PASTEURIZATION OF BEER BY A CONTINUOUS DENSE-PHASE CO2 SYSTEM

By

GILLIAN FOLKES

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2004

Copyright 2004

by

Gillian Folkes

This document is dedicated to beer drinkers everywhere. Enjoy.

ACKNOWLEDGMENTS

I would like to Dr. Charles Sims, Dr. Marty Marshall, Dr. Al Wysocki, Dr. Andre

Khuri, and especially Dr. Murat Balaban, my major advisor. He not only has supported

me with encouragement and challenges, but has also been an exceptional mentor and

friend.

Secondly, I would like to thank my parents who were not only very good at raising

a child, but were also exceptional at raising an adult. They gave me a solid foundation

not only for academics, but also for life and love.

Finally, I would like to thank my husband, Roi Dagan, who knew exactly what to

say, even when he said, “I love you. Now go do some work!” He truly understands what

research is all about.

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TABLE OF CONTENTS Page

ACKNOWLEDGMENTS ................................................................................................. iv

LIST OF TABLES........................................................................................................... viii

LIST OF FIGURES ........................................................................................................... ix

ABSTRACT....................................................................................................................... xi

CHAPTER 1 INTRODUCTION ........................................................................................................1

2 LITERATURE REVIEW .............................................................................................5

Beer Production and Consumption in the United States ..............................................5 Beer Composition .........................................................................................................6 Yeast Cultures in Beer ..................................................................................................7 Beer Quality..................................................................................................................8 Beer Color.....................................................................................................................9 Beer Haze....................................................................................................................10

Composition of Beer Haze ..................................................................................10 Formation of Haze...............................................................................................11 Haze Removal and Beer Stabilization Techniques .............................................13

Beer Foam...................................................................................................................15 Beer Foam Composition......................................................................................15 Beer Foam Formation..........................................................................................16 Beer Foam Stabilization ......................................................................................16

Beer Flavor .................................................................................................................17 Processing of Beer ......................................................................................................18

Pasteurization ......................................................................................................18 Flash pasteurization......................................................................................19 Nonthermal methods ....................................................................................19

Effects of Dense-Phase CO2 Pasteurization ...............................................................20 Microbiology .......................................................................................................20 Theories of Cell Death by Dense-Phase CO2 Pasteurization ..............................23 Effects of Cosolvents...........................................................................................24 Quality Attributes ................................................................................................25

Dense-Phase CO2 Pasteurization of Beer ...................................................................26 Objectives ...................................................................................................................26

v

3 MATERIALS AND METHODS ...............................................................................28

The Dense-Phase CO2 System....................................................................................28 Beer Samples ..............................................................................................................29 Experimental Design ..................................................................................................30

Cleanability Study ...............................................................................................30 Experimental Design ...........................................................................................31 Procedures ...........................................................................................................32

Analysis of Treated Samples ......................................................................................33 Microbial Reduction Experiments.......................................................................33 Haze Measurement ..............................................................................................33 Foam Capacity and Stability Measurements .......................................................33 Polyacrylamide Gel Electrophoresis of Beer Proteins ........................................34 Flavor...................................................................................................................34 Sensory ................................................................................................................36

Fresh beer vs. dense-phase CO2 processed beer...........................................37 Dense-phase CO2 processed beer vs. heat pasteurized beer .........................37 Storage studies..............................................................................................38

Statistical Analysis......................................................................................................38 Conjoint Analysis of Beer Purchase Decision............................................................38

4 RESULTS AND DISCUSSION.................................................................................39

Microbial Reduction Experiments..............................................................................39 Mode of Cell Death ....................................................................................................44 Effect of Dense-phase CO2 Processing on Haze ........................................................46 Effect of Dense-phase CO2 Processing on Foam Capacity and Stability...................48 Effect of Dense-phase CO2 Processing on Beer Aroma and Flavor by Sensory

Evaluation ..............................................................................................................53 Beer Aroma .........................................................................................................53 Beer Flavor ..........................................................................................................56 Beer Aroma After Storage...................................................................................59 Beer Flavor After Storage ...................................................................................61

Effect of Dense-phase CO2 Processing on Beer Aroma and Flavor by Gas Chromatography-Olfactometry and Mass Spectrometry .......................................65

Conjoint Analysis of Beer Purchase Decisions ..........................................................72 5 CONCLUSION...........................................................................................................75

vi

APPENDIX A RAW EXPERIMENT DATA.....................................................................................77

B SENSORY AND CONJOINT BALLOTS.................................................................88

C STATISTICAL MATERIAL .....................................................................................90

LIST OF REFERENCES...................................................................................................92

BIOGRAPHICAL SKETCH .............................................................................................98

vii

LIST OF TABLES

Table page 3-1 Experimental Design ................................................................................................32

4-1 Microbial Reduction Results ....................................................................................42

4-2 Beer Haze in NTU After Processing and After Storage at 1.67° C for 30 Days......47

4-4 Retention Times and Aroma Descriptors for Compounds Detected by Both Assessors on ZB-5 and Carbowax Columns ............................................................65

4-5 Linear Retention Indices and Identification of Compounds using GC-O ...............67

4-6 Average Integration Areas of Identified Compounds in Fresh and CO2 Processed Beer .........................................................................................................69

4-7 Conjoint Analysis Transformation ...........................................................................74

A-1 Yeast Counts for 27 Treatment Combinations Done in Duplicate...........................78

A-2 Foam and Liquid Volumes.......................................................................................87

viii

LIST OF FIGURES

Figure page 2-1 Yeast Growth Stages during Fermentation . ..............................................................8

2-2 Molecular Mechanisms for Haze Formation in Beer ..............................................14

3-1 Schematic of the Continuous Dense-Phase CO2 Pasteurization System..................29

4-1 Scanning Electron Microscopy Picture of Yeast in Fresh Beer ...............................45

4-2 Scanning Electron Microscopy Picture of Yeast in Beer Dense-Phase CO2 Processed at 27.6 MPa, 10% CO2, at 21°C, With a Residence Time of 5 Minutes.....................................................................................................................45

4-3 Scanning Electron Microscopy Picture of Yeast in Beer Pasteurized at 74°C for 30 Seconds................................................................................................................45

4-4 Beer Haze Following Processing and Following Storage at 1.67°C for 30 Days....47

4-5 Foam Capacity and Stability of Beer Samples After Processing .............................50

4-6 Foam Capacity and Stability of Beer Samples After Storage at 1.67°C for 30 Days.....................................................................................................................50

4-7 Aroma Evaluation of Fresh and CO2 Processed Beer Samples................................54

4-8 Evaluation of Aromas of Fresh, CO2 Processed and Heat Pasteurized Beer Samples ....................................................................................................................56

4-9 Evaluation of Beer Flavor Between Fresh and CO2 Processed Samples .................57

4-10 Evaluation of Beer Flavors Between Fresh, CO2 Processed, and Heat Pasteurized Samples .................................................................................................58

4-11 Aroma Evaluation of Aroma Between Aged, CO2 Processed, Heat Pasteurized, and a Non-Stored, Fresh Hidden Control.................................................................60

4-12 Evaluation of Flavor Between Aged, CO2 Processed, Heat Pasteurized, and a Non-Stored, Fresh Hidden Control........................................................................61

ix

4-13 Evaluation of Flavor Between Fresh and CO2 Processed Beer After Storage at 1.67°C for 30 Days, and a Non-Stored, Fresh Hidden Control................................63

4-14 Evaluation of Flavor of Fresh, CO2 Processed, and Heat Pasteurized Samples After 30 Days at 1.67°C, and a Non-Stored, Fresh, Hidden Control .......................64

4-15 Typical FID Chromatogram for Fresh Beer .............................................................70

4-16 Typical Aromagram for Fresh Beer .........................................................................70

A-1 Polyacrylamide Gels ................................................................................................87

B-1 Sample Sensory Ballot .............................................................................................88

B-2 Sample Ballot for Conjoint Analysis .......................................................................89

x

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

PASTEURIZATION OF BEER BY A CONTINUOUS DENSE-PHASE CO2 SYSTEM

By

Gillian Folkes

August 2004

Chair: Murat Balaban Major Department: Food Science and Human Nutrition

The world production of beer grew 26% between 1987 and 1997, despite the

growing market of competing beverages. In 1999-2000, $7.7 billion worth of beer was

produced in the United States.

Bottled beer is currently flash-pasteurized. Because beer is a delicate beverage,

off-flavors are easily formed during pasteurization. With “freshness” being top priority,

it is evident a method of pasteurization using no heat would be of great help to the

brewing industry.

Currently there is great interest in dense-phase CO2 as an alternative processing

method, and studies using the combination of carbon dioxide and pressure for

pasteurization have been successful. Not only is microbial inactivation achieved, but also

no taste or aroma changes are perceived, and vitamin quality is maintained.

The purpose of this investigation was to evaluate the effectiveness of dense-phase

CO2 pasteurization system with beer. A predicted maximum log reduction in yeast

xi

populations of 7.38 logs was seen at 26.5 MPa, 21°C, 9.6% CO2, and 4.77 minutes

residence time. Haze was slightly reduced by dense phase CO2 pasteurization from 146

NTU to 95 NTU. At this same treatment combination, aroma and flavor of beer sample

means were not considered significantly different (p=.3415) from fresh beer sample

means when evaluated in a difference from control test, using fresh beer as the reference.

Foam capacity and stability were affected minimally by CO2 processing, however

changes would most likely be unnoticed by consumers.

In addition, a conjoint analysis was performed to examine motives during beer

purchase decision. The attribute eliciting the larges influence on purchase decision was

lower price, followed by a preference for draft beer taste and an extended shelf life.

Furthermore, indications of the mode of cell death were absorption of CO2 into the

cell membrane creating a physical disruption in membrane structure, visible as divots in

scanning electron microscopy pictures.

A continuous dense-phase CO2 system was effective in the pasteurization of beer.

The ability to produce a clear, consistently fresh beer that forms a good head, with an

extended shelf life is top-priority to brewers. Dense-phase CO2 pasteurization can make

this possible.

xii

CHAPTER 1 INTRODUCTION

Beer dates back to 4000 B.C. when the Babylonians described ale in some of the

world’s oldest writings; however it is believed that beer existed far before these records

(Toussaint-Samat et al., 1991). Egyptians believed that Osiris, the god of agriculture,

made a decoction of barley that had germinated in the Nile river. Becoming distracted he

left it in the sun and forgot it. Upon his return the liquid had fermented. He drank it and

proclaimed mankind should profit from it (Toussaint-Samat, 1992).

The world production of beer grew 26% between 1987 and 1997, despite the

growing market of competing beverages (Gonzalez del Cueto and Miguel, 1999). In

1999-2000, $7.7 billion worth of beer was produced in the United States (Summerour,

2001).

Currently bottled beer is flash-pasteurized. Because beer is a delicate and heat

labile beverage, off-flavors are easily formed during pasteurization. In a study by

Kaneda et al. (1994), non-pasteurized beer was compared to pasteurized bottled beer.

Off-flavors of bottled beers pasteurized at 15-30 pasteurization units (P.U.) had a similar

off-flavor profile to non-pasteurized beers stored at 20ºC for 6-10 days. With “freshness”

being top priority, it is evident a method of pasteurization using no heat would be of great

help to the brewing industry. Its use would lead to a beer with a longer shelf life, cheaper

production and distribution cost because of the elimination of some refrigerated

distribution centers and trucks, and a fresher taste and aroma. Freshness is of utmost

importance in the brewing industry. Current evidence of this is the use of colored glass

1

2

bottles, refrigerated distribution houses and trucks, and the popularization of the “born-

on” date.

One method of non-thermal pasteurization is high hydrostatic pressure processing.

There are a variety of foods such as jams, jellies, guacamole, and pate where high

isostatic pressure technology is applied to inactivate organisms at an efficacy equal to

that of traditional thermal pasteurization methods. The use of high pressure insures no

noticeable changes in taste or flavors of these foods and does not destroy vitamins

(Hoover, 1998).

The combination of high pressure pasteurization with other preservation methods

could lead to a more desirable process or product. The effect of the combination of

pressure, time, and temperature on food has been studied, showing that with the use of

heat and pressure effective pasteurization can take place at low to moderate pressures

(Kalchayanand et al., 1998). Since achieving high pressures can be expensive, the

combined methods insure not only safe food products but also lower costs. However, the

use of heat still may degrade vitamins or change the taste, aroma, or texture of the food.

In addition, high hydrostatic pressure processing is currently a batch process. For the

large volumes that would be processed in the case of beer, a continuous process is more

desirable.

Currently there is great interest in dense-phase CO2 as an alternative processing

method, and studies using the combination of carbon dioxide and pressure for

pasteurization have been successful. Not only is microbial inactivation achieved, but also

no taste, or aroma changes are perceived and vitamin quality is maintained. The addition

of carbon dioxide to the high pressure treatment allows pressures as low as 14 to 107

3

MPa to be used for effective microbial inactivation (Kincal, 2000), instead of 400-900

MPa. These pressures are lower than those in the combined pressure/heat treatment, and

this creates a cost effective alternative pasteurization method because of the small amount

of energy used for pressurization and the use of an inexpensive gas.

The need for inhibition of pathogen/spoilage organism growth and the

characteristic carbonation of beer make it an excellent candidate for dense-phase CO2

pasteurization.

The purpose of this investigation was to evaluate the effectiveness of dense-phase

CO2 pasteurization system for beer. The success of this system will rely on its ability to

inactivate microorganisms, preserve fresh beer taste and aroma, not exacerbate or

possibly prevent beer haze, and insure proper foam formation and stabilization.

Therefore a study examining these characteristics was conducted in order to compare it

with current pasteurization methods and to make inferences regarding the intermolecular

changes occurring in beer during the high pressure carbon dioxide pasteurization.

Although there is promise in the use of dense-phase CO2 pasteurization technology

with beer, research must be done to insure the technical and economic feasibility of the

procedure. The objectives of this study were:

1. To quantify and elucidate mechanisms for inactivation of yeast by dense-phase CO2 pasteurization of beer

2. To prove there are no significant changes in flavor and aroma of beer after dense-phase CO2 pasteurization

3. To prove there are no significant changes in chill haze formation of beer after dense-phase CO2 pasteurization

4. To prove there are no significant changes in foam formation and stability after dense-phase CO2 pasteurization

4

5. To evaluate consumer and industry acceptability and the economic impacts of dense-phase CO2 pasteurization

CHAPTER 2 LITERATURE REVIEW

Beer Production and Consumption in the United States

The brewing industry in the United States is an active part of the national economy,

paying billions of dollars annually in taxes and wages. One sign that beer is a significant

force in today’s economy is that it is included in the basket of goods used to calculate the

Consumer Price Index. Currently the U.S. brewing industry employs approximately 1.66

million workers and pays over $47 billion in wages annually (The Beer Institute, 2003a,

2003b, 2003c).

In 2002, the U.S. brewing industry recorded its seventh straight year of growth.

Currently, there are over 3,500 brands of malt beverages on the market and in the year

2000 it was estimated that 199,650,000 barrels of beer were produced in the United

States. For the same year, consumption of malt beverages reached 21.8 gallons per

capita in the U.S. (The Beer Institute, 2003a, 2003b, 2003c).

Beer consumption is mostly male-dominated, with men accounting for more than

80% of the volume consumed. A large number of these beer drinkers are white and

prefer domestic light beer, followed by domestic draft beer. African American drinkers

make up about 10% of the beer market. In general, they are the biggest consumers of

malt liquors, followed by ice beer. Considering all beer styles, light beer has the

strongest following among women consumers. Women beer drinkers are also more

attracted to specialty micro-brewed beers because of their greater variety (Goldammer,

2000).

