n10_v27/ai_17477162/

http://www.springerlink.com/content/mx4051667207877h/fulltext.pdf?page=1

http://www.foodfirst.org/en/node/2345%20%5B15

http://www.scienceteacherprogram.org/chemistry/Tehilla97.html%20%5B1

http://www.socialresearchmethods.net/kb/unitanal.php

http://www.praxiom.com/iso-definition.htm

35. Specimencare.com: n.d. Online Poster: Haemolysis: The No. 1 Reason for Sample

http://cms.bd.com/resource.aspx?IDX=4852 [10 February 2010]

36. Snow, John. 2005. Process Mapping for Improved Health Logistics System

Performance. Paper presented at the Global Health Council Conference, Washington,

D.C., 31 May – 3 June 2005

37. Thefreedictionary.com, www.thefreedictionary.com/validation [8 August 2010]

38. The Quality Assurance Project. n.d. Methods and Tools.

http://www.qaproject.org/methods/resimprove.html [5 October 2010]

39. Thomas, Prof, Dr. L, n.d. Haemolysis as influence and interference factor, The

Journal of the International Federation of Clinical Chemistry and Laboratory

Medicine, 13(4) http://www.ifcc.org/ejifcc/; vol13no4/130401002.htm [9 February

2010]

40. Virginia Agricultural and Mechanical College. 17 September 2010. Integrating

statistics and Models across the social science curricula.

http://simon.cs.vt.edu/sosci/converted/measurement/ [17 September 2010]

41. Waters, J. H (MD), Williams, B (BS), Yazer, M. H. (MD), Kameneva, M. V. (PhD).

2007. Modification of suction induced hemolysis during cell salvage. Anaesthesia and

Analgesia IRAS 104(3) pg 684-687

http://www.anesthesia-analgesia.org/content/104/3/684.full [10 April 2010]

42. Watkins, Dr. J. A, 2008. Theses/ Dissertations/ Research Reports: A practical guide

for students to the preparation of written presentations of academic research, Privately

published by author

43. Wikipedia. http://en.wikipedia.org/wiki/Dependent_and_independent_variables [28

September 2010]

44. Wikipedia. http://en.wikipedia.org/wiki/Holism [15 August 2010]

45. Wikipedia. http://en.wikipedia.org/wiki/Sample_(statistics) [15 April 2010]

46. Wikipedia. http://en.wikipedia.org/wiki/Variable [28 September 2010]

47. Your Dictionary Online, http://www.your dictionary.com/cuvette [30 April 2010]

48. Yucel, Dogan and Dalva, Klara. 1992. Effect of in vitro hemolysis on 25 common

biochemical tests. Clinical Chemistry Journal 38(4) pg 575-577.

http://www.clinchem.org/cgi/reprint/38/4/575 [10 February 2010]

101

http://www.clinchem.org/cgi/reprint/38/4/575%20%5B10


http://en.wikipedia.org/wiki/Sample_(statistics)%20%5B15

http://en.wikipedia.org/wiki/Holism%20%5B15

http://en.wikipedia.org/wiki/Dependent_and_independent_variables%20%5B28

http://www.anesthesia-analgesia.org/content/104/3/684.full%20%5B10

http://simon.cs.vt.edu/sosci/converted/measurement/

http://www.ifcc.org/ejifcc/

http://www.qaproject.org/methods/resimprove.html%20%5B5

http://www.thefreedictionary.com/validation%20%5B8

http://cms.bd.com/resource.aspx?IDX=4852

ANNEXURE A: SURVEY TO LABSTAFF

September 2010

Biographical Details (for administrative purposes only)Employer:Race – Tick alongside box:

African Asian Coloured White

Gender Male FemaleStaff Categories Veterinary

ManagementTechnical Laboratory Staff Support Staff Admin

Highest Academic Qualification

Doctorate Post Graduate Degree

Undergraduate Degree

Grade 12 with Diploma/certificate

Grade 12

Lower than Grade 12

Time in years employed at the institution

This survey is anonymous. Please do not write your name on this survey. Responses cannot be traced to any individual. The free and frank expression of you opinion will be most helpful

There are no right or wrong answers to any items in this questionnaire. It is your opinion on each of the statements that matters.

This survey contains a 10 statements related to the Quality of Sample Processes (Operations) in the Biochemistry Section of the Western Cape Provincial Veterinary Laboratory. You are requested to respond to each of the statements by placing a TICK MARK in the space, which most accurately fits the extent to which you agree that the statement is describing.

If you completely agree with this statement, you would tick on the number 7. If on the other hand, you slightly agree with the statement you would tick on the number 3, etc.

Completely Agree

Mostly Agree Slightly Agree Undecided Slightly Disagree

Mostly Disagree

Completely Disagree

7 6 5 4 3 2 1

YOUR OPINION ON SHORTFALLS IN OPERATIONAL SYSTEM IN BIOCHEMISTRY

1There are shortfalls in the operational system with regard to sample inputs that affect the quality of analysis conducted in the system.

7 6 5 4 3 2 1

2There are shortfalls in the operational system with regard to sample inputs, which affect the quality of results issued by the system.

7 6 5 4 3 2 1

3There are shortfalls in the operational system with regard to process inputs that affect the quality of procedures/analysis conducted in the system.

