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

Abstract

This study designs 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 are recorded by matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF‐MS). Seven peptides are selected as putative markers, and cutoff levels and performance measures of each marker are defined by receiver operating characteristic (ROC) analysis. The accuracy of these peptides in the training set range between 71% and 90%. A prediction model is 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 is predicted correctly achieving 95% accuracy. Five peptides of the prediction model are 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 demonstrates that the milk peptide profile reflects even minor differences in production parameters.

Biomolecular Profiling by MALDI-TOF Mass Spectrometry in Food and Beverage Analyses

“…The best candidate for the detection of expired UHT milk samples, β-casein f192-206, provided a 100% sensitivity (true positive rate) and specificity (true negative rate), which was evaluated in a blind test (100% accuracy rate) [ 43 ]. Similarly, seven peptide markers differentiating between pasteurized and extended shelf life (ESL) milk were identified by MALDI-TOF MS profiling and relative quantification followed by statistical evaluations to define their cutoff levels for a prediction model [ 44 ]. The model showed an accuracy of 95% with test samples.…”

A systematic approach to identify novel marker peptides for the heat treatment and storage of milk

“…Therefore, a prediction model was designed using the best seven peptides after ROC analysis. Although the accuracy values of the single peptides were between 71 and 90%, integration of those peptides resulted in the prediction of 19 out of 20 unknown milk samples correctly, achieving 95% accuracy [3].…”