Design of a Prediction Model for the Differentiation of Pasteurized Milk from Heated ESL Milk by Peptide Profiling.

Dalabasmaz S, Pischetsrieder M (2019)


Publication Status: Published

Publication Type: Journal article

Publication year: 2019

Journal

Book Volume: 19

Pages Range: e1800292

Journal Issue: 7

DOI: 10.1002/pmic.201800292

Abstract

This study designed a prediction model to differentiate pasteurized milk from heated extended shelf life (ESL) milk based on milk peptides. For this purpose, quantitative peptide profiles of a training set of commercial samples including pasteurized (n=20), pasteurized-ESL (n=13) and heated-ESL (n=16) milk were recorded by MALDI-TOF-MS. Seven peptides were selected as putative markers, and cutoff levels and performance measures of each marker were defined by ROC analysis. The accuracy of these peptides in the training set ranged between 71 and 90%. A prediction model was established based on the combined cutoff levels and evaluated by an independent blind test set. The processing method of 19 out of 20 unknown milk samples was predicted correctly achieving 95% accuracy. Five peptides of the prediction model were identified as αS1-casein182-199 (m/z 2014.0), αS1-casein180-199 (m/z 2216.1), αS1-casein1-24 (m/z 2910.6), β-casein108-125 (m/z 2126.0), and β-casein106-125 (m/z 2391.2) indicating thermal release and the action of plasmin and cathepsins. Thus, the present study demonstrated that the milk peptide profile reflects even minor differences in production parameters.

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How to cite

APA:

Dalabasmaz, S., & Pischetsrieder, M. (2019). Design of a Prediction Model for the Differentiation of Pasteurized Milk from Heated ESL Milk by Peptide Profiling. Proteomics, 19(7), e1800292. https://dx.doi.org/10.1002/pmic.201800292

MLA:

Dalabasmaz, Sevim, and Monika Pischetsrieder. "Design of a Prediction Model for the Differentiation of Pasteurized Milk from Heated ESL Milk by Peptide Profiling." Proteomics 19.7 (2019): e1800292.

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