Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms.

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Analysis of the relationship between short tandem repeats and lactation performance of Xinjiang Holstein cows.

Microsatellite markers, also known as short tandem repeats (STRs), are important for marker-assisted selection to detect genetic polymorphism, and they are uniformly distributed in eukaryotic genomes. To analyze the relationship between microsatellite loci and lactation traits of Holstein cows in Xinjiang, 175 lactating cows with similar birth dates, the same parity, and similar calving dates were selected, and 10 STR loci closely linked to quantitative trait loci were used to analyze the correlation between each STR locus and four lactation traits (daily milk yield, milk fat percentage, milk protein percentage, and lactose percentage). All loci showed different degrees of genetic polymorphism. The average values of observed alleles, effective alleles, expected heterozygosity, observed heterozygosity, and polymorphic information content of the 10 STR loci were 10, 3.11, 0.62, 0.64, and 0.58, respectively. Chi-square and G-square tests showed that all populations of loci were in accordance with the Hardy-Weinberg equilibrium. Analysis of the correlation between STR locus genotype and lactation performance in the whole lactation period showed three loci (namely, BM143, BM415, and BP7) with no significant correlation with all lactation traits, two loci (BM302 and UWCA9) related to milk yield, three loci (BM103, BM302, and BM6425) related to milk fat percentage, two loci (BM302 and BM6425) related to milk protein percentage, and three loci (BM1443, BM302, and BMS1943) related to lactose percentage. The microsatellite loci selected in this study showed rich polymorphism in the experimental dairy cow population and were related to the lactation traits, which can be used for the evaluation of genetic resources and early breeding and improvement of Holstein dairy cows in Xinjiang.