7 6 5 4 3 2 1

4There are shortfalls in the process of validation of sample processing, affecting quality in the operational system. 7 6 5 4 3 2 1

5There are shortfalls in the operational system with regard to process inputs that affect the quality of results issued by the system.

7 6 5 4 3 2 1

YOUR OPINION ON RECOMMENDATIONS TO IMPROVE THE OPERATIONAL SYSTEM IN BIOCHEMISTRY

6By identifying and addressing any factor suspected to be negatively influencing sample condition will result in a Quality Improvement in the operational system.

7 6 5 4 3 2 1

7If QMS focus is placed in terms of improving analysis process in the system, it will result in a Quality Improvement in the section.

7 6 5 4 3 2 1

8If QMS focus is placed in terms of generating policy, education and training for pre-analytical input channels, it will result in a Quality Improvement in the section

7 6 5 4 3 2 1

9The rejection of samples, deemed unsuitable analysis purposes according to credible methods, on the grounds that results on these cannot be validated, will result in a Quality Improvement in the section

7 6 5 4 3 2 1

Use of a colour indicator chart will result in a Quality

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10 Improvement in the Operational System of the Biochemistry Laboratory

7 6 5 4 3 2 1

Ratings:Tube 1 Tube 2 Tube 3 Tube 4 Tube 5

Scale:None No haemolysis apparent+ Slight haemolysis present+ + Mildly Haemolysed+ + + Moderate Haemolysis+ + + + Definite Haemolysis+ + + + + Unacceptable Haemolysis

Dear Participant

Your participation in a brief survey, undertaken for the purposes of Quality Improvement at the WCPVL Stellenbosch, as well as for submission as part of requirements for a B. Tech qualification at Quality Faculty of CPUT, will be greatly valued and appreciated. Your input is requested in order to associate “+” values to blood serum in an attempt determine correlate sample appearance in a colour index to actual spectrophotometric readings taken of them.

Could you kindly please take the time to indicate with number of “+” what you would consider each tube to be rated.

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ANNEXURE B PROTOCOL ANALYSIS

Factors impacting on samples and impacting on sample behaviour in the Biochemistry Research environment

1. Has a process-based system been developed and implemented for the work conducted on samples in Biochemistry?

Y/ N/ N/A

2. Are the adequate Work Instructions in the form of SOP’s (Standard Operating Procedures) that have been developed for all core sample processes (sample registration, preparation and analysis)

Y/ N/ N/A

3. Does this extend to support processes involving samples in the Biochemistry section, such as laboratory cleaning operations?

Y/ N/ N/A

4. Does this extend to support processes involving samples in the Biochemistry section readily available and easily accessible?

Y/ N/ N/A

5. Have the necessary worksheets and forms needed for core sample processing, been developed and are readily available for use?

Y/ N/ N/A

6. Are Work Instructions in the form of SOP’s (Standard Operating Procedures) for all core sample processes (sample registration, preparation and analysis)

Y/ N/ N/A

7. Are adequate records being maintained regarding the work carried out on samples

Y/ N/ N/A

8. Are adequate records being kept regard the support processes involving sample (e.g. temperature readings of refrigerators samples are stored in before processing)

Y/ N/ N/A

9. Sample identification and traceability in the system Y/ N/ N/A

10. Does the system incorporate the use of a back-up system to ensure traceability of samples in the system, such as LIMS (Laboratory Information Management System)

Y/ N/ N/A

11. Are reference standards prepared according to standard operating procedure for each different analysis and used with every batch of samples being analysed

Y/ N/ N/A

12. Are control samples included in every batch of samples as specified by the standard operating procedure?

Y/ N/ N/A

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13. Have calibration and maintenance of equipment being used to process samples, been addressed in SOP’s

Y/ N/ N/A

14. Is calibration and maintenance being performed regularly and is there records/evidence of this.

Y/ N/ N/A

15. Have methods been determined and implemented to verify results of analysis performed on samples, such as interlaboratory testing

Y/ N/ N/A

16. Are samples processes timeously and results send out within preset time constraints?

Y/ N/ N/A

17. If unforeseen circumstances present themselves if there a corrective action procedure in place?

Y/ N/ N/A

18. Has this procedure been documented and are records kept surrounding this

Y/ N/ N/A

Comments

ANNEXURE C: MODIFIED FLOWCHART

Reception: Samples arrive at Lab

Sample information captured

Samples delivered to Biochemistry Section

Sample Reception at BiochemistrySamples information recorded.Biochem lab number assigned.

Test Allocation.Samples stored under ideal

conditions until testing

Veterinarian reviewsCompiles with results from other labs

Issues report

Technologist reviews result

Issues it for release from Biochem

Validation: Checks performed to see if SOP

Followed. Controls and Standards in spec

Sample Analysis:Verifiable analytic method

according to SOP.Controlled conditions

Use of Controls and Standards

Quality Management SystemAll systems in Biochemistry

Critical Suppliers

Record keeping

Satisfied Service Customer

Quality Management Documents and Records

Additional Step:Screening Test

NO: Corrective action involves

redo

YES

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Effect of Haemolysis on Result Quality in Biochemistry Laboratory

Documents

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