Jiang Han, Wen Peipei, Fan Yikai, Zhang Yi, Li Chunfang, Chu Chu, Wang Haitong, Zheng Yue, Yang Chendong, Jiang Guie, Li Jianming, Ni Junqing, Zhang Shujun
Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.
Frontiers Science Center for Animal Breeding and Sustainable Production, Huazhong Agricultural University, Ministry of Education, Wuhan 430070, China.
Foods. 2025 Mar 12;14(6):969. doi: 10.3390/foods14060969.
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, = 4) + 302 wave points, a protein model with PLSR + SG (w = 7, = 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 (RPD (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.
基于傅里叶变换中红外光谱（FT-MIRS）的水牛乳稳健模型尚缺且难以快速完成。因此，本研究使用了来自中国南部和中部两个水牛养殖场的614份乳样进行FT-MIRS分析，以探索预测水牛乳脂肪、乳蛋白和总固体（TS）的潜力，为水牛乳成分含量的测定提供一种快速检测技术。研究还探讨了该模型在时空维度上的快速转化与应用，为新模型的快速应用和稳健模型的建立提供参考策略。从而可为水牛生产管理和遗传育种提供大量表型数据。本研究采用12种预处理方法、人工特征选择方法和偏最小二乘回归建立模型。其中，具有PLSR + SG（w = 15，d = 4）+ 302个波数点的脂肪模型、具有PLSR + SG（w = 7，d = 4）+ 333个波数点的蛋白质模型以及具有PLSR + 无 + 522个波数点的TS模型具有最佳预测性能。然后，使用TS模型探索应用策略。在时间维度上，TS模型有效地预测了同期采集的样本（验证集相对分析误差（RPD）= 3.45）。在空间维度上，首先使用一个养殖场的样本进行建模，然后将另一个养殖场30%-70%的样本添加到调试模型中。结果发现，另一个养殖场样本的预测能力逐渐提高。因此，基于FT-MIRS预测水牛乳成分是可行的。此外，当使用预测同期样本作为模型的两种应用策略，并添加预测养殖场30%-70%的样本时，在稳健模型完全建立之前即可提高模型应用效果。
