牛奶中红外光谱的元聚类鉴定与泌乳早期能量负平衡相关的奶牛群体
Meta-clustering of milk mid-infrared spectra identifies dairy cow groups associated with negative energy balance in early lactation
AI总结：
本研究结合光谱滤波、降维方法与聚类算法，从40万余条牛奶中红外光谱数据中鉴定出与泌乳早期奶牛能量负平衡程度相关的五个元聚类，发现经典PCA结合k均值的高效方法可重现复杂方法的聚类结果。
AI中文摘要：
聚类方法已被用于识别牛奶样本、奶牛或牛群的不同群体。傅里叶变换红外（FTIR）光谱，尤其是中红外（MIR）光谱，已被应用于个体奶牛的牛奶样本以预测多种牛奶性状。将聚类直接应用于MIR光谱数据可能会揭示与牛奶性状或健康障碍相关的潜在奶牛群体，并有助于预防这些状况或监测处于风险中的动物。本研究旨在直接从牛奶MIR光谱中鉴定泌乳早期个体奶牛的群体，并分析它们与牛奶性状的关联。使用来自3408个商业农场的407632条个体牛奶MIR记录组成的数据集，我们结合了（i）选择有效波数的光谱滤波、（ii）两种降维方法：主成分分析（PCA）和自动编码器，以及（iii）两种聚类算法：k均值和谱聚类，以产生八种不同的聚类方法。我们将分配的聚类重新分组为元聚类，这些元聚类包含八种方法识别出的最相似的聚类。我们的结果揭示了五个不同的泌乳早期个体奶牛元聚类，它们与牛奶性状显著相关。尽管存在显著差异，八种方法都收敛于相同的五个元聚类，且使用全光谱的经典、计算高效的基于PCA的k均值方法重现了更复杂、计算密集型方法识别出的聚类。这五个元聚类与泌乳天数（DIM）密切相关，似乎反映了能量负平衡（NEB）严重程度的梯度：严重、中度和可能轻度，而其余两个可能代表从NEB中恢复的奶牛，一个能量平衡快速恢复，另一个处于早期恢复阶段。
英文摘要：
Clustering methods have been used to identify distinct groups of milk samples, cows, or herds. Fourier-transform infrared (FTIR) spectroscopy, particularly mid-infrared (MIR) spectroscopy, has been applied to individual cow milk samples to predict various milk traits. Applying clustering directly to MIR spectral data may reveal latent groups of cows associated with milk traits or health disorders and can help prevent these conditions or monitor at-risk animals. This study aimed to identify groups of individual dairy cows in early lactation directly from milk MIR spectra and to analyze their associations with milk traits. Using a dataset of 407,632 individual milk MIR records from 3,408 commercial farms, we combined (i) spectral filtering that selects informative wavenumbers, (ii) two dimensionality-reduction methods: principal component analysis (PCA) and an autoencoder, and (iii) two clustering algorithms: k-means and spectral clustering to yield eight different clustering approaches. We regrouped the assigned clusters into meta-clusters that encompassed the most similar ones identified by the eight approaches. Our results revealed five distinct meta-clusters of early-lactation individual dairy cows significantly associated with milk traits. Despite substantial differences, the eight approaches converged on the same five meta-clusters, and the classic, computationally efficient PCA-based k-means approach using the full spectrum recaptured clusters identified by more sophisticated, computationally intensive approaches. The five meta-clusters were strongly associated with DIM and appeared to reflect a gradient of negative energy balance (NEB) severity: severe, moderate, and possibly mild, while the remaining two likely represented cows recovering from NEB, one with rapid restoration of energy balance and one in early recovery.
