There are various fatty acids (FAs) in milk, which play an important role in human health and dairy product processing. This study aimed to develop prediction models for FA contents in bovine milk and to apply them to analyze the variation characteristics of various FAs. Taking milk samples covering all regions across China (Northeast China, North China, Northwest China, Central China, East China, South China, except southwest China) as the object, prediction models were developed using mid-infrared spectroscopy combined with partial least squares regression algorithm. Among them, the 14 prediction models for TFA, SFA, UFA, MUFA, MCFA, LCFA, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, C16:1 cis-9, and C18:0 can be used to distinguish between high and low values, which RPDCV were all more than 1.4. However, the others 8 FAs including PUFA, SCFA, C14:1 cis-9 and so on were not achieve the criterion. Then, the 14 models were applied to predict the FA contents of large-scale milk samples from three farms in North China, and mixed linear models were fitted, revealing that parity, lactation stage, sampling season, calving season, and SCC level all had significant influences. Milk collected in summer from multiparous cows had higher contents of UFA and MUFA. Most FAs showed a low peak during the DIM of 35-65d. Some FAs showed significantly difference across different SCC levels,especially for MCFA,C6:0, C8:0 and C12:0, with a significant decreasing gradient as SCC increased. The results provide a reliable reference basis for farms to promptly adjust feeding and management strategies, prevent and control diseases in dairy cows, and are conducive to guiding the selection of suitable processing methods for milk at different stages.

The corpus luteum (CL) arises from the luteinization of granulosa cells (GCs) and theca cells, marked by rapid progesterone elevation and angiogenesis. Intriguingly, angiogenesis lags behind progesterone elevation, creating an avascular phase during which luteal cells must fuel intensive steroidogenesis without perfusion. How the avascular CL meets this energetic demand remains a mystery. Here, we reveal a cellular adaptive mechanism-granulosa cell energy storage (GCES)-that resolves this enigma. We demonstrate that upon luteinization initiation, GCs enter a metabolically quiescent state yet enhance glucose uptake, converting the glucose into glycogen. Catabolism of this glycogen reserve supplies the energy required for the avascular CL, ensuring normal luteogenesis. Disruption of GCES induces luteal insufficiency, whereas timely glucose administration enhances GCES, improving luteal function and optimizing reproductive outcome in both mouse and ovine models. In human study, oral intake of glucose post-hCG significantly augments GCES and enhances progesterone production. These results advance our understanding of luteinization.

Rapid assessment of milk amino acids (AA) is essential for milk quality monitoring and AA-enriched product development. Herein, we used Fourier-transform mid-infrared (MIR) spectroscopy combined with machine learning (ML) to develop prediction models for milk AA contents. Dataset comprised 789 milk samples from dairy cows, buffaloes, goats, sheep, camels, and donkeys. First, dataset was divided into calibration, farm-independent external validation (FEV), and species-independent external validation (SEV) sets. Second, ten ML methods were used to develop models. Finally, model performance was assessed using cow-independent cross-validation, FEV, and SEV. Most methods achieved similar predictive accuracy in cross-validation. In a limited FEV set, spike and slab regression, elastic net, and least absolute shrinkage and selection operator tended to show higher predictive accuracy. The ratio of performance to deviation (RPD) of the “best” model ranged from 2.07 to 3.39 in cross-validation, 1.18 to 3.22 in FEV, and 0.15 to 8.59 in SEV. Satisfactory accuracy was observed for Ile, Leu, Phe, Val, Lys, Asp, Glu, Ala, Tyr, Arg, total essential AA, and total AA models (RPD ≥2.0). In SEV, the “best” model exhibited the highest performance for goat milk. These results provide a basis for the accurate, rapid, and cost-effective quantification of milk AA.