5

6

Beer Composition

Beer contains a variety of components, and although many do not regard beer as a

health food, many components of beer are conducive to good health. Beer is mostly

water and is made from malted grains such as barley or wheat, hops, yeast, and

sometimes adjuncts such as corn or rice. Beer contains about 4-5% alcohol by volume,

which if consumed moderately can help reduce the risk of cardiovascular disease

(Baxter, 2000).

On average, a beer’s two main components, excluding water, are carbohydrates (1

to 60 g/liter) and proteins (2 to 6 g/liter) usually in the form of peptides. The

carbohydrates are found in the form of branched dextrans and not as free sugars, which

would have been consumed by the yeast during fermentation. These dextrans not only

have little immediate impact on blood sugar levels as free sugars but also are less

cariogenic. Another health claim of beer is that it contains no fat (Baxter, 2000).

Because beers are made from malted grains they are a good source of B vitamins.

Beer is generally not considered as the main source of B vitamins; however it becomes an

important source of these in malnourished societies. Yeast also contributes to the B

vitamins in beer. For example, about 1 liter (2 pints) of beer may provide one third to

one half of a consumer’s daily requirement of 5 B vitamins, including folate. From the

use of malted barley also comes silicon, which maintains healthy bones, magnesium, and

potassium (Baxter, 2000).

Beverages such as fruit juices and wines have already been recognized for their

readily available antioxidants. Beer contains two distinct sources of antioxidants:

melanoidins and polyphenols. Melanoidins are formed during the roasting of malted

barley from Maillard reactions. Polyphenols come from not only the malt in the form of

7

ferulic acid, but also from hops in the form of catechin. Although antioxidant levels are

comparatively low in beer, their high bioavailability makes beer as competitive as other

foods rich in antioxidants (Baxter, 2000).

Yeast Cultures in Beer

In the production of beer, only 4 ingredients are necessary: water, malt, hops, and

yeast. After germination of the barley to create malt, the grain is ground and water

added. This solution is then boiled to create the wort. It is then cooled and the yeast is

added to start the fermentation. In the brewing operation, yeast functions as the means of

transforming the fermentable sugars in the malt into alcohol, CO2, and heat through

fermentation, as shown in the Gay-Lussac equation:

C6H12O5→2(C2H5OH) + 2(CO2) +heat

There are two types of yeast used in brewing: top and bottom fermenters. Ales are

made from top fermenting yeast, most commonly Sacchromyces cerevisiae, and lager

beers are made from bottom fermenting yeasts such as Sacchromyces carlsbergensis.

Although yeasts are usually referred to as facultative anaerobes, they are actually aerobes.

During fermentation of beer, yeasts switch between oxidative and fermentative

metabolisms, depending on the presence of oxygen; however they cannot grow

anaerobically indefinitely. The cell membrane of all eukaryotes, yeasts included, contain

unsaturated fatty acids and sterols. These compounds can only be produced by the yeast

under aerobic conditions and although these compounds do exist in wort, the amounts are

too low to sustain yeast growth. Therefore, when “pitching” or adding the initial yeast

culture to the wort to start the fermentation, approximately 1 x 107 to 2 x 107 cells per ml

are added. Because the wort is oxygenated prior to fermentation, yeast numbers may

only increase by three cell divisions, or a factor of 8. Subsequent multiplication of the

8

yeast is inhibited because of the lack of further aeration of the wort which would lead to

off flavors in the beer. Furthermore, fermentation includes the lag, exponential growth,

and stationary phases of yeast growth as seen in Figure 1.

Figure 2-1: Yeast Growth Stages during Fermentation (modified from Campbell, 1997).

In kegged or draft beer, because it is not pasteurized after packaging, yeast cultures

are still viable. Without proper refrigeration or pasteurization the shelf life of this beer is

only several hours, because the yeast cultures will continue to ferment the beer and will

create off-flavors in the process. With proper refrigeration or pasteurization this is

prevented. Bottled beer is flash pasteurized to make it a shelf stable product between

71.5 to 74° C and held for 15 to 30 seconds. The pasteurization step kills all yeast that

remain in beer after fermentation and packaging are complete (Goldammer, 2000).

Beer Quality

The importance of quality in the brewing industry is evident in many of today’s

brewing practices. The popularization of the “born-on date” and extensive expenditures

9

on advertising such characteristics as “quality ingredients . . . time honored practices . . .

finest heritage” of certain beers, highlight the industry’s pride in and strive towards

quality.

The visual quality of a beer is the first to be judged by the consumer. The package

of beer, either the draught, bottle, or can, can easily help or hinder the sales of a product.

Recent innovations in the area of packaging have been tamper proof kegs, oxygen

scavenging crowns for bottles, narrowing the necks of cans to conserve metal, the use of

a “widget” which creates better foam in beers of low carbonation, and the use of plastic

bottles for sales of beer in recreational areas where glass bottles may cause danger

(Bamforth, 2000). The next characteristics to be inspected are the beer’s color, clarity,

and foaming capabilities, and then finally aroma and flavor.

Beer Color

Beer color is mostly dependent on the color of the malt after roasting and the other

solid grist materials used to make the beer. The color forming molecules in malt and

grist are primarily melanoidins, which were formed during the roasting of the malt by

Maillard browning reactions. During the malt roast, the more intense the kilning the

darker the malt and the resulting beer. Sugar content of the malt also determines the

amount of browning that will occur, with higher modified grains having more sugars and

darker color after roasting (Bamforth, 2003).

The second source of color in brewing is the possible oxidation of polyphenols

and tannins. These compounds originate in the malt and hops in beer and if large

amounts of oxygen are allowed into the brewing process further darkening of the beer

will occur. This is similar to polyphenol oxidase reactions in apples, potatoes, and

mushrooms (Bamforth, 2003).

10

Beer Haze

Haze can be defined as the formation of a colloidal suspension that scatters light

and makes a beverage appear cloudy. In beer there are two classes of hazes: biological

and non-biological. Biological haze is irreversible and results from an infection of beer

by wild yeasts or bacteria, resulting in spoilage. Non-biological haze is defined as being

either “chill” or “permanent” haze resulting from native constituents of the beer. Chill

haze is haze that forms upon cooling beer to 0°C and redissolves upon warming to 20°C

or more. The term permanent haze should be used for haze which remains in beer at

20°C or above.

Composition of Beer Haze

Haze in beer is formed by interactions between proteins and polyphenolic

compounds. Most proteins that are haze-active contain large amounts of the amino acid

proline and originate from the barley protein hordein (Asano et al., 1982). Hordein is a

proline-rich prolamine, or alcohol soluble protein. Because proline has a cyclized side

chain and can form cis bonds, the frequent inclusion of this amino acid in a protein’s

sequence gives the protein a more elongated, flexible structure. This allows greater

interactions with polyphenolic compounds and in turn greater haze production. On the

contrary, tighter coiled proteins have a significantly lower affinity for polyphenolic

compounds. More specifically, a 19 kDa proline-rich protein has been found by

immunoelectrophoretic analysis by Asano et al. (1982) to have significant positive effects

on beer haze. Further, Hejgaard and Kaergaard (1983) found a 40 kDa beer protein

involved in both foam and beer haze. In 1993, Kano and Kamimura indicated that both

11

20 and 40 kDa protein fractions contributed to foam and colloid stability of beer with the

20 kDa fraction correlating more with haze formation than the 40 kDa protein.

Polyphenols can be classified into 2 groups: haze-active and non-haze active

polyphenols. They are classified by the number of binding sites per molecule, with haze-

active polyphenols having two or more sites per molecule. Multiple sites allow the

polyphenol not only to interact with one protein but also to cross-link with other proteins

to create the colloid that results in haze (Seibert and Lynn, 1998). Characteristics of a

haze-active polyphenol are not only multiple binding sites per molecule, but also the

ability to form multiple hydrogen bonds with proteins through the many phenolic groups

on each molecule. A phenol of approximately 1,000 Da usually has between 12 and 16

phenolic hydroxyl groups. This facilitates the interaction of multiple proteins with the

polyphenol (Haslam, 1998).

The polyphenols in beer that are naturally occurring haze-active compounds are

proanthocyanidins. More specifically the most predominant proanthocyanidin dimers in

beer are procyanidin B3 and prodelphinidin B3 (McMurrough et al., 1992).

Formation of Haze

Much work has been done on the dynamics of the polyphenol/protein interactions.

Although the type of protein and polyphenol involved does effect the haze produced,

when one discusses a fixed system such as a certain beer where the native proteins and

polyphenols do not differ, pH exerts a significant effect on haze produced. For beer the

most haze will be seen at pH 4.0-4.2, with less haze forming at lower and higher pHs.

Haze formed at pH 3.0 was only 1/7 of the amount resulting at pH 4.0 (Siebert et al.,

1996b).

12

The effect of pH directly correlates to what many researchers have noted as the

driving force for protein/polyphenol interactions: hydrophobic effects (Oh et al., 1980;

Siebert et al., 1996b; Haslam, 1998). Hydrogen bond deployment by polyphenols is best

seen as a secondary feature that follows hydrophobically driven associations (Jencks,

1969; Haslam, 1998). The importance of hydrophobic interactions has also been

demonstrated by Oh et al. in 1980 by observing the interactions of tannin and gelatin or

other polyproline proteins. Interactions increased with increases in temperature and ionic

strength as one would expect for hydrophobic interactions.

Seibert and Troukhanova (1996a) developed a model of haze formation in beverage

corresponding to haze-active protein and polyphenol concentrations. At a constant

protein concentration, haze increased to a maximum and then declined as polyphenol

concentration increased. It was hypothesized that this occurred because a haze-active

protein would have a fixed number of sites to which a polyphenol could bind, and a haze-

active polyphenol has two or more sites to bind to a protein. When the concentrations of

proteins and polyphenol binding sites is closest, this results in maximum utilization of

binding sites and cross-linking, resulting in large colloidal particles and maximum light

scattering. However in a matrix such as beer, there is a large excess of haze-active

protein to polyphenols and each polyphenol should be able to find a binding site on each

protein. These protein dimers created would therefore not be cross-linked together and

result in a small colloidal particle size and in turn less haze. However in all instances

Seibert et al. (1996b) affirm that pH, ethanol content, and temperature all affect the extent

of haze formation.

13

Combinations of proteins with polyphenols generally seem to result from three

different mechanisms. As shown in Figure 2, protein polyphenol interactions can occur

as hydrogen bonding between oxygen atoms of peptide bonds and hydroxyl groups of

polyphenols, hydrophobic bonding between hydrophobic amino acids such as proline,

tryptophan, phenylalanine, tyrosine, leucine, isoleucine, and valine and the hydrophobic

ring structure of polyphenols, and ionic nodding between positively charged groups of

proteins and negatively charged hydroxyl groups of polyphenols. However at the acidic

pH of beer, the first two mechanisms would come into effect, while the third would not

because the hydroxyl groups of polyphenols would have no charge and ionic bonding

could not occur (Asano et al., 1982).

Haze Removal and Beer Stabilization Techniques

There are three types of fining agents used in brewing: those that remove high

molecular weight polyphenols to reduce chill-haze, those that remove high molecular

weight proteins to reduce chill-haze, and those that reduce yeast biomass but do not affect

chill-haze.

The most common polyphenol agent used is polyvinylpolypyrrolidine or PVPP.

Many brewers prefer using a polyphenol scavenger because it does not remove protein,

which could lead to a reduction of foamability. Users of PVPP also benefit from the fact

that because complex polyphenols are being removed, as the beer ages and simple

phenolic compounds complex to form polyphenols, the amount of polyphenol present in

the aged product will remain low. The amount of polyphenol is directly proportional to

the amount of haze present. Therefore even aged beers will have less haze and retain

foam formation if PVPP is used (Fix, 1999).

14

Figure 2-2: Molecular Mechanisms for Haze Formation in Beer (Asano et al., 1982)

Although foam-forming ability can be disrupted by some fining agents, silica gels

are the most widely used protein scavengers because they are highly specific for haze-

active proteins. Silica gels work by complexing with haze-active proteins as a

polyphenol would, therefore disrupting the formation of a haze (Fix, 1999).

Yeast-active agents are less used and will have no effect on chill haze. The most

common yeast-active agent is Isinglass, which comes from the swim bladder of tropical

fish (Fix, 1999).

15

Beer Foam

Foam can be defined as a thermodynamically unstable colloidal system in which

gas is momentarily entrapped in a liquid matrix. Foams can be made in two ways: by

supersaturation or mechanically. When a gas is dissolved in a liquid under pressure, such

as in a carbonated beverage, and pressure is released the gas becomes supersaturated and

gas bubbles form creating a foam. These bubbles do not form spontaneously, but instead

form from air pockets that are already present on the side of the container. Another

example of supersaturation foam formation is the formation of the foam structure in

bread where CO2 collects in air pockets in the dough. Foams that are formed

mechanically could be formed by sparging, beating, or shearing. This would be the case

in whipped products like meringues (Walstra, 1996).

Beer Foam Composition

Beer foam is created when supersaturated CO2 is released and forms gas bubbles in

the continuous liquid matrix. This matrix contains amphiphilic proteins in the beer,

which migrate to the surface of the air/liquid interface. This migration is motivated by

the decrease in the proteins’ free energy as the protein migrates out of solution to the

interface. The result of this migration is that interfacial tension is lowered, which makes

it more favorable for the two thermodynamically incompatible phases (air and liquid) to

co-exist. The air/liquid interface is stabilized because the proteins have formed a strong

viscoelastic film.

Although all proteins are amphiphilic, they can differ greatly in the their surface

activity, making some proteins better emulsifiers/stabilizers than others. Two

characteristics that govern a protein’s ability to act as a surface-active protein are the

16

properties and topology of the protein’s surface and its conformational stability,

flexibility, and adaptability (Damodaran, 1996).

Although an increasing amount of hydrophobic amino acids does mean a protein

could be more surface active, it is the distribution of these amino acids that is the most

important. A protein with clumps of hydrophobic groups will be a better foam stabilizer

than one with randomly dispersed hydrophobic groups. Also, a protein that is in a molten

globule state, or can more easily unfold at the air/liquid surface will be more surface-

active and therefore a better foam former/stabilizer (Damodaran, 1996).

Beer Foam Formation

For surface-active proteins, there is a sequence of events that leads to formation

and stabilization of the foam. First the protein has to have the ability to rapidly diffuse

and absorb to the interface. Secondly, the protein should be able to rapidly unfold and

reorient its polypeptide segments at the interface, and thirdly, while the protein is at the

interface it should be able to interact with neighboring proteins or molecules to form a

strong, continuously cohesive viscoelastic film, able to tolerate mechanical or thermal

forces. The first two steps above are critical for the formation of a foam and the third is

imperative for foam stability.

Beer Foam Stabilization

Foam stability is dependent on the presence of amphipatic polypeptides from

malt, alpha-acids from hops, and the absence of lipophilic materials. Brewers insure

good foam stability by the addition of propylene glycol alginate and the use of nitrogen

gas. Nitrogen works by providing bubbles of very small diameter causing a higher

concentration of small bubbles, which results in a more stable foam. Foam formation and

stability may also affect the flavor and aroma of beer (Bamforth, 2000).

17

Beer Flavor

Because beer is a delicate and heat labile beverage, off-flavors are easily formed

during pasteurization. In a study by Kaneda et al. (1994), non-pasteurized beer was

compared to pasteurized bottled beer. Off-flavors, volatile aldehydes of bottled beers

pasteurized at 15-30 pasteurization units (P.U.) had a similar off-flavor profile to non-

pasteurized beers stored at 20ºC for 6-10 days. With “freshness” being top priority to

brewers, it is evident a method of pasteurization using no heat would be of great benefit

to the brewing industry.

