Use of MALDI-TOF MS technology to evaluate adulteration of small ruminant milk with raw bovine milk

Abstract

Detection of adulteration of small ruminant milk is very important for health and commercial reasons. New analytical and cost-effective methods need to be developed to detect new adulteration practices. In this work, we aimed to explore the ability of the MALDI-TOF mass spectrometry to detect bovine milk in caprine and ovine milk using samples from 18 dairy farms. Different levels of adulteration (0.5, 1, 5, 10, 20, 40, 60, and 80%) were analyzed during the lactation period of goat and sheep (in May, from 60 to 90 d in milk, and in August, from 150 to 180 d in milk). Two different ranges of peptide-protein spectra (500-4,000 Da; 4-20 kDa) were used to establish a calibration model for predicting the concentration of adulterant using partial least squares and generalized linear model with lasso regularization. The low molecular weight part of the spectra together with the generalized linear model with lasso regularization regression model appeared to have greater potential for our aim of detection of adulteration of small ruminants' milk. The subsequent prediction model was able to predict the concentration of bovine milk in caprine milk with a root mean square error of 11.4 and 17.0% in ovine milk. The results offer compelling evidence that MALDI-TOF can detect the adulteration of small ruminants' milk. However, the method is severely limited by (1) the complexity of the milk proteome resulting from the adulteration technique, (2) the potential degradation of thermolabile proteins, and (3) the genetic variability of tested samples. Additionally, the root mean square error of prediction based only on one individual sample adulteration series can drop down to 6.34% for quantification of adulterated caprine milk and 6.28% for adulterated ovine milk for the full set of concentrations or down to 2.33 and 4.00%, respectively, if we restrict only to low concentrations of adulteration (0, 0.5, 1, 5, 10%).

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

“…Spectral peaks with the highest variable importance for the prediction scores included, for example, those of α-lactalbumin, β-lactoglobulin, and γ 2 - and γ 3 -casein [ 48 ]. A more recent paper on caprine and ovine milk adulteration with bovine milk also relied on MALDI-TOF MS and multivariate statistical analyses [ 49 ]. The analyzed samples originated from different geographical regions and were sampled in two lactation periods.…”

How Chemometrics Can Fight Milk Adulteration

“…Least squares support vector machine (LS-SVM) [110] was applied on both NIR and dielectric spectroscopic data to quantify mature bovine milk in colostrum samples [111] and on MIR data to assess cheese whey in bovine milk [38], providing better results than PLS. A generalized linear model with lasso regularization (GLM-Lasso) [112] coupled with MALDI-TOF mass spectroscopy provides better results than PLS too, in this case, to detect bovine milk in caprine and ovine milk [113]. Ehsani et al applied boosted regression tree (BRT) [114] on NIR spectra collected by a portable spectrometer for a fast water quantification in cow's milk [115].…”

“…In some cases, they provide slightly better results than PLS, but in many other cases, the results are comparable. Ovine milk and caprine milk Cow milk MALDI-TOF-MS PLS, GLM-Lasso [113] Cow milk Water NIR (portable) BRT [115] Cow milk Water Digital image analysis XGBoost [116] Abbreviations: ATR = attenuated total reflection, BP-ANN = back propagation artificial neural networks, BRT = boosted regression trees, GA-PLS = genetic-algorithm partial least squares, GLM-Lasso = generalized linear model with lasso regularization, GR-NN = generalized regression neural networks, HSI = hyperspectral imaging, LS-SVM = least squares support vector machine, MALDI-TOF-MS = matrix-assisted laser desorption ionization time-of-flight mass spectrometry, MCR-ALS = multivariate curve resolution alternating least squares, MIR = mid-infrared, MLR = multiple linear regression, NIR = near-infrared, OPLS = orthogonal partial least squares, PARAFAC = parallel factor analysis, PCR = principal component regression, PLS = partial least squares, siPLS = synergy interval partial least squares, TD-NMR = time-domain nuclear magnetic resonance, U-PLS/RBL = unfolded partial least squares with residual bilinearization, Vis = visible, XGBoost = extreme gradient boosting.…”