Fingerprinting applications equivalency testing of diets

8/7/2019 Fingerprinting applications equivalency testing of diets

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Fingerprinting applications for

equivalency testing of diets/ foodproducts

More information = better

equivalency testing


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What is fingerprinting

Target compound analysis, group type

characterization

Different samples = different properties =

different spectra/ chromatograms =

different fingerprint


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What is essential (1)


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Essentials (3)

Statistical techniques to discrimminatebetween (many)samples

Classification and similarity basedapproach

Different techniques for differentapplications to generate information


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Quality assessment of Curcuma

Longa by GC-MS 33 different batches/ samples of C.L.

(plant) originating from different

geographical locations, different cultivarsand different harvesting times

Goal: To establish a fingerprint method for

quality control


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Development a fingerprint method

for Ganoderma Lucidum by LCMS 29 G. L. (mycelia) samples from three

different provinces and six different origins

Dried samples were extracted withchloroform and analyzed by RPLC with

MS detection


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Type of analysis

Validation: RSD of the precision was 0,43% and

2,9% (n=6), based on RRT and RPA.

Similarity analysis by using simulated mean

chromatograms

Calculating characteristic values fordiscrimmination of chromatographic fingerprints


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Similarity analysis of all 29 peaks


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Line chart for visual assessment


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TPA a characteristic value


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Characteristic

ratios


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Assessment of 60 samples of G.L.

from six origins(10 each) Bigger sample size allows for a moreextensive analysis.

The abillity to use other statisticaltechniques to discrimminate betweensamples

No pre-processing of data because ofRPA and RRT


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Similarity and HCA


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PCA

Discrimmination of 3 groups by province


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PCA of log-centered data

Extra group found

because of different

cultivation method

(b) circled items are

possible markers


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PLS-DA to confirm

Loadingsplot indicates

6 marker compounds

Leave one out method:


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SIMCA

Confirm classification Coomans plot:

New samples can

be tested

Every considered

sample is assigned

to one class accordingto its distance from

the class model.


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Marker method

Identical results when

analysis is repeated with

4 peaks instead of 19


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2D GCxGC-MS for analyzing olive

oils

Three different types of olive oils subjected

to 2 different types of extraction processes

Aim to compare the results obtained with

RPAs and statistical image treatment

(imageJ)


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2D fingerprint

Separation: 1D- volatility 2D-polarity

Size and intensity translated to pixel data/ sector


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ANOVA after Tukey validation (loss

of significant figures)


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very similar results


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In conclusion

ImageJ software allows for virtually

identical results

Selection of marker compounds give even

more discrimmination capacity

Be carefull some information may still be

lost


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GCxGC-MS of coffee and hazelnut

Goal: Comparison of data processing

techniques: group-type characterization,

direct image comparison and template

matching

Discrimminate between botanical and

geographical origin and roasting

treatments


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Evaluation of different columns sets


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Group-type analysis

Discrimmination:

geographical location,

roasting treatment


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Direct image comparison

Good for searching

discrimminate analytes

- Distortion effects


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Template matching


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Analysis Lingusticum Chuanxion

Utilizing 2 fingerprints per sample for

analysis

RPLC fingerprint for moderate and weak

polar compounds

HILIC fingerprint for strong polar

compounds


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Fingerprints


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Validation with RRT and RPA


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Similarity analysis


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Fingerprinting applications equivalency testing of diets

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