Meta-clustering of milk mid-infrared spectra identifies dairy cow groups associated with negative energy balance in early lactation
Summary
This study uses meta-clustering on milk mid-infrared spectra to identify dairy cow groups associated with negative energy balance in early lactation, revealing five distinct clusters with varying severity.
View Cached Full Text
Cached at: 08/24/26, 04:31 AM
# Meta-clustering of milk mid-infrared spectra identifies dairy cow groups associated with negative energy balance in early lactation Source: [https://arxiv.org/abs/2608.20653](https://arxiv.org/abs/2608.20653) [View PDF](https://arxiv.org/pdf/2608.20653) > Abstract: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\. ## Submission history From: Eric Paquet R \[[view email](https://arxiv.org/show-email/52d54631/2608.20653)\] **\[v1\]**Fri, 21 Aug 2026 01:19:16 UTC \(6,308 KB\)
Similar Articles
Interaction valence reveals contrasting social networks in dairy cattle
This paper presents a valence-aware social-network framework using computer vision to analyze interactions in dairy cattle, revealing contrasting affiliative and agonistic network structures that emphasize the importance of interaction valence in social network analysis.
Decision Tree and K-Means Analysis of Raman Spectra for Edible Oils: A Physics-Informed AI Approach
The study establishes a physics-informed AI framework using Raman spectroscopy and machine learning to authenticate edible oils, achieving high classification accuracy with compact spectral representations.
Spatial proteomics guided by H&E-based AI reveals recurrence-risk niches in triple-negative breast cancer
A research paper integrates H&E-based deep learning recurrence risk heatmaps with mass spectrometry spatial proteomics to identify intratumoral molecular niches associated with recurrence in triple-negative breast cancer, achieving strong predictive performance and revealing distinct mitotic vs. immune programs.
Advanced Machine Learning and Deep Learning Techniques for Enhanced Cattle Identification and Detection: A Comprehensive Review
A systematic review of machine learning and deep learning techniques for cattle identification, covering methods like CNNs and YOLO, feature extraction techniques, and challenges such as limited datasets and real-time processing.
Towards an approach to multivariate outlier detection for District Heating System data
This paper evaluates different multivariate outlier detection methods (Z-score, Mahalanobis distances, PCA, Isolation Forest, and Hotelling's T-squared) for identifying irregular operations in district heating system data, proposing an ensemble approach based on agreement among the best-performing methods.