Identification of functional genes associated with milk production is essential for establishing effective breeding programs in dairy cattle. To date, the specific functional genes involved in milk production in dairy cows remain to be identified. In this study, we used public RNA-seq data from dairy cows and employed gene co-expression network analysis to identify the integrin beta 1 (ITGB1) as a potential candidate gene related to lactation. In vitro assays demonstrated that ITGB1 knockdown in bovine mammary epithelial cells (MAC-T) inhibited cell proliferation, increased apoptosis, and reduced triglyceride levels. Transcriptomic analysis further revealed that ITGB1 knockdown resulted in differential expression of 503 genes, which were significantly enriched in the FoxO, IL-17, and HIF-1 signaling pathways. Moreover, ITGB1 knockdown caused a reduction in the phosphorylation of both AKT and FoxO1. Conversely, SC79-mediated activation of AKT promoted the phosphorylation and nuclear export of FoxO1, which in turn inhibited the expression of pro-apoptotic factors such as BIM and BAX, thereby attenuating the pro-apoptotic effects induced by ITGB1 knockdown in MAC-T cells. Our findings indicate that ITGB1 is a functional gene regulating milk production and a promising candidate gene for selective breeding in dairy cattle.

Mid-infrared spectroscopy (MIRS) is increasingly used as a rapid and effective analytical method for the quantitative prediction of detailed milk composition, such as minerals, fatty acids, and AA. These analyses require the transportation of samples to a certified laboratory. In this case, storage time may affect MIRS and its prediction results. This study aimed to determine the effect of milk storage time on MIRS and its predictions of AA content. A total of 373 individual milk samples for the development of AA content prediction equations were collected from 7 commercial dairy farms (dataset 1), and 103 individual milk samples for the analysis of the effect of storage time were collected from 2 farms (dataset 2). First, separate quantitative prediction models based on dataset 1 were developed for each AA using partial least squares regression; the accuracy of prediction was assessed using a cross-validation set. Second, repeatability and reproducibility of the predictions of AA were calculated using milk samples in dataset 2, whose spectra were measured once a day for 7 consecutive days after sampling storing at 4°C, to assess the effect of storage time on the consistency of MIRS predicted AA content. Moderate to high prediction accuracy of AA was achieved, with the ratio of performance to deviation of the cross-validation set and the R2 of the cross-validation set being in the range of 1.59 (Gly) to 2.39 (Leu), and from 0.58 (Gly) to 0.79 (Leu), respectively. Results demonstrated that the absorbance of spectral points was affected by milk storage time, especially in the absorption areas associated with fat, protein, lactose, SCC, urea, and acetone. However, the predictions of all the AA by MIRS were repeatable and reproducible across different milk storage times until 6 d after sampling, except for Tyr, His, and Phe, with repeatability and reproducibility both greater than or close to 90%. In conclusion, for milk stored at 4°C and preserved with bronopol, MIRS can provide relatively consistent and accurate predictions for AA content for 0 to 6 d after milk collection. However, a shorter storage time, such as within 3 d after collection, is recommended when conditions permit.

Accurate selection of characteristic spectra is essential to improving the predictive accuracy of spectral models. While conventional spectral selection methods moderately enhance predictive capabilities, they often exhibit limited correlation and precision between the selected wavelengths and the target substance (e.g., minerals in milk), thereby constraining the model's predictive performance. In response, this study introduces a novel MultiCharacteristic Random Frog (MCRF) strategy, designed to enhance the accuracy and robustness of spectral selection through a dual criterion based on the significance (contribution rate) and selection probability of spectral bands relative to the target substances. This study focuses on five essential macrominerals (Ca, K, Na, Mg, P) and five trace minerals (Cu, Fe, Mn, Sr, Zn) present in milk. Using the MCRF strategy alongside three established spectral selection techniques-Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and RF-we identified the characteristic spectra for each mineral. These spectra, combined with preprocessed infrared spectra of milk and Partial Least Squares Regression (PLSR) modeling, were employed to establish predictive models for the mineral concentrations. Compared to the three conventional selection methods, the MCRF strategy significantly enhanced the predictive precision of the mineral content models. Specifically, the Ca content predictive model, constructed using the SNV spectral preprocessing + MCRF strategy + PLSR algorithm, demonstrated high predictive performance, achieving an R2 of 0.96, RMSE of 38.14 mg/kg, and RPD of 4.82-optimizing the model by 5.49 %, 32.23 %, and 47.85 % over the full-spectrum model. Similarly, the P content model (SNV + MCRF + PLSR) exhibited an R2 of 0.76, RMSE of 66.04 mg/kg, and RPD of 2.04, improving predictive accuracy by 8.57 %, 10.71 %, and 12.09 %, respectively. Predictive models for the other eight mineral contents established with MCRF-selected characteristic spectra also displayed strong predictive capabilities (R2 ranging from 0.57 to 0.68). The findings demonstrate that the MCRF strategy provides a marked improvement in spectral authenticity and predictive precision. This novel approach offers an effective strategy for the selection of characteristic spectra and the development of accurate predictive models for mineral and other compositional contents in milk.

