Rapid identification of A1 and A2 milk based on the combination of mid-infrared spectroscopy and chemometrics

Recent advances in the bovine β-casein gene mutants on functional characteristics and nutritional health of dairy products: Status, challenges, and prospects

β-casein is the second most abundant form of casein in milk. Changes in amino acid sequence at specific positions in the primary structure of β-casein in milk will produce gene mutations that affect the physicochemical properties of dairy products and the hydrolysis site of digestive enzymes. The screening method of β-casein allele frequency detection in dairy products also has attracted the extensive attention of scientists and farmers. The A1 and A2 β-casein is the two usual mutation types, distinguished by histidine and proline at position 67 in the peptide chain. This paper summarizes the effects of A1 and A2 β-casein on the physicochemical properties of dairy products and evaluates the effects on human health, and the genotyping methods were also concluded. Impressively, this review presents possible future opportunities and challenges for the promising field of A2 β-casein, providing a valuable reference for the development of the functional dairy market.

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities.

Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy

The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.

An approach on detection, quantification, technological properties, and trends market of A2 cow milk

The genetic variant A2 β-casein integrates the casein protein group in milk and has been often associated with positive health outcomes. Therefore, this review explores the present understanding of A2 β-casein, including detection methods and the market trends for dairy from A2 milk. Also, the interaction of A2 β-casein with αs₁-casein and κ-casein genotypes was examined in terms of technological impacts on A2 milk. A limited number of preliminary studies has aimed to investigate the sensorial and technological impacts of β-casein variants in milk matrices, for instance, in yogurt and other derivatives. Nevertheless, considering studies carried out so far, it is concluded that the manufacture of dairy products from A2 milk is perfectly feasible, as the products presented slight differences when compared to those derived from traditional milk. In one of the works, sensitive drops in rennet coagulation time and curd firmness values were observed in cheese traits. However, it is relevant to point out that variant A of κ-casein plays a negative role in the coagulation features of milk. Therefore, alterations in the pattern of cheese-making properties are not uniquely related to β-casein variants. Attempts to produce A2 β-casein in laboratory (non-natural source), through biosynthesis, for example, have not been found so far. This knowledge gap offers a promising area for future studies concerning proteins and bioactive peptide production.

Association between Days Open and Parity, Calving Season or Milk Spectral Data

Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.

What is the impact of amino acid mutations in the primary structure of caseins on the composition and functionality of milk and dairy products?

The impact of amino acid mutations within the peptide structure of bovine milk protein is important to understand as it can effect processability and subsequently effect its physiological properties. Genetic polymorphisms of bovine caseins can influence the chemical, structural, and technological properties, including casein micelle morphology, calcium distribution, network creation upon gelation, and surface activity. The A1 and A2 genetic variants of β-casein have recently acquired growing attention from both academia and industry, prompting new developments in the area. The difference between these two genetic variants is the inclusion of either proline in β-casein A2 or histidine in β-casein A1 at position 67 in the peptide chain. The aim of this review was to examine the extent to which milk and ingredient functionality is influenced by β-casein phenotype. One of the main findings of this review was although β-casein A1 was found to be the dominant variant in milks with superior acid gelation and rennet coagulation properties, milks comprised of β-casein A2 possessed greater emulsion and foam formation capabilities. The difference in the casein micelle assembly, hydrophobicity, and chaperone activity of caseins may explain the contrast in the functionality of milks containing β-casein from either A1 or A2 families. This review provides new insights into the subtle variations in the physicochemical properties of bovine milks, which could potentially support dairy producers in the development of new dairy products with different functional properties.

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Impact of β- and other caseins on the casein micelle structure and functionality.

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Proline and histidine in β-caseins play a key role in casein micelle conformation.

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Chaperone activity of β-casein A2 towards heat-induced aggregation of whey protein.

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Gels prepared of milks with β-casein A1 possess a denser and firmer structure.

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Ordered structure of β-casein A2 led to improved emulsion and foam formation.