Chu Chu, Wen Peipei, Li Weiqi, Yang Guochang, Wang Dongwei, Ren Xiaoli, Li Chunfang, Yang Zhuo, Liu Li, Li Yongqing, Fan Yikai, Chi Huihui, Zhang Tiezhu, Bao Xiangnan, Xu Xuewen, Sun Wei, Li Xihe, Zhang Shujun
Frontiers Science Center for Animal Breeding and Sustainable Production (Huazhong Agricultural University), Ministry of Education, Wuhan 430070, China; Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.
Ningxia Hui Autonomous Region Animal Husbandry Workstation, Yinchuan 750000, China.
Food Res Int. 2025 Jan;200:115482. doi: 10.1016/j.foodres.2024.115482. Epub 2024 Nov 30.
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.
建立一种利用中红外（MIR）光谱技术高通量检测牛奶中氨基酸（AA）含量的方法，对于提高牛奶营养价值、识别优质奶源、生产特色乳制品以及拓展奶牛群改良（DHI）指标具有深远意义。本研究旨在评估MIR光谱技术预测牛乳中15种单个总氨基酸（TAA）和16种游离氨基酸（FAA）含量的有效性，并探究影响AA含量表型变异的主要因素。2023年3月至2024年3月，从中国10个荷斯坦奶牛场采集了513份牛奶样本，使用宾利光谱仪进行MIR测量分析。通过氨基酸自动分析仪评估其TAA和FAA浓度。使用偏最小二乘回归为每种氨基酸建立单独的定量预测模型；使用独立于奶牛的外部验证（CEV）和独立于农场的外部验证（FEV）集评估预测准确性。在CEV中，TAA模型的性能与偏差比（RPD）范围为1.45（丝氨酸）至2.19（亮氨酸），而FAA模型的范围为1.15（丝氨酸）至2.44（蛋氨酸）。在FEV中，TAA模型的RPD范围为0.98（蛋氨酸）至1.76（天冬氨酸、谷氨酸和丙氨酸），FAA模型的范围为0.33（苯丙氨酸）至1.23（天冬氨酸和酪氨酸）。对于校准集中包含的农场，MIR光谱技术对4种单个TAA（异亮氨酸、亮氨酸、谷氨酸和酪氨酸）和2种FAA（蛋氨酸和组氨酸）提供了粗略的定量估计，以及对9种单个TAA（苯丙氨酸、蛋氨酸、缬氨酸、赖氨酸、苏氨酸、天冬氨酸、丙氨酸、组氨酸和精氨酸）的高低值进行了定性测定。对于校准集之外的农场，MIR光谱技术只能区分5种单个TAA（谷氨酸、天冬氨酸、丙氨酸、亮氨酸和精氨酸）的高低含量。从表型上看，TAA含量的变化模式与蛋白质的变化模式相似，而FAA则没有明显趋势，尽管乳腺炎导致牛奶中FAA显著升高（p < 0.05）。总体而言，MIR光谱技术的应用对于低成本、快速、大规模评估牛奶中单个TAA和FAA含量具有很大的前景。经过改进后，一些模型可能会被纳入DHI，这将极大地造福于牛奶生产和食品行业。