Mastitis significantly impacts both the yield and quality of milk. The somatic cell count (SCC) and differential somatic cell count (DSCC), which are related to immune cells, are primary indicators for assessing mammary gland health. In this study, eight previously established mid-infrared spectroscopy models were utilized to predict the content of milk protein fractions (αs1-CN, β-CN, κ-CN, total CN, α-LA, β-LG, IgG, and LF) in milk samples from 21,388 lactating cows across 33 herds. Four linear mixed models were applied to analyze the secretion patterns of milk protein fractions by days in milk (DIM) and parity, their variations under different mastitis conditions, and their associations with the somatic cell score (SCS), DSCC, and immune cell counts (PMN + LYM score (PMN + LYMS) and MAC score (MACS)). The primary findings of the investigation comprised the following: (1) IgG was higher in early lactation, decreased with advancing lactation days, and slightly increased in late lactation, while seven other protein factions decreased from early to peak lactation and increased during mid-to-late lactation. Parity influenced all milk protein fractions except αs1-CN, with total CN, β-CN, and α-LA decreasing and κ-CN, β-LG, IgG, and LF increasing as parity increased (p < 0.05). (2) Mastitis significantly reduced the milk yield, fat percentage, protein percentage, and the contents of total CN, β-CN, κ-CN, and α-LA while increasing β-LG, IgG, and LF. (3) The SCS was negatively correlated with milk yield and α-LA but positively correlated with the fat percentage, protein percentage, κ-CN, β-LG, IgG, and LF. (4) When the DSCC increased to 50%, the milk yield decreased, while the milk protein percentage and κ-CN content significantly increased (p < 0.05). When the DSCC exceeded 50%, the fat percentage, protein percentage, total casein, αs1-CN, β-CN, κ-CN, β-LG, IgG, and LF decreased, while the α-LA content increased (p < 0.05). (5) When the PMN + LYMS increased, the milk yield and α-LA content rose, while the milk fat percentage, the milk protein percentage, and the contents of αs1-CN, β-CN, κ-CN, total CN, β-LG, IgG, and LF decreased (p < 0.05). Conversely, when the MACS increased, the milk yield and α-LA content declined, whereas the milk fat percentage, the milk protein percentage, and the contents of αs1-CN, β-CN, κ-CN, total CN, β-LG, IgG, and LF increased (p < 0.05). This study offers valuable insights into enhancing milk product quality, advancing the early diagnosis and mechanistic research of bovine mastitis, and the sustainable development of the dairy farming industry.

T-bet (T-box transcription factor TBX21), encoded by the Tbx21 gene, is a key regulator of T helper 1 (Th1) cell differentiation and cellular immunity. However, the role of T-bet in avian species remains elusive. To investigate the role of T-bet in avian immune response, we cloned chicken T-bet (chT-bet) from a White Leghorn chicken spleen-derived cDNA library. Multiple sequence alignments and structural analyses revealed that the amino acid sequence of chT-bet has 50.4 % and 51.1 % identity to its human and mouse orthologs, respectively, but the T-box DNA-binding domain remains conserved across species. We found that chT-bet is highly expressed in immune-related organs, particularly the spleen and thymus, and that Newcastle disease virus (NDV) infection significantly upregulated chT-bet expression both in virto and in vivo. Knockdown of chT-bet by RNAi markedly reduced the expression of IFN-γ but not IL-4 in MSB1 cells. Furthermore, chT-bet overexpression in DF-1 cells activated the promoter of IFN-γ while suppressing promoter activation of IL-2 and IL-4. Chromatin immunoprecipitation (ChIP) assays confirmed the direct binding of chT-bet to IFN-γ and IL-2 promoters. In contrast, chT-bet′s regulation of IL-4 appeared indirect. These findings establish chT-bet as a central orchestrator of Th1 immunity in chickens, directly driving IFN-γ production and regulating IL-2 and IL-4 expressions through distinct pathways, providing insights for vaccine development and disease control strategies.