When evaluating a new non-thermal pasteurization method, the effect on beer

flavor would need to be evaluated as well. Although, much flavor analysis in the

brewing industry is done by panelists, analysis of flavors by instrumentation is also

noteworthy. One technique that has become increasingly popular is solid phase

microextraction (SPME). This method uses a small volume of sorbent dispersed typically

on the surface of a fiber to isolate and concentrate analytes from a sample matrix. After

either exposure to the headspace or wicking into a liquid sample matrix, analytes will

absorb to the fiber until an equilibrium is reached. Analytes are then thermally desorbed

into an analytical instrument for separation and quantification. It is a solvent-free, non-

destructive, and simple preparation technique (Pawliszyn, 2001).

SPME can be used in conjunction with gas chromatography-olfactometry (GC-O)

to determine what flavor compounds may be of interest in a particular sample, or the

importance of specific compounds in the overall perception of a food’s flavor or aroma.

In traditional gas chromatography, a mixture of volatile compounds partition between the

gas and stationary phases and travel at different velocities, resulting in different residence

times within the column. Upon eluting compounds can be characterized using various

18

detectors (Kamimura et al., 2003). In GC-O, the separation capabilities of gas

chromatography are coupled with the sensitivity and precision of the human nose. This

method of flavor analysis detects compounds that have an influence on flavor or aroma of

the food (Naim et al., 1998). GC-O produces an aromagram which indicates an olfactory

impression as a function of retention time plus an intensity descriptor (Kamimura et al.,

2003). GC-O has been used with success to describe hop aromas, off-flavors, and aged

beer flavor compounds, using both immersion and headspace SPME sampling

(Kamimura et al., 2003).

Processing of Beer

Pasteurization

Most beer, packaged in bottles or cans, is pasteurized after filling into containers by

passing through a steam tunnel. During this process, the bottles are passed under a series

of water sprays with the temperature of the water increasing as beer passes through the

tunnel. After reaching the desired temperature, bottles are cooled by a water spray and

then exit the tunnel to air dry. The heat treatment used is referred to in terms of

pasteurization units, with 1 PU = 60° C for 1 minute, with 5 PU resulting in a sufficient

kill of approximately 102/ml yeast cells that would remain in pre-filtered beer. The

steam tunnel used for bottles or cans can be operated at various temperatures, usually

60°C or 62°C for a longer time, depending on operating needs. Up to 30 PU may be

applied depending on pre-pasteurization procedures such as filtering. Because heat

treatment could adversely affect flavor, the choice of pre-pasteurization treatments and

PU value is always a compromise between extending shelf life and beer quality

(Campbell, 1997).

19

Flash pasteurization

Although flash pasteurization is not common in North American breweries, it is

very popular in Europe and Asia. During flash pasteurization, beer is heated to at least

71.5 to 74° C and held for 15 to 30 seconds, resulting in 13.26 PU. This is achieved by

the use of a two- or three-stage plate heat exchanger with hot water as the heat source.

The adjustment of the flow rate determines the PU value for the treatment. After flash

pasteurization, beer is then aseptically packaged into sterilized bottles or cans.

Nonthermal methods

The most common non-thermal processing technique used by brewers to extend

shelf life is sterile filtration. Sterile filtration has been used as an alternative to

pasteurization for many years. It has the advantage over pasteurization in that the risk of

flavor damage by heat is eliminated.

The term "sterile filtration" refers to the reduction of yeast and bacteria to levels

that do not result in spoilage of the beer over its planned shelf life by use of one or many

filters before packaging the beer in sterile containers. This can be accomplished without

the loss of color or flavor compounds (Goldammer, 2000).

The brewer sets a specification for the maximum allowable concentration of yeast

and bacteria for quality control purposes, which may have entered the brewing process

inadvertently, in sterile-filtered beer since not all microorganisms will be removed during

the process. There are no critical levels for allowable microorganisms, and therefore

extensive monitoring of the process must occur. The process is also labor intensive

because of the time needed to clean the filters to prevent fouling (Goldammer, 2000).

20

Effects of Dense-Phase CO2 Pasteurization

Microbiology

Recently there has been great interest in inactivation of microorganisms in food or

model food systems using high pressure carbon dioxide. This new technology has been

shown to inactivate microorganisms as well as conventional heat pasteurization without

the loss of nutrients or quality changes.

The use of carbonation as a means of preserving food started as early as 1939

with the study by Brown et al.(1939) where apple cider was carbonated and microbial

inactivation and flavor changes were recorded. The carbonation of the juice was shown

to preserve the cider for up to 3 months at approximately 21oC with no change in flavor.

The use of carbonation was also investigated for its use in soft drinks as a preservation

agent. Even at the lowest amount of gas pressure (3 volumes of CO2 where 1 volume=1

L of CO2 per L of beer) sterility was achieved on approximately the 20th day depending

on the Brix of the beverage (Insalata, 1952). Researchers have also evaluated the use of

pressurized CO2 and decompression to reduce microbial loads. In 1951, Fraser showed

that 99% of E. coli numbers were rendered non-viable by a decompression of CO2 from

500 psi to atmospheric pressure.

Although carbonation with CO2 has been shown as an affective preservative some

bacteria are not affected. Molin (1983) examined the growth inhibiting effect of carbon

dioxide on a variety of food related bacteria by sparging spiked growth media with the

gas, arriving at a variety of pressures. Only partial success was attained. Although 100%

carbon dioxide did slow the growth of all organisms some were affected less than others.

CO2 had approximately 75% inhibitory affect on Bacillus cereus, Brochothrix

thermosphacta, and Aeromonas hydrophila, and a 53%-29% inhibitory effect on

21

Escherichia coli and Streptococcus faecalis. Inhibitory rates for anaerobic bacteria were

even lower. This discovery proved that carbonation of foods alone would not inactivate

all food-related bacteria.

Because of the need for a preservation method that is safe, inexpensive, and non-

destructive to heat sensitive compounds, the use of supercritical carbon dioxide (SC-CO2)

was tested as a food preservation method on cells in cultures or broths. SC-CO2 was

chosen because of its safety, cost, and high purity. CO2 also has a low critical pressure

and critical temperature which result in excellent solvent power of CO2 when used in

HPCD pasteurization. Kamihira et al.(1987) used HPCD to sterilize cultures of baker’s

yeast, Escherichia coli, and Staphylococcus aureus, however they were only successful

with cultures with moisture contents from 70-90%. This same success was echoed in

1991 and 1992 when Saccharomycces cerevisiae was inactivated using sub and

supercritical CO2 and both cell inactivation and disintegration were studied. (Lin et al.,

1991; Lin et al., 1992a, 1992b; Nakamura et al., 1994) A similar study was performed

again using baker’s yeast and Bacillus magetarium in a spore form (Enomoto et al.,

1997a, 1997b) . However in all studies, in striving for not only the inactivation of cells

but also their disintegration, the vessel must undergo pressurization and depressurization,

in some cases, several times creating an energy-intensive process. However, this is only

true in batch systems. In a continuous system depressurization would happen naturally.

Similar studies have been performed using Leuconostoc (Lin et al., 1993) and

Kluyveromyces fragilis, Saccromyces cerevisia, and Candida utilis (Isenschmid et al.,

1995) showing that inactivation can occur without disruption of the cell wall, but instead

through the leaching of cellular contents by the CO2. The effect on HPCD on bacterial

22

spores has also been examined with the most successful inactivation occurring in the

subcritical region of CO2 (Enomoto et al., 1997a).

It has been shown that the amount of inactivation is proportional to the amount of

dissolved CO2 in the sample. Kumagai et al. (1997) measured dissolved CO2

gravimetrically. Results showed higher inactivation of Saccharomyces cerevisiae as CO2

levels increased. Shimoda et al. (2001) found similar results and were able to model

death kinetics of Saccharomyces cerevisiae as first-order through the critical temperature

and pressure of CO2. Additionally, higher water activities and higher pressures have also

shown higher inactivation because of their effects on CO2 sorption of the yeast cells

(Kumagai et al., 1997). To further enhance this effect not only have higher pressures

been used but also modifications to the necessary machinery have taken place. In studies

by Shimoda et al. (1998) and Ishikawa et al. (1995, 1997), a filter was placed in the

process vessel to create microbubbles of CO2 entering the vessel. It was shown that these

microbubbles were more effective in inactivating bacterial cultures than the process

without the filter. Another innovation has been the development of a semi-continuous

system for dense-phase CO2 pasteurization. One such system was shown to be more

efficient on the inactivation of Saccharomyces cerevisiae (Spilimbergo et al., 2003b).

Conditions such as temperature and pressure have also shown significant effects

on the antimicrobial properties of CO2. Studies on E. coli in Ringers solutions showed a

decrease in survival rate with increases in dissolved CO2, increases in pressure1.2-5 MPa,

and increases in temperature from 25°-45°C (Ballestra et al., 1996). Studies on Listeria

monocytogenes also showed similar results in reference to pressure and temperature

23

variations with a complete inactivation at 6.08 MPa CO2 treatment at 115, 75, and 60

minutes at 25, 35, and 45° C, respectively (Erkman, 2000a, 2000b).

Recently most studies have concentrated on the inactivation of microorganisms in

food samples rather than cultures or broths. Studies by Wei et al.(1991) showed the

effectiveness of using CO2 on cultures of Listeria or Salmonella suspended in water and

spiked onto food samples. The study showed the treatment to be applicable in some food

systems: chicken, shrimp, orange juice, and egg yolk, but ineffective in a whole egg-

salmonella mix, proving this technology may be dependent on the foods characteristics

and their ability to shelter bacteria from of carbon dioxide. Staphylococcus aureus

suspended in broth and compared to a suspension in raw milk was shown to undergo

inactivation at lower pressures and in a shorter length of time. Also compared was raw

milk was compared to orange, peach and carrot juices and the milk showed protection of

the bacteria (Erkman, 1997, 2000c).

Kimchi has also been studied because of its unique properties as a fermented food

which may spoil if fermentation is not stopped at the appropriate time. In two different

studies by Hong et al. (1997, 1999), Lactobacillus was shown to undergo inactivation

after 200 min at 6.86 MPa CO2 pressure in kimchi compared to 30 min at approximately

13.73 MPa in the broth suspension. This affirms the point that the success of HPCD

should be evaluated on a case-by-case basis for different foods.

Theories of Cell Death by Dense-Phase CO2 Pasteurization

Researchers have reported that the inactivation rate of all microorganisms is

sensitive to pressure, temperature, and exposure time to CO2. Furthermore Hong et al.

(1999) found similar results, but also concluded microbial inactivation was mainly due to

24

the transfer rate of CO2 into the cells which could lead to viability loss. To further

elucidate the mode of cell death by dense-phase CO2 pasteurization, much research has

been performed. Using scanning electron micrographs, Ballestra et al. (1996) noted cell

deformation of Escherichia coli after processing at 35°C and 5MPa of CO2 and

Spilimbergo et al. (2003a) noted the same results on gram-positive and gram-negative

bacteria. Shimoda et al. (2001) studied the effect of the concentration of CO2 on

Saccharomyces cerevisiae and found that cell death during continuous versus batch

treatments was due to the “anesthesia effect” of CO2. This effect can be defined as loss

of cell viability because of the diffusion of molecular CO2 into the plasma membrane of

the cell, compromising the construction of membrane domains (Isenschmid et al., 1995).

More specifically, while coining the term “anesthesia effect”, Isenschmid et al. (1995)

found that this effect occurred at temperatures higher than 18°C. At these higher

temperatures, dependency was also noted on the dissolved CO2 concentration, with

increases in temperature and dissolved CO2 concentration resulting in increased cell

death. However, below 18°C a solvent effect was observed as the reason for viability

loss in yeast cells (Isenschmid et al., 1995).

Effects of Cosolvents

Because the solvent effect is suspected as a mode of cell death during dense-phase

CO2 processing, one must also examine the effects of co-solvents such as ethanol that

may be present in the food matrix. Solvent characteristics of CO2 can be greatly

modified by the addition or existence of certain cosolvents. CO2 is a non-polar solvent,

and has limited affinity for polar solutes. It is often used to extract organic solute

molecules. A polar modifier can be added to CO2 to improve the solubility and

25

selectivity for polar molecules. Most often the cosolvents of choice are lower alcohols.

Cosolvents are usually added in 5-10% amounts by volume and even in these small

amounts, can have significant effects, especially in surface processes. For example, the

addition of ethanol may have significant effects on extraction by allowing increased

absorption on surface sites, preventing the re-adsorption of the compound of interest

(Clifford and Williams, 2000).

The addition of a cosolvent modifies the critical temperature and pressure of the

original fluid (Taylor, 1996). Therefore the solubility of the materials of low volatility is

enhanced and as a result, lower pressures can be used to achieve the same extraction yield

(Brunner and Peter, 1982). This has been shown to increase the amount of oil extracted

from sunflower seeds (Raghuram Rao et al., 1992), and rape seed, soy bean, cottonseed,

flax seed, and peanuts, and to enhance the extraction of herbal components such as

borage seed oil and hiprose fruit (Illes et al., 1994).

Quality Attributes

Although the use of HPCD as a pasteurization technique for foods has been well

studied, its effect on food’s quality characteristics needs more examination. During the

injection of CO2 the pH of the foodstuff drops dramatically. Because pH plays such a

central role in regulation of food systems, it is imperative to be able to predict what will

happen to quality attributes of food after HPCD processing. Models for pH of food

systems processed with HPCD have been developed to help forecast pH extremes which

may occur during processing (Meyssami et al., 1992). Specific research has also been

done with single strength orange juice to examine HPCD effect on enzymes, cloud, color,

and Brix value, and total acidity. It was shown that although significant pH changes can

occur during processing, the final pH of the product after de-pressurization was not

26

significantly changed, cloud was enhanced, enzymes were inactivated, and flavor and

aroma were unaffected (Arreola et al., 1991). Commercially, the addition of CO2 has

also been used to preserve cottage cheese with no effect on the mouthfeel or flavor of the

product (Mermelstein, 1997). Flavor changes and protein viability of model systems

have also been examined after rupture of yeast cells (Lin et al., 1991).

Dense-Phase CO2 Pasteurization of Beer

The need for inhibition of pathogen/spoilage organism growth and the

characteristic carbonation of beer lend it to be an excellent candidate for HPCD

pasteurization. Freshness is of utmost importance in the brewing industry. Current

evidence of this is the use of colored glass bottles, refrigerated distribution houses and

trucks, and the popularization of the “born-on” date.

Although there is promise in the use of HPCD pasteurization technology with

beer, research must be done to insure the technical and economic feasibility of the

procedure. Parameters that must be optimized are the process pressure, amount of CO2

to add, process temperature, and residence time of the product under pressure. These

parameters will be evaluated first on the basis of microbial log reduction and then on

their effects of the quality attributes of taste, aroma, foam formation and stability, and

haze. Therefore, a study examining these characteristics will be conducted in order to

compare the process with current pasteurization methods and to make inferences

regarding the intermolecular changes occurring during the high pressure carbon dioxide

pasteurization.

Objectives

1. To quantify and elucidate mechanisms of inactivation of yeast by dense-phase CO2 pasteurization of beer as a function of process pressure, temperature, residence time, and CO2 percentage

27

2. To prove there are no significant changes in flavor and aroma of beer after dense-phase CO2 pasteurization

3. To prove there are no significant changes in chill haze formation of beer after dense-phase CO2 pasteurization

4. To prove there are no significant changes in foam formation and stability after dense-phase CO2 pasteurization

5. To evaluate consumer and industry acceptability of dense-phase CO2 pasteurization

CHAPTER 3 MATERIALS AND METHODS

The Dense-Phase CO2 System

The continuous dense-phase CO2 system was constructed by APV (Chicago, IL)

for Praxair (Chicago, IL) and given to the University of Florida (Gainesville, FL). The

system is housed in the pilot plant of the Food Science and Human Nutrition Department

at the University of Florida. The system mixes cooled, pressurized liquid CO2 with a

liquid feed pressurized by its own pump (Figure 3-1). The mixture then proceeds through

a holding tube (79.2 m, 0.635 cm ID) for a specified residence time, which is modified by

changing the flow rate of the mixture. In the holding tube, temperature can be controlled

by electrical heating tape, insulation, and a controller system, and operating pressure is

maintained. When exiting the holding tube the mixture is depressurized by passing

through a back pressure valve and is then ready for collection.

