Quantification of major milk proteins using ultra-performance liquid chromatography tandem triple quadrupole mass spectrometry and its application in milk authenticity analysis

Discrimination of overheated pasteurized milk using mass spectrometry-based proteomics

Milk is one of the most widely consumed foods globally. To protect consumer interests, it is essential to establish an analytical method to detect the degree of heating in milk. A novel approach using nano liquid chromatography-orbitrap fusion mass spectrometer was developed for screening and identifing thermally sensitive peptides markers in the milk heating process (below 100 °C). This method integrates untargeted proteomics and chemometric tools to analyze protein quantitation data from differently heat-treated milk. Thirteen potential markers were screened out and identified, and further confirmed using by standard substances. Then, the accurate concentrations of 13 potential markers determined by isotope-dilution ultra-performance liquid chromatography-tandem triple quadrupole mass spectrometry were further mining the highly specific and thermally sensitive peptides markers. And Four peptides-INLFDTPLETQYVR, FELLGCELNGCTEPLGLK, QFQFIQVAGR, and GEADALNLDGGYIYTAGK-were selected as marker peptides to differentiate normal pasteurized milk from overheated pasteurized milk. The concentrations of INLFDTPLETQYVR ranges from 150 ± 11 µg/L to 350 ± 23 µg/L, while the concentrations of FELLGCELNGCTEPLGLK ranges from 40 ± 5 µg/L to 92 ± 3 µg/L, can distinguish normal pasteurized milk from overheated pasteurized milk. QFQFIQVAGR indicates overheated pasteurized milk at 230 ± 21 µg/L, and GEADALNLDGGYIYTAGK signifies 750 ± 43 µg/L. This study provides new insights for distinguishing overheated pasteurized milk.

Machine learning-assisted MALDI-TOF MS toward rapid classification of milk products

This study established a method for rapid classification of milk products by combining matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis with machine learning techniques. The analysis of 2 different types of milk products was used as an example. To select key variables as potential markers, integrated machine learning strategies based on 6 feature selection techniques combined with support vector machine (SVM) classifier were implemented to screen the informative features and classify the milk samples. The models were evaluated and compared by accuracy, Akaike information criterion (AIC), and Bayesian information criterion (BIC). The results showed the least absolute shrinkage and selection operator (LASSO) combined with SVM performs best, with prediction accuracy of 100 ± 0%, AIC of -360 ± 22, and BIC of -345 ± 22. Six features were selected by LASSO and identified based on the available protein molecular mass data. These results indicate that MALDI-TOF MS coupled with machine learning technique could be used to search for potential key targets for authentication and quality control of food products.

Preparation and application of UPLC silica microsphere stationary phase:A review

In this review, microspheres for ultra-performance liquid chromatography (UPLC) were reviewed in accordance with the literature in recent years. As people's demands for chromatography are becoming more and more sophisticated, the preparation and application of UPLC stationary phases have become the focus of researchers in this field. This new analytical separation science not only maintains the practicality and principle of high-performance liquid chromatography (HPLC), but also improves the step function of chromatographic performance. The review presents the morphology of four types of sub-2 μm silica microspheres that have been used in UPLC, including non-porous silica microspheres (NPSMs), mesoporous silica microspheres (MPSMs), hollow silica microspheres (HSMs) and core-shell silica microspheres (CSSMs). The preparation, pore control and modification methods of different microspheres are introduced in the review, and then the applications of UPLC in drug analysis and separation, environmental monitoring, and separation of macromolecular proteins was presented. Finally, a brief overview of the existing challenges in the preparation of sub-2 μm microspheres, which required further research and development, was given.

Identification of Marker Peptides for the Whey Protein Quantification in Edam-Type Cheese

Several technologies are available for incorporating whey proteins into a cheese matrix. However, there is no valid analytical method available to determine the whey protein content in matured cheese, to date. Consequently, the aim of the present study was to develop a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the quantification of individual whey proteins based on specific marker peptides ('bottom-up' proteomic approach). Therefore, the whey protein-enriched model of the Edam-type cheese was produced in a pilot plant and on an industrial scale. Tryptic hydrolysis experiments were performed to evaluate the suitability of identified potential marker peptides (PMPs) for α-lactalbumin (α-LA) and β-lactoglobulin (β-LG). Based on the findings, α-LA and β-LG appeared to be resistant to proteolytic degradation during six weeks of ripening and no influence on the PMP was observed. Good levels of linearity (R² > 0.9714), repeatability (CVs < 5%), and recovery rate (80% to 120%) were determined for most PMPs. However, absolute quantification with external peptide and protein standards revealed differences in model cheese depending on the PMP, e.g., 0.50% ± 0.02% to 5.31% ± 0.25% for β-LG. As protein spiking prior to hydrolysis revealed differing digestion behavior of whey proteins, further studies are required to enable valid quantification in various cheese types.

