Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows

文献类型： 外文期刊

第一作者： Zhao, Xiuxin

作者： Zhao, Xiuxin;Song, Yuetong;Zhang, Fan;Wang, Kun;Zhang, Miao;Gao, Yundong;Wang, Xiao;Li, Jianbin;Zhao, Xiuxin;Zhang, Yuanpei;Cai, Gaozhan;Xue, Guanghui;Liu, Yan;Song, Yuetong;Sun, Dongxiao;Song, Yuetong;Chen, Kewei;Wang, Kun;Zhang, Miao

作者机构：

关键词： prediction; milk; fatty acid content; mid-infrared spectroscopy; Chinese Holstein cow

期刊名称：MOLECULES （ 影响因子：4.6； 五年影响因子：4.9 ）

ISSN：

年卷期： 2023 年 28 卷 2 期

页码：

收录情况： SCI

摘要： Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky-Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000 similar to 400 cm(-1) and 3017 similar to 2823 cm(-1)/1805 similar to 1734 cm(-1)) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R-2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R-2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.

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