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使用牛奶光谱和机器学习预测霍尔斯坦牛的产后疾病 - - 从诊断日期的回顾性评估

D Lin¹, J Li², J A Seminara¹

¹Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.

Journal of dairy science

|October 3, 2025

概括

牛奶福里埃变换红外光谱 (FTIR) 可以预测乳牛早期哺乳期疾病. 牛奶光谱的变化可以在诊断前7天准确预测健康问题,有助于早期干预.

科学领域:

兽医医学兽医医学兽医医学
动物科学动物科学
频谱学是一种光谱学.

背景情况:

乳牛在早期哺乳期经常面临健康挑战.
里埃变换红外光谱 (FTIR) 是一种用于牛奶分析的非侵入性工具.
它对早期哺乳期疾病的预测能力仍未得到充分探索.

研究的目的:

评估牛奶FTIR光谱在预测荷尔斯坦牛的产后疾病中的有效性.
开发和评估疾病诊断之前的各种时间框架的预测模型.

主要方法:

收集了1162头荷尔斯坦牛的每日牛奶样本,超过30天的牛奶 (DIM).
使用FTIR光谱分析样本.
开发了诊断前8个时间段的预测模型 (回归,机器学习,深度学习).
通过交叉验证和排列测试评估模型性能.

主要成果:

牛奶FTIR光谱显示了与疾病进展相关的渐进性变化.
预测模型实现了接收器操作特征曲线 (AUROC) 下面面积的增加,从0.50 (>10天前) 增加到0.76 (诊断日).
使用FTIR光谱的部分最小平方差异分析 (PLS-DA) 模型的表现优于牛或牛奶组件模型,达到0.74.7的AUROC.

结论:

牛奶FTIR光谱数据有效预测早期哺乳期奶牛即将出现的健康状况.
FTIR光谱为早期疾病检测和管理提供了一种有前途的非侵入性方法.
需要进一步的研究来证实可通用性和在农场中的适用性.

关键词:

里埃变换红外光谱法中的红外光谱学.深度学习是一种深度学习.机器学习是机器学习.牛奶的光谱牛奶的光谱

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