Rapid detection of neutralising acid adulterants in raw milk using a milk component analyser and chemometrics

This study focused on the development of a method for the rapid detection of acid-neutralising adulterants in raw milk using a milk composition analyser. Qualitative analysis for the discrimination of different acid-neutralising acid adulterants in raw milk and quantification of NaSCN in adulterated raw milk were conducted, combined with chemometrics. The results showed that the milk component analyser combined with principal component analysis (PCA) could judge whether raw milk samples were adulterated but cannot identify the types of adulterated substances. Although partial least squares discrimination analysis (PLS-DA) can distinguish some adulterated raw milk samples, the accuracy rate was only 56.3%; the random forest (RF) model could recognise most adulterated raw milk samples with an accuracy rate of 97.5% and the F1-score was 0.9638. In the prediction model of NaSCN adulteration concentration in raw milk constructed by RF, the coefficient of determination (R²) was 0.9889, and the root means square error (RMSE) was 3.28 × 10^-4, suggesting a high prediction performance of the model. The effectiveness of the method for the detection of real samples in practical production was also proved. Based on the above results, it could conclude that the milk component analyser, combined with chemometrics, effectively distinguished acid-neutralising adulterants in raw milk. These findings provide a reference for the rapid detection of adulterants and the quality control of raw milk.

Focus on the Protein Fraction of Sports Nutrition Supplements

Increasing awareness of balanced diet benefits is boosting the demand for high-protein food and beverages. Sports supplements are often preferred over traditional protein sources to meet the appropriate dietary intake since they are widely available on the market as stable ready-to-eat products. However, the protein components may vary depending on both sources and processing conditions. The protein fraction of five commercial sports supplements was characterized and compared with that of typical industrial ingredients, i.e., whey protein concentrates and isolates and whey powder. The capillary electrophoresis profiles and the amino acid patterns indicated that, in some cases, the protein was extensively glycosylated and the supplemented amino acids did not correspond to those declared on the label by manufacturers. The evaluation by confocal laser scanning microscopy evidenced the presence of large aggregates mainly enforced by covalent crosslinks. The obtained findings suggest that, beside composition figures, provisions regarding sports supplements should also consider quality aspects, and mandatory batch testing of these products would provide more reliable information to sport dieticians.

The Application of FTIR Spectroscopy and Chemometrics for the Authentication Analysis of Horse Milk

Expensive milk such as horse's milk (HM) may be the target of adulteration by other milk such as goat's milk (GM) and cow's milk (CM). FTIR spectroscopy in combination with chemometrics of linear discriminant analysis (LDA) and multivariate calibrations of partial least square regression (PLSR) and principal component regression (PCR) was used for authentication of HM from GM and CM. Milk was directly subjected to attenuated total reflectance (ATR) spectral measurement at midinfrared regions (4000-650 cm-1). Results showed that LDA could make clear discrimination between HM and HM adulterated with CM and GM without any misclassification observed. PLSR using 2nd derivative spectra at 3200-2800 and 1300-1000 cm-1 provided the best model for the relationship between actual values of GM and FTIR predicted values than PCR. At this condition, R 2 values for calibration and validation models obtained were 0.9995 and 0.9612 with RMSEC and RMSEP values of 0.0093 and 0.0794. PLSR using normal FTIR spectra at 3800-3000 and 1500-1000 cm-1 offered R 2 for the relationship between actual values of CM and FTIR predicted values of >0.99 in calibration and validation models with low errors of RMSEC of 0.0164 and RMSEP of 0.0336 during authentication of HM from CM. Therefore, FTIR spectroscopy in combination with LDA and PLSR is an effective method for authentication of HM from GM and CM.