The machine works by pressurizing the liquid feed first, to 6.89 MPa using a

reciprocating pump with a stroke length of 30 mm and a back pressure valve and then to

the desired operating pressure using a second reciprocating pump and another back

pressure valve. CO2 is then introduced by a reciprocating pump at approximately 6.2

MPa or higher. Pressurizing both the liquid feed and the CO2 insures both will remain

liquid and mix with the desired proportions. The CO2/feed mixture then passes through

the second reciprocating pump that maintains the operating pressure which is always 6.89

28

29

MPa or more. This pressure is maintained throughout the holding tube. The mixture

then exits the holding tube, is depressurized, and collected in sterile bottles as aseptically

as possible, using alcohol to sterilize the end of the holding tube.

P

Mai

n Pu

mp

Juice stream

Vacu

um

Heating system

Hold tube

Treated

CO2

juice

CO

ta

nk2

Expansion valve

Pump

Pump

Chiller

4

1

2

3

5

6

78

9

Figure 3-1: Schematic of the Continuous Dense-Phase CO2 Pasteurization System

Pressure, temperature of the holding tube, flow rate (which in turn controls

residence time in the holding tube), and weight % of CO2 were all controllable

independent variables. Thermocouples and pressure sensors were also located

throughout the machine to monitor operating parameters.

Beer Samples

Fresh beer samples (less than one month old) were purchased from Market Street

Pub in Gainesville, FL. The beer was purchased in 58.7 L (15.5 gallon) kegs and

transported to the pilot plant in the Food Science and Human Nutrition Department at the

University of Florida and stored at 1.67°C until used. The beer is an ale brewed using an

30

all-barley malt extract and whole hops which insured a high level of consistency from

batch to batch. The clarity of the beer had not been improved by the use of brewing aids.

Experimental Design

Cleanability Study

Experiments were conducted to evaluate the cleanability of the dense-phase CO2

system. Principal and Oxonia (27.5% hydrogen peroxide, 5.8% peroxy acetic acid,

66.7% inert ingredients) (Ecolab, St. Paul, MN) were used as sanitizing agents. A non-

pathogenic spoilage organism Lactobacillus fermentum (ATCC, Manassas, VA) was used

in cleanability experiments because of its frequent use as a test organism for thermal

inactivation studies. A freeze dried culture of Lactobacillus fermentum was rehydrated in

25 mls Lactobacillus MRS broth (Difco Laboratories, Sparks MD) and incubated at 37°C

for 24 hours. Butterfield’s phosphate buffer was used to dilute this culture to 106

CFU/ml.

The machine was sanitized by pumping Principal at 50 ml per 18.9 L hot tap water,

followed by 18.9 L cold tap water rinse and then followed by Oxonia at 946 ml per18.9 L

cold water. Some Oxonia solution was left in the system overnight. The next day, 6 L of

sterile water was used to rinse the machine after the remaining Oxonia had been pumped

through. Then 6 L of the cell suspension was pumped through the sanitized machine,

collected, and plated to measure recovery of the microorganisms. The machine was then

rinsed with water and re-sanitized. The machine was then allowed to sit for 2 hours.

Then 6 L sterile water followed by 6 L of sterile Butterfield’s phosphate buffer were run

and collected into sterile bottles. The collected buffer was filtered using a vacuum pump

and .45 micron mixed cellulose water testing filters (Fischer Scientific, Pittsburg, PA).

1.2 L of buffer was filtered per filter and all filters were plated on Lactobacillus MRS

31

agar (Difco Laboratories a subsidiary of Becton, Dickinson, and Company, Sparks MD).

Plates were incubated overnight at 37°C.

The recovered cell suspension plates showed that 105/ml lactobacilli were

recovered after pumping the suspension through the machine, i.e. approximately 900,000

CFU remained in the machine after the cell suspension exited. After the sanitizing

procedure was completed, collection of buffer pumped through the system resulted in no

CFUs on the plates for the filters. Results show that the Principal and Oxonia solutions

were effective in cleaning the machine without pressure.

Experimental Design

A Central Composite Design (CCD) was used because it is an economical design,

allowing a researcher to fit a second order prediction equation to a response surface. The

independent variables were pressure, CO2 %, residence time, and temperature. Twenty-

seven treatment combinations were selected by CCD. The aim was to establish an

optimum set of operating conditions based on the dependent variable of yeast population

reduction.

More experiments were conducted after this one on a subset of the 27 points to

evaluate the effect on the quality attributes of haze, foam, and aroma/flavor changes. The

subset was selected from the 27 points by choosing the most effective combination

treatment for log reduction of yeast, a more economical version of the previous, and

adding a heated beer sample (74°C, 30 seconds), and an untreated, fresh beer control.

The experimental design is shown in Table 3-1.

32

Table 3-1: Experimental Design Pressure Temp CO2 Time Pressure (MPa)

0.000 -1.414 0.000 0.000 -1=20.7-1.000 -1.000 -1.000 -1.000 0=27.61.000 -1.000 -1.000 -1.000 1=34.5

-1.000 -1.000 -1.000 1.000 1.414214=37.31.000 -1.000 -1.000 1.000 -1.414214=17.8

-1.000 -1.000 1.000 -1.0001.000 -1.000 1.000 -1.000 Co2 (%)

-1.000 -1.000 1.000 1.000 -1=81.000 -1.000 1.000 1.000 0=100.000 0.000 -1.414 0.000 1=120.000 0.000 0.000 -1.414 1.414214=12.83

-1.414 0.000 0.000 0.000 -1.414214=7.170.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 Temp (degrees C)0.000 0.000 0.000 0.000 1-=251.414 0.000 0.000 0.000 0=350.000 0.000 0.000 1.414 1=450.000 0.000 1.414 0.000 1.414214=49

-1.000 1.000 -1.000 -1.000 -1.414214=211.000 1.000 -1.000 -1.000

-1.000 1.000 -1.000 1.000 Residence1.000 1.000 -1.000 1.000 Time (minutes)

-1.000 1.000 1.000 -1.000 -1=41.000 1.000 1.000 -1.000 0=5

-1.000 1.000 1.000 1.000 1=61.000 1.000 1.000 1.000 1.414214=6.410.000 1.414 0.000 0.000 -1.414214=3.56

Procedures

For every experiment, the dense-phase CO2 system was sanitized one day prior to

experiments by the procedure described in the cleanability experiment. Then the

machine was rinsed with 6 L of sterile de-ionized water. As the feed tank emptied, beer

was added and allowed to run in the machine without pressure for at least 2 hold-up

volumes (5 L) to insure all water had exited the machine. Operating parameters were set

for each treatment and 2 hold-up volumes (approximately 5 L) of beer were allowed to

pass to insure steady state conditions before sample collection. Samples were collected

in sterile bottles. The 27 treatment combinations were run in duplicate on separate days,

using different batches of beer.

33

For heat pasteurized beer, fresh beer was pumped by a peristaltic pump (Cole

Parmer, Chicago, IL) into a temperature controlled water bath (Hart Scientific, American

Fork, UT) through copper tubing. The beer was pasteurized at 74° C for 30 seconds and

collected in a sterile bottle. Copper tubing was sanitized using pumping soapy hot water,

then hot water, then alcohol through the system prior to use.

Analysis of Treated Samples

Microbial Reduction Experiments

All beer samples were plated on potato dextrose agar for enumeration of yeast

colonies. Beer was diluted serially using 90 ml dilution bottles containing Butterfield’s

phosphate buffer (Hardy Diagnostics, Santa Maria, CA). Dilutions were done in

duplicate and plated in duplicate. Plates were incubated at 37°C for 5 days.

Haze Measurement

Beer samples were evaluated for haze using a Hach turbidimeter (Hach, Loveland,

CO) and the AOAC procedure for beer haze measurement (AOAC 10.013). Readings

were recorded as NTU, nephelometric turbidity units.

Foam Capacity and Stability Measurements

Beer samples (25 ml) were degassed at room temperature for 24 hours by using a

stir plate and stir bar, and placed in graduated cylinders with their tops cut off to facilitate

the addition and removal of the homogenizer blade. A Virtus 45 homogenizer (The

Virtus Company, Gardiner, New York) was used to mechanically foam the beer at setting

20 for 60 seconds, keeping the blade at an equal depth for all beer samples. At the end of

the 60 seconds the homogenizer was turned off and the volume of foam produced in ml

was recorded, as an indication of foam capacity. Sixty seconds after that, the amount of

34

beer liquid collected in the bottom of the cylinders was recorded as well, as an indication

of foam stability (Odabasi, 2003).

Polyacrylamide Gel Electrophoresis of Beer Proteins

Polyacrylamide gel electrophoresis (PAGE) was run on beer proteins to determine

if beer proteins were effected by dense-phase CO2 pasteurization. Beer samples (1ml)

were centrifuged at 1.6°C at 10,000g for 15 minutes in 30 kDa Millipore Centricon

Centrifugal Filter Devices (Millipore, Bedford, MA) to concentrate beer proteins. The

retentate was collected and mixed 1:1 with PAGE sample buffer containing dye (Biorad,

Hercules, CA) and kept on ice. 30 uml of each sample was then loaded onto a pre-cast

polyacrylamide gel (Biorad, Hercules, CA) and the gels were run at 100 V until dye

bands had reached the end of the gel. A molecular weight marker was also loaded on

each gel as a known standard (SigmaMarker, Sigma Aldrich, St. Louis, MO). Samples

were run in duplicate on 15% and 18% polyacrylamide gels. Differences in proteins

were evaluated by Rf values (mm traveled by band/mm traveled by dye front).

Flavor

Gas chromatography-olfactometry was performed on beer samples using solid

phase microextraction (SPME) as the sample preparation method. The fiber used was

100um polydimethylsiloxane coating SPME fiber assembly (Supelco, Bellefonte, PA).

Flavor profiles of fresh and processed beer were created by headspace SPME sampling.

Fiber exposure time was optimized prior to sampling. Aliquots (7ml) of fresh beer

were poured into 40 ml vials and each sealed with caps containing Teflon-coated septa.

Volatiles were subsequently extracted using a pre-conditioned 100 µm PDMS fiber

(Supelco, Bellafonte, PA) under different extraction conditions, which varied in time (5,

35

10, 15 min) and temperature of fiber exposure (30 and 40°C). Equilibrium was reached

at 40°C for 15 minutes. Chromatographic data is available by contacting Dr. Murat

Balaban at the Food Science and Human Nutrition Department, University of Florida.

For sampling, the whole coated fiber was exposed to the headspace of the samples

and after the extraction conditions were completed, the fiber was removed from the

headspace and immediately inserted into a GC-splitless injector, where aroma

compounds were allowed to be desorbed for 2 min.

Volatile components were separated in a HP-5890 GC (Palo Alto, CA) equipped

with a sniffing port (DATU, Geneva, NY), a flame ionization detector (FID), and a ZB-5

column (30 m x 0.32 mm i.d. x 0.5 mm film thickness) from J&W Scientific (Folsom,

CA) and in the same model GC with a Carbowax column 30 m x 0.32 mm i.d x 0.5 um

film thickness). For both columns the initial oven temperature was 40°C which was then

increased at 20°C/min to a temperature of 120°C. The temperature was then increased at

5°C/min to a temperature of 160°C, and then increased to 240°C at 15°C/min and held at

this final condition for 5 min. Injection and detection port temperatures were 250 and

250°C, respectively. A 0.2 µL aliquot of alkane standard solution was also injected in the

splitless mode. A GC splitter split the column effluent between the FID and the

olfactometer in a 1:2 ratio, respectively. Two trained assessors (training based on aroma

active compounds present in beer) were employed to evaluate each treatment in

duplicate. Each assessor was asked to describe each odor detected in the GC-O effluent

and to indicate the aroma intensity continuously during the chromatographic run using a

linear potentiometer. This device has a pointer that can be moved across a 10-cm span to

indicate aroma intensity. Aroma descriptors along with their respective retention times

36

were recorded manually, and later transcribed into the chromatographic software for

inclusion with the olfactometry time-intensity data. Time-intensity aromagrams were

obtained for each treatment. Aroma-active compounds were defined as only those

compounds producing an intensity response at the same retention time and similar

descriptor from at least half of the panel responses. Mean aroma intensities of each

aroma-active compound were calculated by averaging the peak height among each

chromatographic run. Chromatograms were recorded, compounds tentatively identified

by comparing LRI values and descriptors, and their correspondent peak areas integrated.

Beer samples were also evaluated by GC-MS using the same chromatographic

conditions as above to further aid in peak identification. A ZB-5 column (60 m x 0.25

mm i.d x 0.25 um film thickness) was used. Mass spectra were matched with several

flavor libraries of mass spectra for identification using Xcaliber Software, Version 1.3.

Sensory

Sensory analysis was done to compare the aroma and flavor profiles of fresh beer

to dense-phase CO2 processed beer and dense-phase CO2 processed beer to heat

pasteurized beer, and a ranking test was used to rate the likability of different dense-

phase CO2 processed beer treatments. A randomized complete block design was used

(Ott, 1993) and difference from control measurements were recorded using Compusense

on a line scale with anchors at 0 and 10 of “no difference” and “extremely different”

(Compusense, Guelph, Ontario, Canada). All sensory tests were performed in the taste

panel facility in Building 120, University of Florida, Gainesville, Florida which consisted

of privacy booths for each panelists. The relative humidity was approximately 60% and

the room temperature was between 23-25°C. Forty-five to sixty untrained panelists were

37

used in each test and selected on the basis of age (21+ years or older) and familiarity with

beer and willingness to sign the informed consent document. No incentive was given to

participate, however snacks were available afterwards to counteract any effects of the

alcohol on panelists.

Samples were labeled with random numbers and were presented on a white plate

with the reference sample at the top center. In the next row the first 3 samples from left

to right, and in the bottom row the last 2 samples from left to right. Samples were

degassed overnight by placing on a hot plate with stirring to equalize carbonation levels

and were served at room temperature.

Data was assumed to be normal and an analysis of variance was performed using

SAS Software (The SAS Institute, Cary, North Carolina). Tukey’s mean separation

(alpha=.1) tests were also performed on SAS if there was a significant difference in mean

to examine mean separations.

Fresh beer vs. dense-phase CO2 processed beer

A difference from control test was used with the reference being fresh beer and the

samples consisting of a hidden control of fresh beer, and three different dense-phase CO2

processed beer treatments. Panelists were asked to first rate the intensity of the

differences in the aroma of the samples and then to rate the intensity of the differences in

the flavor of the samples.

Dense-phase CO2 processed beer vs. heat pasteurized beer

A difference from control test was used with the reference being fresh beer and the

samples consisting of a hidden control of fresh beer, and one dense-phase CO2 processed

beer treatment (26.7MPa, 21ºC, 10% CO2, 5 minute residence time), and one heat

pasteurized beer sample (74°C, 30 seconds). Panelists were asked to first rate the

38

intensity of the differences in the aroma of the samples and then to rate the intensity of

the differences in the flavor of the samples.

Storage studies

All sensory tests were repeated after 30 days of storage at 1.67°C.

