In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle

Giannuzzi, Diana; Mota, Lúcio Flávio Macêdo; Pegolo, Sara; Gallo, Luigi; Schiavon, Stefano; Katz, Gil; Fainboym, David; Minuti, Andrea; Trevisi, Erminio; Cecchinato, Alessio

doi:10.1038/s41598-022-11799-0

Cited by 14 publications

(11 citation statements)

References 83 publications

(102 reference statements)

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“…Model accuracy increased (up to 29%) when class imbalance was treated with repeated cross-validation and down-sampling. Giannuzzi et al [ 14 ] applied several ML models to select the best model for predicting the cow blood metabolic profile using the milk. Gradient boosting machine and ANN outperformed RF, stacking clusters, and other regression models among the techniques used.…”

Section: Discussionmentioning

confidence: 99%

Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants

et al. 2022

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Background Bluetongue (BT) is a disease of concern to animal breeders, so the question on their minds is whether they can predict the risk of the disease before it occurs. The main objective of this study is to enhance the accuracy of BT risk prediction by relying on machine learning (ML) approaches to help in fulfilling this inquiry. Several risk factors of BT that affect the occurrence and magnitude of animal infection with the virus have been reported globally. Additionally, risk factors, such as sex, age, species, and season, unevenly affect animal health and welfare. Therefore, the seroprevalence study data of 233 apparently healthy animals (125 sheep and 108 goats) from five different provinces in Egypt were used to analyze and compare the performance of the algorithms in predicting BT risk. Results Logistic regression (LR), decision tree (DT), random forest (RF), and a feedforward artificial neural network (ANN) were used to develop predictive BT risk models and compare their performance to the base model (LR). Model performance was assessed by the area under the receiver operating characteristics curve (AUC), accuracy, true positive rate (TPR), false positive rate (FPR), false negative rate (FNR), precision, and F1 score. The results indicated that RF performed better than other models, with an AUC score of 81%, ANN of 79.6%, and DT of 72.85%. In terms of performance and prediction, LR showed a much lower value (AUC = 69%). Upon further observation of the results, it was discovered that age and season were the most important predictor variables reported in classification and prediction. Conclusion The findings of this study can be utilized to predict and control BT risk factors in sheep and goats, with better diagnostic discrimination in terms of accuracy, TPR, FNR, FPR, and precision of ML models over traditional and commonly used LR models. Our findings advocate that the implementation of ML algorithms, mainly RF, in farm decision making and prediction is a promising technique for analyzing cross-section studies, providing adequate predictive power and significant competence in identifying and ranking predictors representing potential risk factors for BT.

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Section: Discussionmentioning

confidence: 99%

Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants

et al. 2022

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“…For monitoring the animal’s health and reproductive condition, metabolic profiles are important to review the oxidants and antioxidant substances [ 67 ].…”

Section: Biological Health Markers Of Cows In the Lactation Periodmentioning

confidence: 99%

“…There have actually been a number of studies in cattle, sheep, and goats, but they have mostly focused on the effects of diseases, such as mastitis, pneumonia, sepsis, acidosis, ketosis, enteritis, joint disease, and retained placentas [ 88 , 89 , 90 ]. Nowadays, peripartum metabolic diseases are becoming increasingly studied in dairy ruminants, while dairy cattle blood biochemical parameters are well-established as a means of analyzing metabolic profiles [ 67 ]. Nevertheless, metabolic profile tests can serve as an effective method of discovering which areas of dairy management and nutrition require more attention [ 91 ].…”

