Evaluation of Ovsynch versus modified Ovsynch program on pregnancy rate in water buffaloes: a meta-analysis.

Ovsynch is a widely accepted estrus synchronization protocol for improving the reproductive performance of water buffaloes who manifest low reproductive efficiency. Recently, some modified protocols based on Ovsynch such as 2 injections of prostaglandin 14 days apart following the Ovsynch are also introduced to enhance the reproductive potential of this species. In the present study, a meta-analytical assessment was performed with the objective to evaluate the reproductive performance of water buffaloes synchronized with Ovsynch or modified Ovsynch programs. Meta-analysis of the fixed or random effects model was determined by the heterogeneity among the studies. Reproductive outcome of interest was pregnancy per artificial insemination (P/AI) measured on day 25 (25-100). A total of 32 articles including 4003 buffaloes using either Ovsynch or modified Ovsynch protocol were reviewed. In the random effects model for buffaloes, the overall proportion of P/AI was 42.55% [95% confidence interval (CI): 37.48-47.70; n = 3,089] and 46.44% (95% CI: 39.63-53.31; n = 914) on day 25 after AI for Ovsynch and modified Ovsynch, respectively. Results for P/AI were then categorized by ovarian activity, where P/AI was available for 3575 cyclic buffaloes and 320 non-cyclic buffaloes. For cyclic buffaloes, the overall proportion of P/AI was 47.54% (95% CI: 42.72-52.38; n = 2911) and 57.97% (95% CI: 54.12-61.77; n = 664) on day 25 after AI for Ovsynch and modified Ovsynch, respectively. In the fixed effects model for non-cyclic buffaloes, the overall proportion of P/AI was 19.68% (95% CI: 13.48-26.58; n = 167) and 33.01% (95% CI: 25.50-40.94; n = 153) on day 25 after AI for Ovsynch and modified Ovsynch, respectively. In conclusion, a benefit for P/AI is detected in buffaloes with the modified Ovsynch protocol. Besides, whichever estrus synchronization protocols (Ovsynch or modified Ovsynch), cyclic buffaloes have higher P/AI compared with non-cyclic buffaloes.

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.

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.

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods.

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 RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

Influence of Estrus on the Milk Characteristics and Mid-Infrared Spectra of Dairy Cows.

Milk produced by dairy cows is a complex combination of many components. However, at present, changes in only a few milk components (e.g., fat, protein, and lactose) during the estrus cycle in dairy cows have been documented. Mid-infrared (MIR) spectroscopy is a worldwide method routinely used for milk analysis, as MIR spectra reflect the global composition of milk. Therefore, this study aimed to investigate the changes in milk MIR spectra and milk production traits (fat, protein, lactose, urea, total solids (TS), and solid not fat (SnF)) due to estrus. Cows that were successfully inseminated, leading to conception, were included. Cows confirmed to be pregnant were considered to be in estrus at the day of insemination (day 0). A general linear mixed model, which included the random effect of cows, the fixed classification effects of parity number, days in relation to estrus, as well as the interaction between parity number and days in relation to estrus, was applied to investigate the changes in milk production traits and 1060 milk infrared wavenumbers, ranging from 925 to 5011 cm-1, of 371 records from 162 Holstein cows on the days before (day -3, day -2, and day -1) and on the day of estrus (day 0). The days in relation to estrus had a significant effect on fat, protein, urea, TS, and SnF, whose contents increased from day -3 to day 0. Lactose did not seem to be significantly influenced by the occurrence of estrus. The days in relation to estrus had significant effects on the majority of the wavenumbers. Besides, we found that some of the wavenumbers in the water absorption regions were significantly changed on the days before and on the day of estrus. This suggests that these wavenumbers may contain useful information. In conclusion, the changes in the milk composition due to estrus can be observed through the analysis of the milk MIR spectrum. Further analyses are warranted to more deeply explore the potential use of milk MIR spectra in the detection of estrus.