Statistical Analysis

Sensory data were gathered by the Compusense system (Compusense, Guelph,

Ontario, Canada), and data were pooled and analyzed using Excel spreadsheets for

Windows and SAS software Version 9.0 (The SAS Institute, Cary, North Carolina). An

analysis of variance was done on each set of data and a Tukey’s mean separation was

performed. A 90% confidence level was used.

Conjoint Analysis of Beer Purchase Decision

A conjoint analysis was performed to determine the part-worth values of price,

flavor, and shelf stability of beer in a consumer’s purchase decision. This was done using

a full factorial of combinations created from the characteristics of bottled vs. draft flavor,

refrigerated vs. shelf stability, and $6.00/six 12 oz. bottles vs. $8.00/six 12 oz. bottles.

These eight combinations were presented to consumers (21 and older) and consumer

were asked to rank the combinations in order of purchase choices without ties. Part-

worth values for each level of each attribute were then calculated. The higher the part-

worth value the more influence the level of a characteristic has on purchase decision

(Hair, et al., 1998).

CHAPTER 4 RESULTS AND DISCUSSION

This chapter contains experimental results obtained for the dense-phase CO2

pasteurization of beer, presented in five sections, namely yeast log reduction, foam, haze,

sensory evaluation, and gas chromatography results.

Microbial Reduction Experiments

Yeast plate counts are listed in Table A-1 in Appendix A. Yeast log reductions for

control and treated samples are presented in Table 4.1. A prediction equation was

calculated using the log reduction responses and response surface methodology, where

x1=pressure in MPa, x2=temperature in °C, x3=CO2 %, and x4=residence time in minutes

(Khuri and Cornell, 1996). SAS software was used using the code in Appendix C. The

prediction equation in coded variables is:

Predicted Log Reduction = 5.16 - .135x1 –.80x2 –.043x3 + .05x4 –1.17x12 +.514(x2x1) +1.28(x22) +.012(x3x1) +.734(x3x2) -.971(x32) -.077(x4x1) + .852(x4x2) -.087(x4x3) -.756(x42)

And in non-coded variables the prediction equation is:

Predicted log reduction =.653 +.553x1 –1.016x2 –1.860x3 +2.623x4 -.012x12

+.004(x2x1) +.007(x22) +.0004(x3x1) +.019(x3x2) -.121(x32) -.006(x4x1) + .043(x4x2) -.022(x4x3) -.372 (x42) The ANOVA table used to generate these results is:

The RSREG Procedure Coding Coefficients for the Independent Variables Factor Subtracted off Divided by X1 27.550000 9.750000 X2 35.000000 14.000000

39

40

X3 10.002500 2.827500 X4 4.985000 1.425000 Response Surface for Variable Y Response Mean 4.572981 Root MSE 0.651957 R-Square 0.7307 Coefficient of Variation 14.2567 Type I Sum Regression DF of Squares R-Square F Value Pr > F Linear 4 13.142063 0.2135 7.73 0.0001 Quadratic 4 19.328234 0.3140 11.37 <.0001 Crossproduct 6 12.515434 0.2033 4.91 0.0008 Total Model 14 44.985731 0.7307 7.56 <.0001 Sum of Residual DF Squares Mean Square Total Error 39 16.576879 0.425048 Parameter Estimate Standard from Coded Parameter DF Estimate Error t Value Pr > |t| Data Intercept 1 0.653046 7.574578 0.09 0.9317 5.161059 X1 1 0.552928 0.223678 2.47 0.0179 -0.134616 X2 1 -1.015581 0.144890 -7.01 <.0001 -0.800175 X3 1 1.860443 0.884747 2.10 0.0420 -0.042847 X4 1 2.623033 1.754089 1.50 0.1429 0.050220 X1*X1 1 -0.012258 0.003265 -3.75 0.0006 -1.165235 X2*X1 1 0.003771 0.001670 2.26 0.0296 0.514805 X2*X2 1 0.006516 0.001573 4.14 0.0002 1.277046 X3*X1 1 0.000423 0.008352 0.05 0.9598 0.011674 X3*X2 1 0.018548 0.005763 3.22 0.0026 0.734228 X3*X3 1 -0.121398 0.038833 -3.13 0.0033 -0.970548 X4*X1 1 -0.005533 0.016703 -0.33 0.7422 -0.076869 X4*X2 1 0.042709 0.011525 3.71 0.0007 0.852052 X4*X3 1 -0.021641 0.057625 -0.38 0.7093 -0.087194 X4*X4 1 -0.372491 0.153716 -2.42 0.0201 -0.756389 Sum of Factor DF Squares Mean Square F Value Pr > F X1 5 8.623020 1.724604 4.06 0.0046 X2 5 32.363641 6.472728 15.23 <.0001 X3 5 8.654597 1.730919 4.07 0.0045 X4 5 8.468728 1.693746 3.98 0.0051 Canonical Analysis of Response Surface Based on Coded Data Critical Value Factor Coded Uncoded X1 -0.009502 27.457356 X2 0.241912 38.386764 X3 0.061900 10.177523 X4 0.166366 5.222071

41

Predicted value at stationary point: 5.067764 Eigenvectors Eigenvalues X1 X2 X3 X4 1.437413 0.093337 0.967866 0.144464 0.183449 -0.776571 -0.159294 -0.097086 -0.391092 0.901247 -1.050380 -0.383442 -0.147191 0.863977 0.291290 -1.225588 0.904923 -0.179288 0.282347 0.263153 Stationary point is a saddle point. Estimated Ridge of Maximum Response for Variable Y Coded Estimated Standard Uncoded Factor Values Radius Response Error X1 X2 X3 X4 0.0 5.161059 0.210737 27.550000 35.000000 10.002500 4.985000 0.1 5.255745 0.210093 27.412807 33.620157 9.977017 4.981320 0.2 5.378149 0.208419 27.299691 32.245753 9.942388 4.965593 0.3 5.528925 0.206472 27.195846 30.877347 9.904840 4.945407 0.4 5.708271 0.205532 27.096467 29.512775 9.866025 4.923116 0.5 5.916267 0.207389 26.999570 28.150655 9.826552 4.899667 0.6 6.152952 0.214171 26.904196 26.790168 9.786696 4.875515 0.7 6.418347 0.227957 26.809822 25.430817 9.746596 4.850902 0.8 6.712462 0.250290 26.716139 24.072285 9.706333 4.825973 0.9 7.035307 0.281894 26.622955 22.714361 9.665955 4.800817 1.0 7.386885 0.322772 26.530142 21.356901 9.625492 4.775491

Table 4-1: Microbial Reduction Results (Independent Variables are in Coded Units)

42

Pressure Temp CO2 Time Log Reduction Replicate Log Reduction0.000 -1.414 0.000 0.000 6.11 6.06

-1.000 -1.000 -1.000 -1.000 6.11 6.061.000 -1.000 -1.000 -1.000 6.11 6.06

-1.000 -1.000 -1.000 1.000 6.11 6.061.000 -1.000 -1.000 1.000 4.71 4.18

-1.000 -1.000 1.000 -1.000 6.11 6.061.000 -1.000 1.000 -1.000 4.41 4.18

-1.000 -1.000 1.000 1.000 4.24 3.961.000 -1.000 1.000 1.000 4.24 3.960.000 0.000 -1.414 0.000 3.19 3.280.000 0.000 0.000 -1.414 3.63 3.58

-1.414 0.000 0.000 0.000 3.46 3.400.000 0.000 0.000 0.000 4.71 4.660.000 0.000 0.000 0.000 6.11 6.060.000 0.000 0.000 0.000 6.11 6.061.414 0.000 0.000 0.000 3.93 3.810.000 0.000 0.000 1.414 4.71 4.360.000 0.000 1.414 0.000 4.71 4.18

-1.000 1.000 -1.000 -1.000 2.89 3.211.000 1.000 -1.000 -1.000 3.06 3.38

-1.000 1.000 -1.000 1.000 4.01 3.761.000 1.000 -1.000 1.000 4.11 3.81

-1.000 1.000 1.000 -1.000 3.37 3.301.000 1.000 1.000 -1.000 4.01 3.81

-1.000 1.000 1.000 1.000 4.41 4.361.000 1.000 1.000 1.000 4.41 4.180.000 1.414 0.000 0.000 6.11 6.06

43

All terms, regardless of significance were kept as to mirror what independent

variables would effect log reduction in a real system, as compared to this model. The

overall shape of the response surface was a saddle point with occurred at x1=27.4 MPa,

x2= 38°C, x3=10.1% CO2, x4=5.2 minutes residence time, and predicted log reduction of

5.1 logs.

Using the prediction equation above, the predicted maximum log reduction of 7.38

occurred at 26.5 MPa, 21°C, 9.6% CO2, and 4.77 minutes residence time. As stated in

literature, log reduction was directly proportional to absorption of CO2 (Kumagai et al.,

1997). The lowest experimental temperature would allow the greatest amount of CO2 to

be dissolved in the beer. Although higher pressure and higher CO2 % would dissolve

more CO2, and longer residence times would allow more equilibration, data shows that

saturation is reached at 9.6% CO2, before the highest CO2 % of 12. Higher levels of CO2

only resulted in an excess of CO2. Also an increase of pressure from 26.62 to 37.33 MPa

did not result in significant increase in CO2 uptake. Because beer was carbonated before

processing the possible saturation of CO2 occurred at lower pressures and CO2 levels than

expected.

Overall, a predicted log reduction of 7.38 makes dense-phase CO2 pasteurization a

formidable alternative to heat pasteurization for the brewing industry. Currently most

brewers try to limit the amount of heat needed to pasteurize by prefiltering the beer to

decrease the number of yeast that must be killed by heat. This allows a brewer use to a

minimal amount of heat during pasteurization, thus limiting flavor damage. However the

use of dense-phase CO2 not only would allow the elimination of the filtering step prior to

pasteurization, but also would prevent flavor damage by heat because the process is

44

optimized for 21°C. This would simplify the extension of beer shelf life, while insuring

no heat damage.

Mode of Cell Death

Yeast populations remained unchanged after one month of storage at 1.67°C,

indicating that there may not be an injury/repair mechanism due to the nature of the

dense-phase CO2 pasteurization. To elucidate the mode of death, samples were examined

using scanning electron microscopy. Images 1, 2, and 3 compare yeast from unprocessed

(fresh) beer to that from beer after pasteurization at 27.6 MPa, 10% CO2, 21°C, for 5

minutes, and heat pasteurization at 74°C for 30 seconds, respectively. Fresh yeast are

pert and round, with a smooth appearance. Heat pasteurized yeasts still appeared round

and pert with slightly textured surfaces. After dense-phase CO2 pasteurization, some

cells show explosive decompression but most have a shrunken appearance with divots in

the surface. This illustrates the ability of dense-phase CO2 to affect cell me

extraction of their components.

mbranes by

45

Figure 4-1: Scanning Electron Microscopy Picture of Yeast in Fresh Beer

Processed at 27.6 MPa, 10% CO2, at 21°C, With a Residence Time of 5 Figure 4-2: Scanning Electron Microscopy Picture of Yeast in Beer Dense-Phase CO2

Minutes

Figure 4-3: Scanning Electron Microscopy Picture of Yeast in Beer Pasteurized at 74

°C for 30 Seconds

46

Alcohol in beer acts as a co-solvent, aiding in the extraction of hydrophobic and

some semi-polar materials. It has been reported that the solvent characteristics of CO2 can

be greatly modified by the additio n cosolvents. For example, the

additi

20.7 MPa CO2 processed samples at 101 NTU, heated

mple 120.7 NTU, and fresh samples at 146 NTU. Turbidity after one month of storage

at 1.67°C, showed that all samples increased in haze significantly, however a mean

separation was not performed due to the increase in haze was most likely due to

microbial growth, not differences in beer protein (Table 4-2). The following ANOVA

results were used in the analysis:

Sum of Source DF Squares Mean Square F Value Pr > F Model 3 4720.916667 1573.638889 1110.80 <.0001 Error 8 11.333333 1.416667 Corrected Total 11 4732.250000

n or existence of certai

on of ethanol may have significant effects on extraction by allowing increased

absorption on surface sites, preventing the re-adsorption of the compound of interest

(Clifford and Williams, 2000). Cosolvents have also been shown to increase the amount

of oil extracted from sunflower seeds (Raghuram Rao et al., 1992), and rape seed, soy

bean, cottonseed, flax seed, and peanuts, and to enhance the extraction of herbal

components such as borage seed oil and hiprose fruit (Illes et al., 1994).

Effect of Dense-phase CO2 Processing on Haze

Turbidity results after processing showed significant differences between all

sample means (p<.0001) on the ANOVA table and Tukey’s mean separations (alpha=.1)

are shown in Figure 4-4. The 27.6 MPa CO2 processed samples had the lowest turbidity

average at 95.3 NTU, followed by

sa

47

R-Square Coeff Var Root MSE y Mean

Source DF Type I SS Mean Square F Value Pr > F x 3 4720.916667 1573.638889 1110.80 <.0001 Source DF Type III SS Mean Square F Value Pr > F x 3 4720.916667 1573.638889 1110.80 <.0001

Table 4-2: Beer Haze in NTU After Processing and After Storage at 1.67° C for 30 Days

0.997605 1.028283 1.190238 115.7500

After Processing After Storage DifferenceSample Turbidity (NTU) Trubidity (NTU) Over TimeFresh 146 425 P-valueFresh 144 440 3.702E-07Fresh 148 43127.6 MPa 95 424 P-value27.6 MPa 95 428 4.198E-0727.6 MPa 96 442

20.720.7 100 392Heated 121 440 P-valueHeated 120 444 8.506E-08Heated 121 452

20.7 MPa 101 404 P-value MPa 102 400 1.247E-07 MPa

Figure 4-4: Beer Haze Following Processing and Following Storage at 1.67°C for 30 Days

D

48

Changes seen in haze due to dense-phase CO2 processing may have been caused b

the drastic pH change associated with this process. During processing the pH drops to

approximately 3, which would affect protein conformation and polyphenol conformation,

interfering with protein-polyphenol complexes. To further examine the cause of the h

differences seen between beer samples Polyacrylamide gel electrophoresis of beer

proteins was performed. Gels showed no differences in protein bands between fresh,

CO2 treated, and heat pasteurized samples. It must be concluded that differences in haz

between samples after processing cannot be attributed to changes in beer proteins

dense-phase CO2 processing. Gel pictures are available in Appendix A.

y

aze

e

during

Effect of De d Stability

se-

Sum of Source DF Squares Mean Square F Value Pr > F

544.000000 22.67 0.0003

Error 8 192.000000 24.000000

Corrected Total 11 1824.000000

SE y Mean

nse-phase CO2 Processing on Foam Capacity an

Foam capacity results after processing showed differences between fresh, den

phase CO2 processed, and heated beer sample means (p=.0003). Tukey’s mean

separation (alpha=.1) showed that the CO2 processed samples were not significantly

different from each other, however they did differ from heated samples. Fresh and heated

samples were also found to be significantly different. The following ANOVA results

were used in the analysis for foam capacity and foam stability, respectfully:

Foam Formation:

Model 3 1632.000000 R-Square Coeff Var Root M

49

0.894737 1.530931 4.898979 320.0000 Source DF Type I SS Mean Square F Value Pr > F

Source DF Type III SS Mean Square F Value Pr > F

oam Stability:

Source DF Squares Mean Square F Value Pr > F Model 3 164.0000000 54.6666667 41.00 <.0001 Error 8 10.6666667 1.3333333 Corrected Total 11 174.6666667 R-Square Coeff Var Root MSE y Mean 0.938931 2.074312 1.154701 55.66667 Source DF Type I SS Mean Square F Value Pr > F x 3 164.0000000 54.6666667 41.00 <.0001