The somatic cell count (SCC) and differential somatic cell count (DSCC) are proxies for the udder health of dairy cattle, regarded as the criterion of mastitis identification with healthy, suspicious mastitis, mastitis, and chronic/persistent mastitis. However, SCC and DSCC are tested using flow cytometry, which is expensive and time-consuming, particularly for DSCC analysis. Mid-infrared spectroscopy (MIR) enables qualitative and quantitative analysis of milk constituents with great advantages, being cheap, non-destructive, fast, and high-throughput. The objective of this study is to develop a dairy cattle udder health status diagnostic model of MIR. Data on milk composition, SCC, DSCC, and MIR from 2288 milk samples collected in dairy farms were analyzed using the CombiFoss 7 DC instrument (FOSS, Hilleroed, Denmark). Three MIR spectral preprocessing methods, six modeling algorithms, and three different sets of MIR spectral data were employed in various combinations to develop several diagnostic models for mastitis of dairy cattle. The MIR diagnostic model of effectively identifying the healthy and mastitis cattle was developed using a spectral preprocessing method of difference (DIFF), a modeling algorithm of Random Forest (RF), and 1060 wavenumbers, abbreviated as “DIFF-RF-1060 wavenumbers”, and the AUC reached 1.00 in the training set and 0.80 in the test set. The other MIR diagnostic model of effectively distinguishing mastitis and chronic/persistent mastitis cows was “DIFF-SVM-274 wavenumbers”, with an AUC of 0.87 in the training set and 0.85 in the test set. For more effective use of the model on dairy farms, it is necessary and worthwhile to gather more representative and diverse samples to improve the diagnostic precision and versatility of these models.

Maternal dietary protein plays a pivotal role in shaping offspring development, health, and productivity in economically important livestock species, including pigs, cattle, and sheep. Protein intake during gestation influences multiple physiological processes in the offspring, such as fetal growth, metabolic programming, muscle development, immune function, reproduction, and gut health. The specific effects of maternal protein intake vary depending on the species and the gestational period, as the demands for protein fluctuate throughout pregnancy to support fetal development and postnatal adaptation. This review systematically explores the effects of maternal protein nutrition on the offspring of different species and identifies the commonalities and differences observed in the studies. Studies indicate that maternal protein restriction can lead to lower birth weights, impaired muscle growth, altered metabolic programming, and compromised immune function in offspring, potentially affecting their long-term productivity. Conversely, excessive protein intake may also have adverse effects, such as immune dysregulation and metabolic imbalances. The impact of maternal protein levels extends beyond birth, influencing postnatal growth trajectories, reproductive performance, and gut microbiota composition. While considerable progress has been made in understanding these relationships, gaps remain in identifying the precise molecular mechanisms underlying these effects. Future research should focus on refining dietary recommendations tailored to different livestock species, investigating the role of gestation stage-specific protein requirements, and integrating multi-omics approaches to elucidate the long-term consequences of maternal protein intake. A deeper understanding of these mechanisms will contribute to optimizing feeding strategies, enhancing animal welfare, and improving the sustainability of livestock production systems.