Section: Biological Health Markers Of Cows In the Lactation Periodmentioning

confidence: 99%

Biological Health Markers Associated with Oxidative Stress in Dairy Cows during Lactation Period

et al. 2023

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This review aims to summarize and present different biological health markers in dairy cows during the lactation period. Biochemical health markers provide an indicator of how foreign chemical substances, whether external or internal, affect the animal’s health. To understand the relationship between dairy cow health issues and oxidative stress, various biomarkers of oxidative stress must be investigated. Biochemical and hematological factors play a significant role in determining the biological health markers of animals. A variety of biochemical parameters are dependent on various factors, including the animal’s breed, its age, its development, its pregnancy status, and its production status. When assessing the health of cattle, a blood test is conducted to determine the blood chemistry. To diagnose diseases in dairy animals, the blood biochemistry is necessary to determine the cause of many physiological, metabolic, and pathological problems. Observing blood alterations during pregnancy and at peak lactation may determine what factors lift oxidative stress in cows due to disturbances in feed intake and metabolic processes.

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“…Аналогичные исследования фальсификации кокосового молока проводят в работе [11]. В статье [12] используется ансамблевый классификатор Stacking и многослойная нейронная сеть с прямой связью для ежедневного прогнозирования метаболического профиля крови у молочного скота. Авторы исследования [13] сравнивают метод частичной регрессии (PLSR), методы с частными коэффициентами наименьших квадратов (PLS) в сочетании с линейной и полиномиальной регрессией опорных векторов (PLS + SVR), а также метод с использованием PLS и искусственной нейронной сети с одним скрытым слоем для прогнозирования содержания лактоферрина в коровьем молоке.…”

Section: обзор исследованийunclassified

Prediction of milk quantitative traits based on infrared spectroscopy using machine-learning methods

Legashev¹,

Bolodurina²,

Zabrodina³

et al. 2022

CTAC&R

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Fourier transform mid-infrared spectroscopy is a fast and cheap way to analyze cow's milk samples to determine fat, protein, lactose and other quantitative and qualitative indicators of milk quality. Modern tools for data analysis will reveal the relationship between different pairs of quantitative and qualitative characteristics of milk. Purpose of the study. Perform predictions on some key milk quality traits based on infrared spectroscopy data to study the accuracy of the developed mathematical model. Methods. The work was carried out in the winter period of 2022 on the basis of an experimental herd of Holsteinized black-and-white cattle (Krasnodar Territory). The analysis of milk traits was carried out with an automatic analyzer MilkoScan (FOSS) using the method of infrared spectroscopy by unloading the obtained spectra when analyzing the composition of raw milk. 23 indicators of the quantitative milk traits were studied: mass fraction of fat, protein (true and total), lactose, DSMR (dry skimmed milk residue), dry matter, casein, traces of acetone and beta-hydroxybutyrate, urea, freezing point, acidity of milk, myristic, palmitic, stearic, oleic fatty acids (FA), long-chain fatty acids, medium-chain fatty acids, short-chain fatty acids, monounsaturated and polyunsaturated fatty acids, saturated fatty acids, trans fatty acids. Methods based on linear regression, approaches to the regularization of the linear regression model (Ridge, Lasso and ElasticNet), as well as polynomial regression, the partial regression method (PLSRegression) and the Bayesian regression method for the problem of predicting key features of milk traits were considered. A method for reducing the dimensionality of infrared spectroscopy data is implemented based on the algorithm of random search of readings along the length of the window, and the most significant features are identified. Results. Models have been developed for predicting six main indicators of milk quality – mass fraction of fat ('Fat'), mass fraction of casein ('Cas.B'), fatty acids – myristic ('C14:0') and oleic ('C18: 1'), monounsaturated ('MUFA') and polyunsaturated fatty acids ('PUFA') – with an average absolute error not exceeding 0,016. Conclusion. The results obtained in the course of the study will further improve the predictive ability of the equation for determining the quality and composition of milk according to new breeding traits of milk productivity, reduce analysis costs and monitor the health of animals at an early stage.

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In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle

Cited by 14 publications

References 83 publications

Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants

Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants

Biological Health Markers Associated with Oxidative Stress in Dairy Cows during Lactation Period

Prediction of milk quantitative traits based on infrared spectroscopy using machine-learning methods

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