Source DF Type III SS Mean Square F Value Pr > F x 3 164.0000000 54.6666667 41.00 <.0001

Mean separations are shown on Figure 4-5. Heated samples had the highest foam

capacity at 333%, followed by 27.6 MPa CO2 processed beer with 324%, 20.7 MPa CO2

processed beer with 321%, and fresh beer with 301% foam capacity (Figure 4-5). After

30 days of storage at 1.67°C, sample means again showed significant differences

(p=.0006). Heat and fresh samples were not significantly different and had the highest

both with foam capacities of 307% (Figure4-6).

x 3 1632.000000 544.000000 22.67 0.0003 x 3 1632.000000 544.000000 22.67 0.0003

F

Sum of

foam capacity with 332% and 327%, respecitively. CO2 processed samples followed,

50

Figure 4-5: Foam Capacity and Stability of Beer Samples After Processing

Figure 4-6: FoaDay

Foam stab

phase CO2 proc

Foam For

Source

mean separation

significantly dif

The following A

a

m Capacity and s

ility results after

essed, and heated

mation, Aged:

(alpha=.1) show

ferent from each

NOVA results w

b

Stability of

processing

beer samp

DF

ed that the

other, how

ere used in

Sum

b

Beer Samp

showed di

le means (p

Squares

CO2 proce

ever did di

the analys

of

a

b

a

les After Storage at 1.67°C for 30

fferences between fresh, dense-

<.0001) (Figure 4-5). Tukey’s

Mean Square F Value Pr > F

ssed samples were not

ffer from fresh and heated samples.

is:

51

Model 3 1584.000000 528.000000 18.86 0.0006

DF Type I SS Mean Square F Value Pr > F x 3 1584.000000 528.000000 18.86 0.0006

18.86 0.0006

Source DF Squares Mean Square F Value Pr > F

Error 8 224.000000 28.000000 Corrected Total 11 1808.000000 R-Square Coeff Var Root MSE y Mean 0.876106 1.663995 5.291503 318.0000

Source

Source DF Type III SS Mean Square F Value Pr > F x 3 1584.000000 528.000000

Foam Stability, Aged:

Sum of Model 3 334.6666667 111.5555556 9.30 0.0055 Error 8 96.0000000 12.0000000 Corrected Total 11 430.6666667

0.777090 7.585624 3.464102 45.66667 Source DF Type I SS Mean Square F Value Pr > F

x 3 334.6666667 111.5555556 9.30 0.0055

Source DF Type III SS Mean Square F Value Pr > F

x 3 334.6666667 111.5555556 9.30 0.0055

2

R-Square Coeff Var Root MSE y Mean

Fresh and heated sample means were not significantly different. Heated samples

had the highest foam stability at 60%, followed by fresh beer with 59%, and CO

52

processed samples with 52% foam stability for both CO2 treatments. After 30 days of

storage at 1.67°C, sample means again showed significant differences (p<.0055). Heated

samples were significantly different from all other treatments with 55% foam stability.

Fresh and CO2 processed samples were not significantly different with fresh beer foam

stability at 44%, and 27.6 MPa and 20.7 MPa at 43% and 41%, respectively (Figure 4-6).

Changes seen in foam characteristics due to dense-phase CO2 processing may have

been caused by the extraction of cell membrane or cell wall parts that may have changed

ing.

In both foam capacity and foam stability, data showed that both CO2 processing

and heat pasteurization may have significant effects, however, all beers formed a stable

head at a level that would probably insure customer satisfaction. Original foam volumes

and liquid volumes are listed in Table A-2.

To further examine the cause of the foam differences seen between beer sample

means Polyacrylamide gel electrophoresis of beer proteins was performed. Gels showed

no differences in protein bands between fresh, CO2 treated, and heat pasteurized samples.

attributed to changes in beer proteins during processing. Gel pictures are available in

Appendix A.

In the cases of both beer haze and foam, dense-phase CO2 pasteurization of beer in

no way decreased the quality of the finished product. Consumers would be expected to

see no differences between fresh and CO2 pasteurized beer.

the amount of hydrophobic compounds in the beer, therefore, affecting foam

It must be concluded that differences in foam between samples after processing cannot be

53

Effect of Dense-phase CO2 Processing on Beer Aroma and Flavor by Sensory

Difference from control panels and gas chromatography-olfactometry were

erformed to prove there were no aroma or flavor changes due to dense-phase CO2

asteurization. Panels were conducted within 1 day of processing and repeated after 30

ays of storage at 1.67°C. All samples were decarbonated and served at room

mperature. For sensory panels, analysis of variance (∀=.05) was used to compare

eatment means and, when differences existed, a Tukey’s mean separation test was

Science and Human Nutrition Department, University of Florida.

Beer

% CO2, for 5 minutes, at 21°C. Samples were evaluated on a line scale with 0=no

ifference from the fresh beer reference and 10=extremely different from the fresh beer

ference.

The following ANOVA results were created and used for analysis:

Sum of

Model 63 643.348046 10.211874 1.85 0.0022

Error 116 641.861454 5.533288

Corrected Total 179 1285.209500

R-Square Coeff Var Root MSE y Mean

0.500578 67.56231 2.352294 3.481667

Evaluation

p

p

d

te

tr

employed to compare specific treatment means at ∀=.10. A sample ballot is included in

Appendix B and raw data is available by contacting Dr. Murat Balaban in the Food

Aroma

A difference from control test comparing aromas of fresh beer, beer processed at

27.6 MPa with 10% CO2, for 5 minutes, at 21°C, and beer processed at 20.7 MPa with

10

d

re

Source DF Squares Mean Square F Value Pr > F

54

row 59 573.3695000 9.7181271 1.76 0.0051

column 2 2.9962126 1.4981063 0.27 0.763

Source DF Type III SS Mean Square F Value Pr >

row 59 573.5187454 9.7206567 1.76 0.0051

Source DF Type I SS Mean Square F Value Pr > F

treatment 2 66.9823333 33.4911667 6.05 0.0032 3

F

treatment 2 66.9877883 33.4938942 6.05 0.0032

Sample means were significantly different (p=.0032), with average scores of 2.72,

3.52, and 4.21, respectively on the ten point scale. All samples were rated as only

slightly different from the reference and similarities were seen between fresh and the 27.6

MPa beer, and between the 2 CO processed sample means. Mean separations are shown

using letters on Figure 4-7.

column 2 2.9962126 1.4981063 0.27 0.7633

2

Figure 4-7: Aroma Evaluation of Fresh and CO Processed Beer Samples (mean

C, 30 seconds). Samples

were again evaluated on a line scale with 0=no difference from the fresh beer reference

2 separations labeled)

A difference from control test was then conducted to compare the aroma of fresh,

27.6 MPa CO2 processed beer, and heat pasteurized beer (74°

a b

A AB

B

55

and lts

were created and used for analysis:

Sum of r > F

Error 86 484.8647953 5.6379627

Corrected Total 134 961.1760000

R-Square Coeff Var Root MSE y Mean

Source DF Type I SS Mean Square F Value Pr > F .0108

treatment 2 25.2840000 12.6420000 2.24 0.1124 90 0.61 0.5463

F

5 treatment 2 26.4211450 13.2105725 2.34 0.1021 column 2 6.8645381 3.4322690 0.61 0.5463

2.75, 3.29, and 3.81 and are shown in Figure 4-8.

10=extremely different from the fresh beer reference. The following ANOVA resu

Source DF Squares Mean Square F Value P

Model 48 476.3112047 9.9231501 1.76 0.0113

0.495550 72.39145 2.374439 3.280000

row 44 444.1626667 10.0946061 1.79 0

column 2 6.8645381 3.43226

Source DF Type III SS Mean Square F Value Pr >

row 44 445.3035982 10.1205363 1.80 0.010

There were no significant differences between sample means(p=.1021). Average

scores were

56

Samples

Beer Flavor

A difference from contr

Figure 4-8: Evaluation of Aro

o

27.6 MPa with 10% CO2, for 5

10% CO2, for 5 minutes, at 21

difference from the fresh beer

reference. The following ANO

Source Model Error

R-Square 0.523751

Source

row treatment column

Corrected Total

A

mas of Fresh, CO2 Processed and Heat Pasteurized Beer

l test comparing flavor of fresh beer, beer processed at

minutes, at 21°C, and beer processed at 20.7 MPa with

°C. Samples were evaluated on a line scale with 0=no

reference and 10=extremely different from the fresh beer

VA results were created and used for analysis:

Sum of DF Squares Mean Square F Value Pr > F 63 681.309167 10.814431 2.02 0.0005 116 619.517278 5.340666

Coeff Var Root MSE y Mean

60.09504 2.310988 3.845556

DF Type I SS Mean Square F Value Pr > F

59 666.5731111 11.2978493 2.12 2 11.5821111 5.7910556 1.08

2 39444 1.57 22 0.30 0.7449

179 1300.826444

0.0003 0.3415

3.15 697

57

row 59

Source DF Type III SS Mean Square F Value Pr > F

666.9011463 11.3034093 2.12 0.0003

treatment 2 11.5196545 5.7598272 1.08 0.3435 column 2 3.1539444 1.5769722 0.30 0.7449

Sample means were not significantly different (p=.3435), with average scores of

3.63, 3.71, and 4.2, respectively on the ten point scale. Scores are shown on Figure 4-9.

Figure 4-9: Evaluation of Beer Flavor Between Fresh and CO2 Processed Samples

A difference from control test was then conducted to compare the flavor of fresh,

27.6 M

the fresh beer reference

and 10=extremely different from the fresh beer reference.

The following ANOVA results were created and used for analysis:

Sum of Source DF Squares Mean Square F Value Pr > F Model 48 658.515137 13.719065 2.51 <.0001 Error 86 469.693011 5.461547 Corrected Total 134 1128.208148 R-Square Coeff Var Root MSE y Mean

Pa CO2 processed beer, and heat pasteurized beer (74°C, 30 seconds). Samples

were again evaluated on a line scale with 0=no difference from

58

0.583682 55.69185 2.336995 4.196296 Source DF Type I SS Mean Square F Value Pr > F row 44 571.6481481 12.9920034 2.38 0.0003 treatment 2 76.3779259 38.1889630 6.99 0.0015 column 2 10.4890631 5.2445316 0.96 0.3869 Source DF Type III SS Mean Square F Value Pr > F row 574.7377992 13.0622227 2.39 0.0003 treatment 2 78.8786056 39.4393028 7.22 0.0013 column 2 10.4890631 5.2445316 0.96 0.3869

Sample means were significantly different (p=.0013), with average scores of 3.66,

3.67,

different from the reference and similarities were seen between fresh and the 27.6 MPa

beer, and the heated sample was seen as significantly different from the others. Mean

separations are shown using letters on Figure 4-10.

44

and 5.26, respectively on the ten point scale. All samples were rated only slightly

Figure 4-10: Evaluation of Beer Flavors Between Fresh, CO Processed, and Heat 2

Pasteurized Samples (mean separations labeled)

A A B

59

Beer Aroma After Storage

A storage study was conducted at 1.67° C for 30 days and beer aroma and taste

tests w

proce 7

2

stored, fresh hidden control. Samples were evaluated on a line scale with 0=no difference

from the fresh beer reference and 10=extremely different from the fresh beer reference.

The following ANOVA results were created and used for analysis:

Sum of Source DF Squares Mean Square F Value Pr > F Model 56 961.287287 17.165844 3.77 <.0001 Error 155 706.211393 4.556203 Corrected Total 211 1667.498679 R-Square Coeff Var Root MSE Sample_Aroma Mean

F

1 Samp_ 3 24.9533962 8.3177987 1.83 0.1448

1

n Square F Value Pr > F

3.95 <.0001 1.86 0.1383 Samp_Code 1 1.0002111 1.0002111 0.22 0.6401

ere repeated. A difference from control test comparing aromas of fresh beer, beer

ssed at 27.6 MPa with 10% CO2, for 5 minutes, at 21°C, and beer processed at 20.

MPa with 10% CO , for 5 minutes, after 30 days of storage at 1.67°C at 21°C, and a non-

0.576485 74.35419 2.134526 2.870755

Source DF Type I SS Mean Square F Value Pr >

Samp_Set 52 935.3336792 17.9871861 3.95 <.000

Samp_Code 1 1.0002111 1.0002111 0.22 0.640

Source DF Type III SS Mea

Samp_Set 52 935.3336792 17.9871861 Samp_ 3 25.4545507 8.4848502

There were no significant differences seen between sample means and averages

scores of 2.35, 3.3, 2.99, and 2.84 are shown on Figure 4-11.

60

Figure 4-11: Aroma Evaluation of Aroma Between Aged, CO2 Processed, Heat

Pasteurized, and a Non-Stored, Fresh Hidden Control

27.6 MPa CO2 processed beer, heat pasteurized beer (74 C, 30 seconds) after 30 days of

storage at 1.67°C, and a non-aged fresh hidden control. Samples were again evaluated on

a line scale with 0=no difference from the fresh beer reference and 10=extremely

different from the fresh beer reference.

The following ANOVA results were created and used for analysis:

Sum of Source DF Squares Mean Square F Value Pr > F Model 50 825.213462 16.504269 3.03 <.0001 Error 129 703.666538 5.454779 Corrected Total 179 1528.880000 R-Square Coeff Var Root MSE taste Mean 0.539750 58.38867 2.335547 4.000000

r > F set 44 533.1900000 12.1179545 2.22 0.0003 number 3 290.1186667 96.7062222 17.73 <.0001 code 3 1.9047954 0.6349318 0.12 0.9504

A difference from control test was then conducted to compare the aroma of aged,

°

Source DF Type I SS Mean Square F Value P

61

Source DF Type III SS Mean Square F Value Pr > F 003 number 3 290.0674621 96.6891540 17.73 <.0001

set 44 533.1900000 12.1179545 2.22 0.0

code 3 1.9047954 0.6349318 0.12 0.9504

Sample means were significantly different (p<.0001), with average scores of 2.56,

3.34, 4.87, and 1.99, respectively on the ten point scale. Mean separations are shown

using letters on Figure 4-12.

Figure 4-12: Evaluation of Flavor Between Aged, CO2 Processed, Heat Pasteurized, and

a Non-Stored, Fresh Hidden Control (mean separations labeled)

27.6 MPa with 10% CO2, for 5 minutes, at 21°C, and beer processed at 20.7 MPa with

10% C

Beer Flavor After Storage

A difference from control test comparing the flavor of aged beer, beer processed at

O2, for 5 minu s, after 30 days of torage at 1.67°C at 2 °C, and a non-store ,

fresh hidden control. Samples were evalu

sA

ith 0=no difference ated on a line scale w

dA

teA

1B

A

from

62

the fresh beer reference and 10=extremely different from the fresh beer reference. The

following ANOVA results were created and used for analysis:

Sum of Source DF Squares Mean Square F Value Pr > F Model 56 798.921305 14.266452 2.92 <.0001 Error 155 756.016242 4.877524 Corrected Total 211 1554.937547 R-Square Coeff Var Root MSE Taste_Difference Mean 0.513796 61.58965 2.208512 3.585849

Source DF Type I SS Mean Square F Value Pr > F Samp_ 3 13.3967925 4.4655975 0.92 0.4349

<.0001

Samp_ 3 13.7483426 4.5827809 0.94 0.4231 0.8419653 0.8419653 0.17 0.6784

age scores of

Samp_Set 52 784.6825472 15.0900490 3.09 <.0001

Samp_Code 1 0.8419653 0.8419653 0.17 0.6784

Source DF Type III SS Mean Square F Value Pr > F

Samp_Set 52 784.6825472 15.0900490 3.09

Samp_Code 1

Sample means were not significantly different (p=.4231), with aver

3.58, 3.91, 3.65, and 3.20, respectively on the ten point scale. Scores are shown on

Figure 4-13.