The potential of milk Fourier-transform mid-infrared (FTIR) spectroscopy in predicting cow fertility has been extensively examined, but largely based on the number of services per conception (NSC). Compared with NSC, Calving-to-conception interval (CCI) may be a more critical factor influencing the profitability, productivity, and sustainability of dairy herds, as it reflects days to first breeding, voluntary waiting period, NSC, and service intervals. Our objectives were to evaluate the ability of FTIR spectroscopy and farm data collected from early lactation to predict CCI length postcalving in Holstein cows from a highly productive TMR system. We also sought to identify the most informative milk sampling periods for CCI prediction. From January 2019 to December 2023, FTIR spectra records, cow information, milk recording information, and fertility information were collected from 28,434 Holstein cows within 13 dairy farms in China. First, cows were classified into long calving-to-conception interval (LCCI) and short calving-to-conception interval (SCCI) groups based on 2 strategies. Strategy 1 defined LCCI as cows with a CCI longer than 150 d and SCCI as cows with a CCI shorter than 150 d. Strategy 2 employed a similar method but with a CCI threshold of 90 d. Second, partial least squares discriminant analysis was employed to develop prediction models for the classification of LCCI and SCCI cows. The performance of models was assessed using herd-independent cross-validation. These analyses were conducted separately for the complete dataset as well as for each of the 9 subsets stratified based on postcalving time windows. The results showed that the area under the receiver operating characteristic curve of cross-validation (AUCCV) varied from 0.461 to 0.675 across different predictors, strategies, and time windows. Across all strategies, prediction accuracy was highest for models developed using data from time windows 22 to 30 days postpartum (dpp) and >60 dpp. The classification model, developed using standard normal variate preprocessed spectra, cow information, milk yield, SCS, and fat-to-protein ratio (FPR) data collected from the time window 22 to 30 dpp, demonstrated the best performance in strategy 1. The values of AUCCV, sensitivity of cross-validation (SENSCV), and specificity of cross-validation (SPECCV) were 0.650, 0.519, and 0.706, respectively. The best-performing classification model based on strategy 2 was developed using Savitzky-Golay preprocessed spectra, cow information, milk yield, SCS, and FPR data collected from the time window >60 dpp, with AUCCV, SENSCV, and SPECCV values of 0.675, 0.552, and 0.712, respectively. In conclusion, FTIR and farm data collected from early lactation could distinguish between cows with different CCI lengths with moderate accuracy. The time window 22 to 30 dpp could provide more effective and accurate predictions for the future fertility of dairy cows, and its use and implementation should be considered in practical farm production. Our study highlights the future application of high-throughput phenotyping technologies in precision livestock farming and offers novel insights into alternate methods for assessing cow fertility.

A robust model of buffalo milk based on Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) is lacking and is difficult to complete quickly. Therefore, this study used 614 milk samples from two buffalo farms from south and central China for FT-MIRS to explore the potential of predicting buffalo milk fat, milk protein, and total solids (TS), providing a rapid detection technology for the determination of buffalo milk composition content. It also explored the rapid transformation and application of the model in spatio-temporal dimensions, providing reference strategies for the rapid application of new models and for the establishment of robust models. Thus, a large number of phenotype data can be provided for buffalo production management and genetic breeding. In this study, models were established by using 12 pre-processing methods, artificial feature selection methods, and partial least squares regression. Among them, a fat model with PLSR + SG (w = 15, p = 4) + 302 wave points, a protein model with PLSR + SG (w = 7, p = 4) + 333 wave points, and a TS model with PLSR + None + 522 wave points had the optimal prediction performance. Then, the TS model was used to explore the application strategies. In temporal dimensions, the TS model effectively predicted the samples collected in a contemporaneous period (RPDV (Relative Analytical Error of Validation Set) = 3.45). In the spatial dimension, at first, the modeling was conducted using the samples from one farm, and afterward, 30-70% of a sample from another farm was added to the debugging model. Then, we found that the predictive ability of the samples from the other farm gradually increased. Therefore, it is possible to predict the composition of buffalo milk based on FT-MIRS. Moreover, when using the two application strategies that predicted contemporaneous samples as the model, and adding 30-70% of the samples from the predicted farm, the model application effect can be improved before the robust model has been fully developed.

