AIMC Topic: Milk

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Determination of milk yield in water buffaloes using multi-class logistic regression and machine learning methods.

Tropical animal health and production
In this study, Random Forest, Gradient Boosting Machines (GBM), and Support Vector Machines (SVM), Multi-Class Logistic Regression (MCLR) models were comparatively evaluated for the prediction of milk yield in water buffaloes. The study's main purpos...

Smartphone-based fluorescence Eu/Ce-MOFs hydrogel sensor for sensitive and visual detection of tetracyclines with machine learning-assistance.

Food chemistry
The excessive use of tetracyclines (TCs) poses a significant threat to human health, necessitating the development of convenient, rapid, and intelligent detection methods for monitoring TCs residues in food products. In this work, we present a hetero...

Monitoring of milking routines for dairy cows using a computer vision system: A diagnostic accuracy study.

Journal of dairy science
The primary objective was to assess the performance of a computer vision system for the detection of reattachment and manual removal of the milking unit, as well as the assessment of the preparation lag time of the milking routine. The secondary obje...

Leveraging unsupervised machine learning techniques for detecting outliers in the daily milk yield data of dairy cows.

Journal of dairy science
The lactation curve is essential for developing effective feeding plans, optimizing breeding, and strategizing milk production for dairy farms. However, health disorders, as well as external factors such as heat stress, dietary changes, and certain m...

Association of artificial intelligence-predicted milk yield residuals to behavioral patterns and transition success in multiparous dairy cows.

Journal of dairy science
Data-driven health monitoring based on milk yield has shown potential to identify health-perturbing events during the transition period. As a proof of principle, we explored the association between the cow's residual milk yield, that is, the differen...

Exploration of the fluorine-fluorine interaction mechanism in fluoroquinolone antibiotics recognition and ciprofloxacin detection on the basis of fluorine-doped carbon quantum dots and machine learning.

Food chemistry
The uncontrolled use of antibiotics poses a significant threat to human health and ecosystems. Accurate differentiation and trace detection of fluoroquinolone antibiotics (FQs) in foods are imperative. Fluorine-doped carbon quantum dots chelated with...

Model-driven multivariate control chart and support vector machine as tools to detect variation in the milking process and monitor parlor performance.

Journal of dairy science
The efficiency of the milking process is the key to dairy farm management. However, due to the high variability of data from single or multiple milk meters, it is difficult to know whether the milking process is under control or not. The main objecti...

Detection and quantification of formaldehyde adulteration in cow and buffalo milk using UV-Vis-NIR spectroscopy with machine learning.

Food chemistry
This work uses UV-Vis-NIR spectroscopy (200-1700 nm), spectral preprocessing, principal component analysis (PCA), and machine learning (ML) to identify and quantify formalin adulteration in cow and buffalo milk. Formalin was added to milk at various ...

Predicting dyscalcemia status in early-lactation multiparous Holstein cows using milk weight and constituent analysis from a single milking at 4 days in milk.

Journal of dairy science
Many multiparous cows struggle to adapt to the challenges of the early postpartum period. Dyscalcemia, a condition defined by low blood calcium concentrations at 4 DIM and associated with suboptimal performance across a spectrum of epidemiologically ...

Identification of natural food-derived emulsifiers using QSAR and machine learning: Application in dairy emulsions.

Food chemistry
Emulsifiers maintain the stability of emulsions, and milk protein-formed emulsions are unstable. Hence, efficient ways to screen food-derived compounds need to be identified. This study combined molecular descriptors with machine learning algorithms ...