63

Figure 4-13: Evaluation of Flavor Between Fresh and CO2 Processed Beer After Storage

at 1.67°C for 30 Days, and a Non-Stored, Fresh Hidden Control

A difference from control test was then conducted to compare the flavor of aged,

27.6 MPa CO2 processed beer, heat pasteurized beer (74°C, 30 seconds) after 30 days of

storage at 1.67°C, and a non-aged fresh hidden control. Samples were again evaluated on

a line scale with 0=no difference from the fresh beer reference and 10=extremely

different from the fresh beer reference.

The following ANOVA results were created and used for analysis:

Sum of Source DF Squares Mean Square F Value Pr > F

Error 131 648.607913 4.951205

Corrected Total 179 1441.913111 R-Square Coeff Var Root MSE Sample_Aroma Mean

0.550175 69.80193 2.225130 3.187778

Source DF Type I SS Mean Square F Value Pr > F

Samp_Set 44 581.5481111 13.2170025 2.67 <.0001 Samp_ 3 211.7388889 70.5796296 14.26 <.0001 Samp_Code 1 0.0181980 0.0181980 0.00 0.9517

Model 48 793.305198 16.527192 3.34 <.0001

64

Samp_Set 44 581.5481111 13.21

Source DF Type III SS Mean Square F Value Pr > F

70025 2.67 <.0001 Samp_ 3 211.6859758 70.5619919 14.25 <.0001

0.0181980 0.00 0.9517

Sample means were significantly different (p<.0001), w h average scores of 2.92,

.71, 6.14, and 3.23, re ectively on the t n point scale. Mean separations are s own

sing letters on Figure 4-14.

Samp_Code 1 0.0181980

3

u

Figur

Samples After 30 Days at 1.67°C, and a Non-Stored, Fresh, Hidden Control

ing all decarbonated beer samples, overall, panelists could easily

distinguish heat pasteurized beer, but in only on one occasion was a difference noted

between fresh and the 20.7 MPa CO2 processed beer. This difference in aroma may have

been caused by the increased spillage of cell components, caused by CO2 processing at

20.7MPa. These cell compo ents have been known to contribute to off-flavors in beer

(Lin et al., 1991). No differ

following less-volatile comp

masked any differences in a

e 4-14: Evaluation of Flavor of Fresh, CO2 Processed, and Heat Pasteurized

(mean separations labeled)

Consider

AA

nA

ences were n

ounds, many

roma during b

e

sp

oted in flavor between the same sample

of which could be hop constituents, co

eer tastings. On average, panelists co

hA

it

B

s and in

uld have

uld only

65

discern heat pasteurized beer during sensory tests. The flavor differences caused by

dense-phase CO2 pasteurization were negligible. In an industry with its foundations in

producing a consistently fresh product, dense-phase CO2 pasteurization is an alternative

to heat pasteurization that results in the same extended shelf life, while preserving fresh

beer taste.

Effect of Dense-phase CO2 Processing on Beer Aroma and Flavor by Gas Chromatography-Olfactometry and Mass Spectrometry

Odor descriptors and retention times of aroma active compounds present in beer

the as

on the ZB-5 column were used to characterize beer samples.

was

ined from the ZB-5 and Carbowax columns and

their spectroscopy.

the sample, however was not

etected previously by GC-O. Mass spectra library matches, chromatograms, and

d Science and

egration

d using LRI

alues and confirmed using mass spectra (Table 4-6). When comparing values of

samples can be observed in Table 4-4. A variety of aroma compounds were detected by

sessors, however only those detected more than 50% of the time by both assessors

Linear retention indices (LRI) were calculated for aroma active compounds.

Tentative identification of the aroma compounds present in fresh beer (Table 4-5)

conducted based on their LRI values obta

aroma descriptors. Compounds in red were then confirmed by mass

Methyl nerolate was also identified by mass spectra in

d

aromagrams are available by contacting Dr. Murat Balaban in the Foo

Human Nutrition Department, University of Florida.

To evaluate if dense-phase CO2 processing did cause flavor changes, int

areas (reported in mV), were compared for compounds that had been identifie

v

compounds before and after dense-phase CO2 pasteurization, many compounds had

negligible differences, however in the case of ethyl hexanoate, the amount of the

66

compound decreased on average by approximately 49%. Because of ethyl hexanoate’s

processing.

Table 4-4: Retention Ti s and Aroma D criptors for Comp nds Detected by oth

Although stripping did occur, sensory panelists did not recognize differences

between the 26.7MPa dense-phase CO2 and fresh beer. This is possible because even

though ethyl hexanoate did decrease in CO2 processed beer, if it remained above its

flavor threshold, no flavor change would be detectable to panelists. Overall, it can be

concluded that although some stripping did occur, no appreciable changes were detected

between fresh and CO2 processed beer samples.

volatile nature, not only did this compound elute early in the chromatography run (4.26

minutes on Carbowax column), but was also easily stripped by the carbon dioxide during

me es ou BAssessors on ZB-5 and Carbowax Columns

(desciptors are listed for the appRetention Times (minutes) Descriptor on ZB-5 Descriptor on Carbow ax

ropriate column only)2.82 fruity3.21 banana, bubble gum3.71 fruit4.17 Fruity4.26 musty, fruity4.92 Fruity/sweet5.58 fruity, sour5.96 fruity, wine6.18 Fruity6.5 Fruity7.3 pepper8.7 green apple/sweet9.6 sweet,

cooked/coconutty11.2 green apple/sweet11.9 apple pie13.1 slight red

burnt juice15.15 burnt

apple/sweet13.61 apple pie14.81

67

Table 4-5: Linear Retention I es d I tif tio f C pounds using GC-O (Com nds in Red Wer Confir ed by Mass S tro py

e m

)1051)

ndic an den ica n o om

Compounds on -5 RI of om unds n C108113119

769125

879141145

020156

128194269

184365396

pou

wa Ten ive eth 2-m hylbhexenal Z (11hexanal E (12eth 2-m hylpeth hex oatehexenal Z (85hex ol, 141eth l hept oatcar e (

eth 3-hy oxyeth oct teeth l phenylacedam scdod ena 140dod ano aci

pec sco ) ete ion T e LRI

2 23 13 14 74 64 25 85 6

1 29

1 11 1

R nt im of ZB L C po o arbo x tat Identificatio RI.8 1 yl et utano (.2 5 -3 45).7 7 -2 28).1 yl et op (751).2 8 yl an (.9 -3 3).5 6 an 2 ( 7).9 2 y an e

6.5 1 en 1011)7.3 0 linalool (1552)8.7 1 yl dr he e (1134)9.6 1 yl anoa (11. 1 y ta

11. 9 a enone (183. 1 ec l ( 7)3.6 1 ec ic d (

n (Late

anoate1242)

(1346)

xanoat195)te27)

1568)

68

Retention index values are useful in compound identification when LRI values are

compared across different columns and/or different chromatographic conditions. Several

factors affect the retention time of an analyte in a chromatographic run such as column

length, carrier

gas flow, film thickness of the column, column packaging, and

compound, however, do not affect their retention indices, because these are relative

values, calculated from alkane standards run at the same chromatographic conditions.

temperature program. All these factors significantly affect the retention time of a

Table 4-6: Average Integration Areas of Identified Compounds in Fresh and CO2 Processed Beer

69

Identified Compounds Average Fresh Integration Areas Average CO2 Integration Areas Fresh-CO2 Integration Areas % change

ethyl hexanoate 15083 7744 7339 48.66ethyl heptanoate 534903 532768 2136 0.40ethyl octanoate 274993 254857 20136 7.32ethyl phenylacetate 17668 19087 -1419 -8.03dodecenal 28457 30530 2073 -7.28-dodecanoic acid 139467 144960 -5493 -3.94

70

When comparing the relationship between the GC-FID chromatogram (Figure 4-

15) and the aromagram peak intensities for each compound (Figure 4-16), one can

observe that aroma active components that may drive the aroma profile either did not

have a response at all (peaks with RT= 4.17, 4.92, 6.18 min) or had a relatively

response (peaks with RT= 6.53, 9.62, 13.3, 13.61) in the FID detector. The latter

observation likely occurred due to the presence of these compounds in lower quantiti

to their poor response and specific interactions with the FID detector, or have lower

low

es,

threshold values. Moreover, compounds that have relatively high peak intensities to the

FID detector can be odorless compounds that do not contribute to the typical key notes of

the sample and thus they can not be detected by the assessor while conducting the GC-O

sniffing. However, an assessor can also not identify a specific aroma compound due to

selective anosmia or due to the low sensory threshold of the specific compound.

Figure 4-15: Typical FID Chromatogram for Fresh Beer

Figure 4-16: Typical Aromagram for Fresh Beer

71

The data produced by GC-O has a qualitative component in which the assessor

describe perception. This usually involves association of the precept

with a word or group of words in a lexicon. Qualitative GC-O data are either

measure ent of odor potency or perceived intensity plotted against a retention index

(Fried and Acree, 2000). The major advantage of using GC-O is the ability to detect

the pr ce of aroma impact components. One does not need to know ahead

of tim h components to measure. Using this technique many highly potent aroma

etected that would have been missed simply because they were

ch minute concentrations. However, GC-O has limitations since it can not

gonistic interactions from other aroma active components in

, 2000).

reduce experimental error and variation, several factors can be monitored

and sample temperature, time of day, duration of

repeated standardization of sniffers, and use of a standard

at people can be trained to consistently identify smells if

e ically and trained to sniff with standard chemicals and

larie precis of threshold detection of an individual makes it more difficult

odor experience than the beginning of the

e variation to intensity ratings (Rouseff and Naim, 2000;

although some stripping did occur, no

etween fresh and CO2 processed beer samples. This

the sensory panels, can be used to support the case

s the nature of their

m

rich

esen

e whic

ine synergistic or anta

unexpected

impact compounds are d

present at su

determ

the sam

including sample preparation, room

analysis, repetition of analysis,

lexicon. It is well documented th

they

voc

for the assessor to detect the end of an

experience, lending som

Friedrich and Acree, 2000).

appreciable changes were detected b

finding, along with the results from

p

To

le (Rouseff and Naim

ar

abu

standa

s. T

rdiz

he

ed period

ion

Overall, it can be concluded that

72

for den

ing

in

is

ion

Department, University of Florida.

The attribute eliciting the largest influence on purchase decision was price, which

was 53.9% of a consumer’s purchase decision. Overall, consumers preferred paying

$6.00/six 12 ounce bottles (average rank=3.705), rather than $8.00/6 12 ounce bottles

(average rank=5.279). The next most influential attribute was beer flavor, being 34% of

or with an average rank of

4.003, compared to bottled beer flavor with an average rank of 4.995. Finally, beer shelf

life showed the least effect on beer purchase decision, being 12.1% of the purchase

decision. Consumers preferred shelf stable beer with an average rank of 4.321 over beer

that required refrigeration that had an average rank of 4.674.

This conjoint analysis gives insight into characteristics of the beer that influence

which beer a consumer will purchase. This information is useful when introducing a new

se-phase CO2 as an alternative to heat pasteurization of beer. Furthermore,

because of beer’s complex mix of flavors, the minimal stripping that does occur dur

dense-phase CO2 pasteurization is easily masked by other flavor compounds, resulting

a final product no different from fresh beer.

Conjoint Analysis of Beer Purchase Decisions

A conjoint analysis is used to quantify how important a specific characteristic is of

a product in a consumer’s purchase decision (Table 4-7). A conjoint analysis of beer

purchase decisions was conducted to elucidate how flavor, shelf life requirements, and

price affect purchase decision. Two levels of each attribute were tested, creating a full 23

factorial. Panelists were asked to rank 8 descriptions of beers in the order in which they

would purchase the beers. A sample ballot is available in Appendix B and ranking data

available by contacting Dr. Murat Balaban in the Food Science and Human Nutrit

the purchase decision. Consumers preferred draft beer flav

73

produc r

it’s

t to an existing market, as in the case of introducing dense-phase pasteurized bee

to the current beer market. Using the data from the conjoint analysis, dense-phase CO2

pasteurized beer would successful because of its draft beer taste, coupled with

extended shelf life. However, if the cost of using dense-phase CO2 increased the price of

the beer significantly, consumer may opt not to purchase the product. Having seen that

price is 53.9% of the purchase decision, it would be in the best interest to minimize or

prevent a price increase by lowering the cost of production by recycling CO2 during

processing and eliminating cold storage during transit for the product.

74

Table 4-7: Conjoint Analysis Transformation

FaFlaDraBot dSto geRef gerSh StaPri

$$

ctor Lev

Average Rank of Le

Deviation from Overall Average

nkReversed

iationSquared

via nStandardized

e ioEstimated Part-

rt

Range of Part-W orth

Factor por ce

vorft 03 -0 7 97 24 28 0 0 5 .0tle 95 0 5 - 95 24 - 19 -0 5rarid ate 21 -0 9 79 03 07 0 7 5 .1elf ble 74 0 4 - 74 03 - 01 -0 8ce

6. 05 -0 5 95 63 14 1 4 9 .98. 79 0 9 - 79 60 - 31 -1 5

s Im tan

1.81 34 %

0.64 12 %

2.87 53 %

De tio D viat n W o h

0. 7 0.8 .910. 5 0.8 .90

0. 2 0.1 .320. 0 0.1 .31

0. 2 2.1 .450. 7 2.0 .42

el vel Ra Dev

4.0 .49 0.44.9 .49 0.4

d 4.3 .17 0.14.6 .17 0.1

00 3.7 .79 0.700 5.2 .77 0.7

CHAPTER 5

g in a 5-log or higher decrease in yeast populations.

2 or essing and after

30 days of storage at 1.67°C.

• Beer haze was significantly reduced by dense-phase CO2 pasteurization.

• Beer foam capacity and stability were affected by dense-phase CO2 pasteurization, however not to the detriment of the finished product’s quality.

• Beer purchase decision is most affected by price, then beer flavor, and finally shelf life requirements. Consumers prefer shelf stable, draft beer and are adverse to an increase in price.

In general, a continuous dense-phase CO2 system was effective in the

pasteurization of beer. The success of this system in the brewing industry would rely on

its predicted 7.38 log reduction in yeast populations, while preserving fresh beer aroma,

flavor, foaming capacity and stability, and aiding in the reduction of beer haze. By

resulting in an extended shelf life dense-phase CO pasteurization is a formidable

alternative to heat pasteurization, and would be preferred to heat pasteurization because

of its ability of maintain fresh beer characteristics. The ability to produce a clear,

consistently fresh tasting beer that forms a good head, with an extended shelf life is top-

priority to brewers. Dense-phase CO2 pasteurization can make this possible.

In addition, it is predicted that consumer acceptability of this technology in the

brewing industry would be high based on consumers’ priorities when it comes to beer

CONCLUSION

The conclusions of this study are the following:

• Dense-phase CO2 pasteurization is effective in the pasteurization of beer, resultin

• Although some stripping did occur during dense-phase CO pasteurization, flavand aroma changes detected by panelists were negligible after proc

75

76

characteristics. Consumers prefer a beer that does not have to be refrigerated and that has

draft beer taste; however they would be adverse to an increase in beer prices. If brewers

concentrated on cost-effective processing techniques, such as recycling CO2 throughout

the brewery and elimination of the cold storage of product, dense-phase CO2

pasteurization could be used to create a product with extended shelf life and draft beer

taste with no increase in price.

Indications of the mode of cell death were absorption of CO2 into the cell

membrane creating a physical disruption in membrane structure, visible as divots in

scanning electron microscopy pictures.