Accurate identification of cows' likelihood of conception during the period from recent calving to the first artificial insemination (AI) will provide assistance in managing the fertility of dairy cows and contribute to the economic prosperity and sustainability of farms. The purpose of this study was to use Fourier-transform infrared (FTIR) spectroscopy data collected between recent calving and the first AIto predict the likelihood of a cow conceiving after the first AI and the first or second AI. This study specifically focused on the role of FTIR spectral and farm data collected during different time windows in improving the accuracy of models for predicting a cow's likelihood of conceiving after the first AI and the first or second AI. From 2019 to 2023, fertility information of 10,873 Holstein dairy cows in China were collected, coupled with 21,928 spectral data. First, cows were classified as having a good or poor likelihood of conception. In strategy 1, cows conceiving after the first AI were classified as having a good likelihood of conception and as others as having a poor likelihood of conception. In strategy 2, cows conceiving after the first or second AI were classified as having a good likelihood of conception and others as having a poor likelihood of conception. Second, partial least squares discriminant analysis was used to develop models for predicting the likelihood of conception after the first AI and the first or second AI. The model was assessed using a cross-validation set and herd-independent external validation set. The study also focused on examining the potential correlation between the accuracy of prediction and the period of spectral and farm data collection by analyzing the diagnostic performance of the model in 8 different time windows: from 0 to 7 d postpartum (dpp), 8 to 14 dpp, 15 to 21 dpp, 22 to 30 dpp, 31 to 45 dpp, 46 to 60 dpp, >= 61 dpp, and 0 to 7 d before the first AI. The results showed that the model based on strategy 1 performed better when in proximity to the first AI, with AUC for the cross-validation and herd-independent external validation sets of 0.621 and 0.633, respectively. The model based on strategy 2 exhibited superior performance throughout the late phase of uterine involution. The optimal model was developed by using spectral data collected from 22 to 30 dpp. The AUC for the cross-validation and herd-independent external validation sets were 0.644 and 0.660, respectively, which were higher than those of strategy 1. This study demonstrates the potential of using FTIR spectral data to predict a cow's ability to conceive. The model developed from data collected within a certain time window exhibited better prediction accuracy, particularly from 22 to 30 dpp and 0 to 7 d before the first AI. This study offers novel perspectives on alternate approaches for assessing the fertility of cows, which will contribute to the regularization and sustainability of farms, as well as to the precision management of agriculture.

A comprehensive understanding of the molecular differences between X and Y sperm in Holstein bull semen is crucial for advancing sex control technologies. While previous studies have primarily focused on proteomic and transcriptomic differences, the genome-wide DNA methylation differences between these sperm types remains largely unexplored. In this study, we employed whole-genome bisulfite sequencing to systematically compare the autosomal methylation profiles of X and Y sperm. Although global methylation patterns showed remarkable consistency between the two sperm types, our localized comparative analysis revealed 12,175 differentially methylated regions mapping to 2,041 genes (differentially methylated genes, DMGs). Functional enrichment analysis of these DMGs revealed their involvement in essential biological processes, particularly in energy metabolism and membrane voltage regulation. Notably, SPA17 and CHCHD3, identified as hypermethylated genes in X sperm in this study, have also been reported to show lower protein expression levels in X sperm compared to Y sperm. Furthermore, we identified 28 DMGs functionally associated with spermatogenesis and 5 DMGs related to fertilization. Our findings lay the foundation for thorough understanding of molecular differences between X and Y sperm in bull, providing essential insights for the development of more advanced sex control technologies in the future.

