Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5–50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning—especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)—exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and R_V² = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and R_V² = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

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 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 the farm. The purpose of this study was to use FTIR spectroscopy collected from recent calving to the first artificial insemination (AI) to predict the cow's likelihood of conception to first AI, first or second AI. This study specifically focused on the role of FTIR spectral and farm data collected at different time windows in improving the accuracy of model for predicting the cow's likelihood of conception to first AI, 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 into "good" and "poor." Strategy 1 (S1) defined "good" as cows conceived at first AI and "poor" as the others. Strategy 2 (S2) defined "good" as cows conceived at first or second AI and "poor" as the others. Second, the partial least squares discriminant analysis was used to establish models for predicting the likelihood of conception to first AI, first or second AI. The model was assessed using a cross-validation (CV) set and herd-independent-external validation (HEV) 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 at 8 different time windows: '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 S1 performed better while in proximity to the first AI with AUC_CV and AUC_HEV of 0.621 and 0.633, respectively. Models based on S2 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_CV and AUC_HEV were 0.644 and 0.660, respectively, which was higher than that of S1. This study demonstrates the potential of using FTIR spectral data to predict the cow's ability to conceive. The model developed from data collected within a certain time window exhibited better prediction accuracy, particularly during 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.

Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) can be used for quantitative detection of milk components. Here, milk samples of 458 Chinese Holstein cows from 11 provinces in China were collected and we established a total of 22 quantitative prediction models in milk fatty acids by FT-MIRS. The coefficient of determination of the validation set ranged from 0.59 (C18:0) to 0.76 (C4:0). The models were adopted to predict the milk fatty acids from 2138 cows and a new high-throughput computing software HiBLUP was employed to construct a multi-trait model to estimate and analyze genetic parameters in dairy cows. Finally, genome-wide association analysis was performed and seven novel SNPs significantly associated with fatty acid content were selected, investigated, and verified with the FarmCPU method, which stands for “Fixed and random model Circulating Probability Unification”. The findings of this study lay a foundation and offer technical support for the study of fatty acid trait breeding and the screening and grouping of characteristic dairy cows in China with rich, high-quality fatty acids. It is hoped that in the future, the method established in this study will be able to screen milk sources rich in high-quality fatty acids.

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 (AUC_CV) 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 AUC_CV, sensitivity of cross-validation (SENS_CV), and specificity of cross-validation (SPEC_CV) 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 AUC_CV, SENS_CV, and SPEC_CV 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.