It is recommended that more research be conducted to elucidate the mode of cell

death and the role of alcohol as a co-solvent in yeast death by dense-phase CO2

pasteurization . Further research in the area of beer flavor stripping would also lend to

the application of this technology to beverages in general. Recycling of the CO2 stream

and recovery of stripped volatiles would also be valuable research topics.

APPENDIX A RAW ATA EXPERIMENT D

78

Table A-1: Yeast Counts for 27 Treatment Combination on D ic

s D e in upl ate

B A B ve To Col0 00 00 00 00 00 00 00 00 00 00 00 0

5 9 3 6 16.70 0.5 0.0 0.5 0.0 0 0 00 0 0 0

0.242 22 11.2

1 0.5 0.20 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 0

Treatment DilutionA A Ave A B ve A tal oni ma L edu log )

1 1 0 0 0 0 0 0 6.12 0 0 0 0 03 0 0 0 0 04 0 0 0 0 05 0 0 0 0 06 0 0 0 0 0

2 1 0 0 0 0 0 0 6.12 0 0 0 0 03 0 0 0 0 04 0 0 0 0 05 0 0 0 0 06 0 0 0 0 0

3 1 3 52 27. 5 675 2.82 1 0 0.5 1 53 1 0 0.5 1 54 0 0 05 0 0 06 0 1 0.5 0 0 0 5

4 1 0 1 0.5 2 5 125 3.02 0 0 0 0 53 0 cont 04 0 0 05 0 0 06 0 0 0

5 1 0 0 0 0 6.12 0 0 03 0 0 04 0 0 05 0 0 06 0 0 0

ction (no/n106

106

866

594

106

es Re ining og R.00

.00

1 .00

1 .00

.00

79

Table A-1: Continued Treatment Dilution

A A Ave A B B Ave B Ave Total Colonies Remaining Log Reduction log(no/n)6 1 0 0 0 0 2 1 0.5 50.00 4.4116

2 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

7 1 4 5 4.5 5 8 6.5 5.5 550.00 3.37022 1 1 1 1 0 0.5 0.753 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 cont 0 0 0 0 06 0 0 0 0 0 0 0

8 1 1 1 1 1 2 1.5 1.25 125.00 4.012 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 1 0.5 0 0 0 0.25

9 1 0 0 0 0 0 0 0 0.00 6.112 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 1 0 0.5 0 0 0 0.256 0 0 0 0 0 0 0

10 1 0 0 0 0 1 0.5 0.25 25.00 4.712 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

11 1 5 0 2.5 0 0 0 1.25 125.00 4.01372 0 0 0 0 0 0 03 0 0 0 0 0 0 04 1 0 0.5 0 0 0 0.255 1 0 0.5 0 0 0 0.256 0 1 0.5 0 0 0 0.25

12 1 0 0 0 2 2 2 1 100.00 4.11062 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 1 0.5 0.255 0 0 0 0 0 0 06 0 0 0 0 0 0 0

37

06

26

80

Table A-1: Continued Treatment Dilution

otal Colonies Remaining Log Reduction log(no/n)0 0.00 6.1106

2 0 1 0.5 0 0 0 0.253 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

21 1 8 cont 8 5 12 8.5 8.25 825.00 3.19412 0 0 0 1 0 0.5 0.253 0 0 0 0 1 0.5 0.254 0 0 0 0 0 0 05 0 1 0.5 0 0 0 0.256 0 0 0 0 0 0 0

22 1 0 0 0 0 1 0.5 0.25 25.00 4.71262 0 1 0.5 0 0 0 0.253 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

23 1 1 4 2.5 4 3 3.5 3 300.00 3.63352 2 0 1 1 0 0.5 0.753 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 1 0 0.5 0.256 0 0 0 0 0 0 0

24 1 0 1 0.5 0 0 0 0.25 25.00 4.71262 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

25 1 1 0 0.5 0 0 0 0.25 25.00 4.71262 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 2 0 1 0.56 0 0 0 0 0 0 0

26 1 cont cont #DIV/0! 0 0 0 #DIV/0! 0.00 6.11062 cont cont #DIV/0! 0 0 0 #DIV/0!3 0 cont 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

A A Ave A B B Ave B Ave T20 1 0 0 0 0 0 0

Table A-1: Continued Treatment Dilution

A A Ave A B B Ave B Ave Total Colonies Remaining Log Reduction log(no/n)20 1 0 0 0 0 0 0 0 0.00 6.1106

2 0 1 0.5 0 0 0 0.253 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

21 1 8 cont 8 5 12 8.5 8.25 825.00 3.19412 0 0 0 1 0 0.5 0.253 0 0 0 0 1 0.5 0.254 0 0 0 0 0 0 05 0 1 0.5 0 0 0 0.256 0 0 0 0 0 0 0

22 1 0 0 0 0 1 0.5 0.25 25.00 4.71262 0 1 0.5 0 0 0 0.253 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

23 1 1 4 2.5 4 3 3.5 3 300.00 3.63352 2 0 1 1 0 0.5 0.753 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 1 0 0.5 0.256 0 0 0 0 0 0 0

24 1 0 1 0.5 0 0 0 0.25 25.00 4.71262 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

25 1 1 0 0.5 0 0 0 0.25 25.00 4.71262 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 2 0 1 0.56 0 0 0 0 0 0 0

26 1 cont cont #DIV/0! 0 0 0 #DIV/0! 0.00 6.11062 cont cont #DIV/0! 0 0 0 #DIV/0!3 0 cont 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

81

Table A-1: Continued Treatment Dilution

A A Ave A B B Ave B Ave Total Colonies Remaining Log Reduction log(no/n)27 1 0 0 0 0 0 0 0 0.00 6.1106

2 0 0 0 1 2 1.5 0.753 0 0 0 1 0 0.5 0.254 0 0 0 0 0 0 05 0 1 0.5 1 0 0.5 0.56 0 0 0 0 0 0 0

82

Treatment DilutionA A Ave A B B Ave B Ave Total Colonies Remaining Log Reduction log(no/n)

1 1 0 0 0 0 0 0 0 0.00 6.05692 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

2 1 0 0 0 0 0 0 0 0.00 6.05692 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

3 1 5 7 6 9 7 8 7 700.00 3.21182 1 1 1 0 0 0 0.53 0 1 0.5 0 1 0.5 0.54 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

4 1 1 1 1 5 12 8.5 4.75 475.00 3.38022 0 1 0.5 1 0 0.5 0.53 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

Table A-1: Continued

83

Treatment DilutionA A Ave A B B Ave B Ave Total Colonies Remaining Log Reduction log(no/n)

5 1 0 0 0 0 0 0 0 0.00 6.05692 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

6 1 0 2 1 0 1 0.5 0.75 75.00 4.18182 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

7 1 6 7 6.5 4 6 5 5.75 575.00 3.29722 1 1 1 1 1 1 13 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 cont 0 0 0 0 06 0 0 0 0 0 0 0

8 1 2 1 1.5 2 2 2 1.75 175.00 3.81392 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 1 0.5 0 0 0 0.25

9 1 0 0 0 0 0 0 0 0.00 6.05692 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 1 0 0.5 0 0 0 0.256 0 0 0 0 0 0 0

Table A-1: Continued Treatment Dilution

A A Ave A B B Ave B Ave Total Colonies Remaining Log Reduction log(no/n)10 1 1 1 1 0 1 0.5 0.75 75.00 4.1818

2 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

11 1 3 4 3.5 1 0 0.5 2 200.00 3.75592 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 2 1 0 0 0 0.55 1 0 0.5 0 0 0 0.256 0 0 0 0 0 0 0

12 1 1 2 1.5 2 2 2 1.75 175.00 3.81392 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

13 1 1 1 1 2 1 1.5 1.25 125.00 3.96002 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

14 1 1 1 1 2 1 1.5 1.25 125.00 3.96002 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

15 1 1 0 0.5 0 1 0.5 0.5 50.00 4.35792 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

84

Table A-1: Continued Treatment Dilution

A A Ave A B B Ave B Ave Total Colonies Remaining Log Reduction log(no/n)16 1 1 1 1 0 1 0.5 0.75 75.00 4.1818

2 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

17 1 2 4 3 6 6 6 4.5 450.00 3.40372 1 0 0.5 0 1 0.5 0.53 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

18 1 0 2 1 2 3 2.5 1.75 175.00 3.81392 0 0 0 0 1 0.5 0.253 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

19 1 0 0 0 0 0 0 0 0.00 6.05692 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

20 1 0 0 0 0 0 0 0 0.00 6.05692 0 1 0.5 0 0 0 0.253 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

21 1 8 7 7.5 4 5 4.5 6 600.00 3.27882 0 0 0 1 0 0.5 0.253 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

85

Table A-1: Continued Treatment Dilution

A A Ave A B B Ave B Ave Total Colonies Remaining Log Reduction log(no/n)22 1 1 1 1 0 1 0.5 0.75 75.00 4.1818

2 0 1 0.5 0 0 0 0.253 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

23 1 2 2 2 3 5 4 3 300.00 3.57982 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

24 1 1 1 1 0 0 0 0.5 50.00 4.35792 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

25 1 0 1 0.5 0 0 0 0.25 25.00 4.65902 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 2 0 1 0.56 0 0 0 0 0 0 0

26 1 0 0 0 0 0 0 0 0.00 6.05692 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

27 1 0 0 0 0 0 0 0 0.00 6.05692 0 0 0 0 0 0 03 0 0 0 0 0 0 04 0 0 0 0 0 0 05 0 0 0 0 0 0 06 0 0 0 0 0 0 0

86

87

Table A-2: Foam and Liquid Volumes A fter P rocess ing A fter S torage

Treatm ent foam volum e liquid volum e foam volum e liquid volum eFresh 75 10 81 14Fresh 76 10 81 14Fresh 75 11 83 14Fresh A verage 75.33333333 10.33333333 81.66666667 1426.7 M P a 80 12 78 1426.7 M P a 81 12 76 1426.7 M P a 82 12 76 1526.7 M P a A verage 81 12 76.66666667 14.3333333320.7 M P a 78 12 78 1420.7 M P a 82 12 76 1620.7 M P a 81 12 76 1420.7 M P a A verage 80.33333333 12 76.66666667 14.66666667Heated 84 10 81 12Heated 83 10 84 10Heated 83 10 84 12

10 83 11.33333333

Bottom Gel: 18% cross-linked Lanes from Left to Right:

Same as Above

Heated A verage 83.33333333

lamide Gels Figure A-1: Polyacry

Top Gel: 15% cross-linked Lanes from Left to Right:

1. Heated Beer 2. 20.7 MPa 3. 27.6 MPa 4. Fresh Beer 5. Heated Beer 6. 20.7 MPa 7. 27.6 MPa 8. Fresh Beer 9. Molecular

Weight Marker

88

APPENDIX B SENSORY AND CONJOINT BALLOTS

Figure B-1: Sam

ple Sensory Ballot

89

Beer Purchase Survey

Read each of the descriptions below, then rank them in the order in which you would purchase the beers. For instance, the beer you would most likely purchase would be ranked #1 and the beer you are least likely to purchase would be #8. There can be no ties. Beer Descriptions Rankings Bottled Beer Taste Must be refrigerated $8.00/ six 12 oz. bottles Draft Beer Taste Shelf Stable $6.00/ six 12 oz. bottles Bottled Beer Taste Shelf Stable $6.00/ six 12 oz. bottles Draft Beer Taste Must be refrigerated $6.00/ six 12 oz. bottles Draft Beer Taste Must be refrigerated $8.00/ six 12 oz. bottles Draft Beer Taste Shelf Stable $8.00/ six 12 oz. bottles Bottled Beer Taste Shelf Stable $8.00/ six 12 oz. bottles Bottled Beer Taste Must be refrigerated $6.00/ six 12 oz. bottles

Figure B-2: Sample Ballot for Conjoint Analysis

APPENDIX C STATISTICAL MATERIAL

Response Surface SAS Code

data firstmicro; input X1 X2 X3 X4 Y; u1=27.6; u2=35; u3=10; u4=5; v1=6.9; v2=10; v3=2; v4=1; z1=(X1-u1)/v1; z2=(X2-u2)/v2; z3=(X3-u3)/v3; z4=(X4-u4)/v4; cards; 27.6 21 10 5 6.1106 20.7 25 8 4 6.1106 34.5 25 8 4 6.1106 20.7 25 8 6 6.1106 34.5 25 8 6 4.7126 20.7 25 12 4 6.1106 34.5 25 12 4 4.4116 20.7 25 12 6 4.2355 34.5 25 12 6 4.2355 27.6 35 7.175 5 3.1941 27.6 35 10 3.56 3.6335 17.8 35 10 5 3.4574 27.6 35 10 5 4.7126 27.6 35 10 5 6.1106 27.6 35 10 5 6.1106 37.3 35 10 5 3.9345 27.6 35 10 6.41 4.7126 27.6 35 12.83 5 4.7126 20.7 45 8 4 2.8866 34.5 45 8 4 3.0594 20.7 45 8 6 4.0137 34.5 45 8 6 4.1106 20.7 45 12 4 3.3702 34.5 45 12 4 4.0137 20.7 45 12 6 4.4116 34.5 45 12 6 4.4116 27.6 49 10 5 6.1106 27.6 21 10 5 6.0569 20.7 25 8 4 6.0569 34.5 25 8 4 6.0569

90

91

20.7 25 8 6 6.0569 34.5 25 8 6 4.1818 20.7 25 12 4 6.0569 34.5 25 12 4 4.1818 20.7 25 12 6 3.96 34.5 25 12 6 3.96 27.6 35 7.175 5 3.2788 27.6 35 10 3.56 3.5798 17.8 35 10 5 3.4037 27.6 35 10 5 4.6590 27.6 35 10 5 6.0569 27.6 35 10 5 6.0569 37.3 35 10 5 3.8139 27.6 35 10 6.41 4.3579 27.6 35 12.83 5 4.1818 20.7 45 8 4 3.2118 34.5 45 8 4 3.3802 20.7 45 8 6 3.7559 34.5 45 8 6 3.8139 20.7 45 12 4 3.2972 34.5 45 12 4 3.8139 20.7 45 12 6 4.3579 34.5 45 12 6 4.1818 27.6 49 10 5 6.0569 ; proc print data=firstmicro;run; proc sort data=firstmicro; by X1 X2 X3 X4; run; proc rsreg data=firstmicro; model y=x1 x2 x3 x4; ridge max; run;

Example Code for ANOVA and Tukey’s Test

PROC IMPORT OUT= WORK.taste_031804 DATAFILE= "C:\Documents and Settings\GFolkes\Desktop\Sensory Results\031804 results SAS taste.xls" DBMS=EXCEL2000 REPLACE; SHEET="Sheet1$"; GETNAMES=YES; RUN; proc print; run; proc sort; by Samp_Set Samp_ Samp_Code Taste_Difference; proc glm; class Samp_Set Samp_ Samp_Code; model Taste_Difference=Samp_Set Samp_ Samp_Code; means Samp_/tukey alpha=.01; run;

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BIOGRAPHICAL SKETCH

Gillian Folkes was born in 1978 in Tampa, FL, to Edward V. Folkes, Jr. and Gilda

Folke of

Florida as a National Merit Scholar and graduated in May of 2000 with a bachelor’s

degre

following fall, Gillian started her Ph.D. program under the instruction of Dr. Murat

Balab arrying Roi Dagan in May of 2004, Gillian will

2004, orking for ABC Research Corporation.

s. After graduating high school as salutatorian, Gillian attended the University

e in food science with honors and a minor in business administration. The

an as an Alumni Fellow. After m

receive her Ph.D. in food science and a minor in food resource economics in August

and will continue to reside in Gainesville, w

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