南湖新闻网讯（通讯员 褚楚) 近日，我校动物科学技术学院、动物医学院农业动物遗传育种与繁殖教育部重点实验室张淑君教授团队在牛奶中红外光谱研究领域取得重要进展，相关系列成果以“Prediction of individual total amino acids and free amino acids in Chinese Holstein cows milk using mid-infrared spectroscopy and their phenotypic variability”和“Influence of milk storage time on mid-infrared spectroscopy and its predictions for amino acid content”为题分别在Food Research International和Journal of Dairy Science期刊上发表。

牛奶在儿童和成人的蛋白质饮食摄入量中占很大比例，被誉为“白色血液”。牛奶是氨基酸的重要来源，这些氨基酸对于人类的生长发育至关重要。牛奶氨基酸含量受到遗传和非遗传因素的影响，因此通过饲养管理和育种的方法可改善牛奶中氨基酸的含量和组成，以提高牛奶的营养和加工价值。但迄今为止，氨基酸表型变异研究不足，在中国的奶牛育种目标中氨基酸也并未考虑在内，原因之一是目前氨基酸测定方法（如高效液相色谱）费时且昂贵，导致无法获得大量数据。因此，需要开发一种准确、快速、低成本、高通量的牛奶氨基酸含量检测技术。

牛奶样品的中红外光谱图（以吸光度表示）

中红外光谱(Mid-infrared spectroscopy, MIRS)技术被公认为是一种强大的高通量表型分析工具，可实现样品中某种化学成分的定性鉴别和定量检测。MIRS法快速、经济、环保，已广泛应用于全球奶牛生产性能测定计划中，用于脂肪、蛋白质等指标的检测。奶牛生产性能测定计划是提高奶牛群性能和培育高产奶牛群的重要途径，通过这种途径可获得大量表型数据，对于奶牛遗传评估、牛群管理监测和牛奶质量筛查非常有用。目前，由于缺少相关的预测模型，氨基酸并未纳入中国的奶牛生产性能测定体系中。

基于MIRS的牛奶中氨基酸含量预测模型的在验证集上的预测结果（真实值和预测值之间的拟合线）

本系列研究基于MIRS和机器学习算法建立了牛奶中氨基酸含量预测模型，该模型经过进一步优化后可纳入中国奶牛生产性能测定体系中，从而实现牛奶中氨基酸含量的绿色、快速、低成本、高效、准确的批量检测。这对奶牛遗传评估、牛奶营养价值提升、优质奶源筛选、特色乳制品生产和奶牛生产性能测定体系指标拓展有重要意义。同时，还确定了应用该检测技术时牛奶样品的最佳储存时间（采样后3天内）和最长储存时间（采样后6天内），以规范牛奶检测的时间程序。

本系列研究建立的氨基酸含量预测模型打破了国外的技术垄断和模型垄断，解决了快速批量检测牛奶中氨基酸含量的“卡脖子”技术，为中国乃至世界奶牛的智能化表型测定和遗传评估提供了研究基础、理论指导和技术支撑。

我校动物科学技术学院、动物医学院博士生褚楚为文章第一作者，张淑君教授为通讯作者，国家乳业技术创新中心李喜和、孙伟，宁夏回族自治区畜牧工作站李委奇，新疆维吾尔自治区畜牧科学院胡波、郑文新等为共同作者。本研究得到国家重点研发计划和中央高校基本科研业务费专项基金等项目的资助。

原文链接：

https://doi.org/10.1016/j.foodres.2024.115482. (Food Research International);

https://doi.org/10.3168/jds.2024-25903. (Journal of Dairy Science).

英文摘要：

https://doi.org/10.1016/j.foodres.2024.115482. (Food Research International):

Establishing a high-throughput detection technology for amino acid (AA) content in milk using mid-infrared (MIR) spectroscopy has profound implications for enhancing nutritional value of milk, identifying superior milk sources, producing specialty dairy products, and expanding Dairy Herd Improvement (DHI) metrics. The aim of this study was to evaluate the effectiveness of MIR spectroscopy in predicting the content of 15 individual total AA (TAAs) and 16 free AA (FAAs) in bovine milk as well as to investigate the major factors affecting the phenotypic variability of AA content. From March 2023 to March 2024, 513 milk samples were collected from 10 Holstein dairy farms in China and analyzed using Bentley spectrometers for MIR measurements. Their TAAs and FAAs concentrations were assessed through an AA autoanalyzer. Separate quantitative prediction models were developed for each AA using partial least squares regression; accuracy of prediction was assessed using Cow-independent external validation (CEV) and Farm-independent external validation (FEV) set. In CEV, the ratio of performance to deviation (RPD) of the TAAs models ranged from 1.45 (Ser) to 2.19 (Leu), while the FAA models ranged from 1.15 (Ser) to 2.44 (Met). In FEV, the RPD of the TAAs models ranged from 0.98 (Met) to 1.76 (Asp, Glu, and Ala), and the FAAs models ranged from 0.33 (Phe) to 1.23 (Asp and Tyr). For farms included in the calibration set, MIR spectroscopy provided a rough quantitative estimation for 4 individual TAAs (Ile, Leu, Glu, and Tyr) and 2 FAAs (Met and His), as well as a qualitative determination for high and low values in 9 individual TAAs (Phe, Met, Val, Lys, Thr, Asp, Ala, His, and Arg). For farms outside the calibration set, MIR spectroscopy could only distinguish between high and low contents for 5 individual TAAs (Glu, Asp, Ala, Leu, and Arg). Phenotypically, the variation pattern in TAAs contents mirrored that of protein, while FAAs did not show a clear trend, though mastitis led to a significant elevation of FAAs in milk (p < 0.05). Overall, the application of MIR spectroscopy can be considered very promising for a low-cost, rapid, large-scale assessment of individual TAAs and FAAs contents in milk. After refinement, some models could potentially be incorporated into DHI, which would greatly benefit the milk production and food industries.

https://doi.org/10.3168/jds.2024-25903. (Journal of Dairy Science):

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 amino acids (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. 373 individual milk samples for the development of AA content prediction equations were collected from 7 commercial dairy farms (data set 1). 103 individual milk samples for the analysis of the effect of storage time were collected from 2 farms (data set 2). First, separate quantitative prediction models based on data set 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 data set 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 RPD_CV and being in the range from 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, somatic cell count, urea, and acetone. However, the predictions of all the AA by MIRS were repeatable and reproducible across different milk storage time until 6 d after sampling except for Tyr, His, and Phe, with repeatability and reproducibility both greater than or close to 90%. In conclusion, it is 0–6 d after milk collection, stored at 4°C with preserved-bronopol, that MIRS can provide relatively consistent and accurate predictions for AA content. However, a shorter storage time, such as within 3 d post-collection, is recommended when conditions permit.

审核人：张淑君