用贝叶斯有限混合模型评估牛乳质量的直接和间接诊断方法的最佳切线估计和评估
Eleftherios Meletis1, Juan Carlos Arango-Sabogal2, J Trenton McClure3
1Laboratory of Epidemiology, Biostatistics and applied Artificial Intelligence, Faculty of Public and One Health, University of Thessaly, Karditsa 43100, Greece.
Journal of dairy science
|November 22, 2025
概括
高品质的牛乳对小牛健康至关重要. 贝叶斯模型改进了放射性免疫扩散 (RID) 测定,传输红外光谱 (TIR) 和数字布里克斯 (dBrix) 折射计的诊断准确性,证实了dBrix的高度有效性.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 动物科学动物科学
- 生物统计学 生物统计学
背景情况:
- 高质量的胆乳对于新生小牛的被动免疫力和长期健康至关重要.
- 准确评估乳母免疫球蛋白G (IgG) 度对于确保充分的被动转移至关重要.
- 现有的胆乳质量的诊断方法需要评估,以获得最佳的诊断准确性和切断值.
研究的目的:
- 应用贝叶斯有限混合模型来估计乳母质量评估的最佳切割值.
- 为了评估放射性免疫扩散 (RID) 测定,传输红外光谱 (TIR) 和数字布里克斯 (dBrix) 折射计的诊断准确性.
- 为了比较评估牛乳质量的单个和组合方法的性能.
主要方法:
- 贝叶斯有限混合模型被用来分析来自42个荷尔斯坦乳牛群的591个牛乳样本.
- 计算了诊断准确度指标,包括曲线下的面积 (AUC),灵敏度 (Se),特异性 (Sp),正预测值 (PPV) 和负预测值 (NPV).
- 使用RID测定,TIR光谱和dBrix折射计的Youden指数来确定最佳切割值.
主要成果:
- 高品质乳酪的流行率估计为83%.
- dBrix折射计显示了最高的分辨力 (AUC:0.94),其次是RID测定 (AUC:0.92) 和TIR光谱 (AUC:0.82).
- 确定了最佳切断值:34.15 g/L用于RID (Se=0.86,Sp=0.83),22.74 g/L用于TIR (Se=0.88,Sp=0.66),以及19.62%的Brix用于dBrix (Se=0.90,Sp=0.85).
结论:
- 使用连续测量的精细切割值,比现有值提供了较小的性能改进.
- 结合多种方法可以提高诊断准确度,序列解释最大限度地提高PPV,并行解释最大限度地提高NPV.
- dBrix折射计是一种非常准确和有效的方法来评估牛乳质量,结合方法提供进一步的优化.
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