This study was performed to evaluate the effects of maternal 25-hydroxycholecalciferol (25-OH-D3) supplementation on sow performance, as well as the dynamic alterations of both the amino acid and fatty acid profiles in milk. On day 85 of gestation, twenty primiparous hybrid sows were allocated into two groups (10 sows/group) and fed a basal diet (3200 IU/kg vitamin D3) containing either 0 or 50 μg/kg 25-OH-D3 until weaning on d 21 of lactation. Milk was collected at 1, 3, 7, 14, and 21 d of lactation. The results showed that dietary 25-OH-D3 supplementation notably decreased the score of tear stain at 101 d of gestation when compared to the Ctrl group (p = 0.030). No significant difference was found in terms of the gestation day, litter size, and litter weight at birth, whereas maternal 25-OH-D3 intervention notably increased weaning weight and weight gain of the piglet (p < 0.05), while dietary 25-OH-D3 supplementation contributed to a 16.4% body gain during lactation. The concentration of all amino acids in milk was higher in colostrum, following a dramatic drop. The rate of reduction for all amino acids was increased by dietary 25-OH-D3 supplementation. The contents of saturated fatty acids and polyunsaturated fatty acids were increased and decreased linearly throughout lactation (both p < 0.05). Dietary 25-OH-D3 supplementation initially suppressed both saturated and unsaturated fatty acid levels from d 1 to 7, while prompting a recovery of specific fatty acids from d 14 to 21 of lactation, particularly oleic acid, linoleic acid, and arachidonic acid. These findings indicate that maternal 25-OH-D3 supplementation alters the pattern of milk fatty acid and amino acid composition, which may be associated with the observed improvement in piglet outcomes.

Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) is highly effective in identifying functional groups or chemical bonds in organic compounds. It is widely utilized in analytical chemistry, industrial production, and smart agriculture. While calibration transfer methods have demonstrated their efficacy in decreasing spectral variations between instruments, the diversity in configurations remains a significant hurdle for data fusion analysis. Therefore, this study focuses on three milk component datasets(total protein, total fat and total solids) and combines three modeling algorithms (Partial Least Squares Regression (PLSR), Weighted Ensemble, and Convolutional Neural Network (CNN)) for the research and testing of six calibration transfer methods (Piecewise Direct Standardization (PDS), Single wave number Standardization (SWS), Agglomerative Clustering Piecewise Direct Standardization (ACPDS), Canonical Correlation Analysis (CCA), Slope and Bias Correction (SBC), and Deep Transfer Spectra (DTS)) among three instrument brands (Foss, Bentley, and Perkin Elmer). The results indicate that employing the CNN algorithm to develop prediction models on the master instrument (Foss) yields optimal performance when coupled with the PDS calibration transfer method across the three milk component datasets (referred to as the CNN-PDS combination). The R2 values range from 0.599 to 0.859, with an average of 0.769 for test data after calibration transfer. Additionally, it is validated that the DTS method, which is independent of standard sample dependence, is still applicable and shows significant improvement potential among FT-MIRS instruments. Particularly noteworthy is its highest performance, with an R2 of 0.894 on the protein dataset of the Perkin Elmer instrument. Furthermore, Monte Carlo random testing further verifies the adaptability and stability of CNN-PDS and DTS to Bentley and Perkin Elmer, respectively. Finally, the research findings lead to the proposal of application strategies for calibration transfer methods among different brands of FT-MIRS instruments, marking the first such recommendations. These strategies advocate for the judicious selection of the optimal transfer scheme for modeling algorithms (PLSR or CNN) and calibration transfer methods (PDS, ACPDS, or DTS) based on considerations such as dataset characteristics, experimental conditions, and instrument brand type. This study develops and summarizes effective FT-MIRS calibration transfer schemes and usage strategies. It provides technical and theoretical foundations for the utilization of predictive models across multiple brands of FT-MIRS instruments, as well as for FT-MIRS calibration transfer and its application in diverse fields.

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

Classification models for rapid discrimination of milk storage time were developed. Buffalo raw milk (BuR) and bovine raw (BoR), pasteurised (BoP) and ultra-high temperature sterilised (BUHT) milk were analysed by mid-infrared (MIR) spectroscopy at different storage times. A total of 175 (44), 96 (24), 252 (64) and 383 (97) samples were used in the developed (validated) models of BuR, BoR, BoP and BUHT milk, respectively. Seven, two, five, and three spectral regions were selected for BuR, BoR, BoP and BUHT, respectively, to develop their models, which reflect changes in milk protein, fat, lactose and urea nitrogen. Accuracies of the optimised models for BuR, BoR, BoP and BUHT milk were 0.95, 1.00, 0.89, and 0.93, respectively, in the validation dataset. All optimal models were obtained by machine learning methods. This preliminarily study shows that MIR combined with machine learning can rapidly identify the storage time of these four types of milk.