Logistic regression model reveals major factors associated with total bacteria and somatic cell counts in goat bulk milk

Júnior, Wellington Dias Lopes; Monte, Daniel Farias Marinho do; Leon, Candice M.G.C. De; Moura, José Fábio Paulino de; Silva, Núbia Michelle Vieira da; Queiroga, Rita de Cássia Ramos do Egypto; Neto, Severino Gonzaga; Givisiez, Patrícia Emília Naves; Pereira, Walter Esfrain; Oliveira, Celso José Bruno de

doi:10.1016/j.smallrumres.2021.106360

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…The use of pond water compared to deep tube-well water for cleaning the milk containers was also a risk factor, as has previously been reported (Aliyo et al, 2022 ). Ponds are generally located close to the manure reservoir, and using this water to clean the containers might cause bacterial cross-contamination (Lopes et al, 2021 ). At the middleman level, mixed cow and buffalo milk and a poor cleanliness score for the milk containers were significantly associated with high TBC, consistent with the findings of Aliyo et al ( 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Factors influencing somatic cell counts and bacterial contamination in unpasteurized milk obtained from water buffalo in Bangladesh

Singha

Ceciliani

Rahman

et al. 2023

Trop Anim Health Prod

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Little has been published on the factors influencing the safety and quality of milk derived from water buffalo in Bangladesh. This study aims to describe the milk hygiene parameters and milk chain characteristics of unpasteurized raw milk sold to consumers in order to improve milk hygiene. A quantitative study design evaluated somatic cell counts, total bacterial counts, and specific gram-negative (Enterobacteria) and gram-positive (staphylococci) pathogens in 377 aseptically collected milk samples. Samples were collected at multiple nodes along the buffalo milk value chain: 122 bulk tank milk samples were collected at the farm level, 109 milk samples at the middlemen level, and 111 milk samples at the milk collection centers. In addition, 35 samples were taken from various milk products at the retail level. It was found that progressively increasing somatic cell counts and bacterial counts, including potential pathogens, occurred along the milk chain. A seasonal increase in spring was found, varying based on the farming system (semi-intensive versus intensive). Other factors included water purity and cleanliness of containers, mixing buffalo and cow’s milk, and the location of the water buffalo milk producer (coastal or river basin). This study demonstrated how improving udder health and milk hygiene along the water buffalo milk value chain would increase the safety and quality of water buffalo milk in the study area.

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Section: Discussionmentioning

confidence: 99%

Factors influencing somatic cell counts and bacterial contamination in unpasteurized milk obtained from water buffalo in Bangladesh

Singha

Ceciliani

Rahman

et al. 2023

Trop Anim Health Prod

View full text Add to dashboard Cite

show abstract

“…(2) Analytical hierarchy process: it decomposes the relevant factors affecting the prediction results into different levels such as objectives, methods, and criteria, then makes qualitative and quantitative analysis, and finally obtains the decision-making method [14]. (3) Grey system theory: aiming at the sample system with low information accuracy, it comprehensively evaluates the risk indicators during SCM by determining the standard weight, evaluation standard, sample matrix, the grey evaluation coefficient, grey evaluation weight matrix, and other methods or steps.…”

mentioning

confidence: 99%

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

2022

Wireless Communications and Mobile Computing

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The present work is aimed at solving the investment risks in the supply chain management (SCM) process of enterprises. Therefore, the Back Propagation Neural Network (BPNN) algorithm, logistic regression analysis, and other related theories are used for the risk prediction analysis of supply chain samples. Firstly, 40 pieces of supply chain training data are collected as research samples. Secondly, the examples are trained using the BPNN algorithm. Meanwhile, a logistic regression model is constructed based on Principal Component Analysis (PCA). Finally, the two models conduct risk prediction on the test samples. The results indicate that the BPNN model can effectively predict various risks in the SCM process. It achieves an excellent evaluation effect of single risks, consistent with the actual results. Still, there are some deviations between the prediction results and the actual results of mixed risks. When the significance P value is more than 0.5, the sample is predicted to be of high risk. When it is less than 0.5, the sample is predicted to be of low risk. The prediction accuracy of the logistic regression model is as high as 92.8%, demonstrating brilliant applicability and popularization in the investment risk prediction of the supply chain. The BPNN algorithm and logistic regression model can precisely predict the investment risk in SCM and provide a reference for the improved SCM and the sustainable and stable development of enterprises in the supply chain.

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Recognition of bovine milk somatic cells based on multi-feature extraction and a GBDT-AdaBoost fusion model

Bai

Xue

Jiang

et al. 2022

MBE

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<abstract> <p>Traditional laboratory microscopy for identifying bovine milk somatic cells is subjective, time-consuming, and labor-intensive. The accuracy of the recognition directly through a single classifier is low. In this paper, a novel algorithm that combined the feature extraction algorithm and fusion classification model was proposed to identify the somatic cells. First, 392 cell images from four types of bovine milk somatic cells dataset were trained and tested. Secondly, filtering and the K-means method were used to preprocess and segment the images. Thirdly, the color, morphological, and texture features of the four types of cells were extracted, totaling 100 features. Finally, the gradient boosting decision tree (GBDT)-AdaBoost fusion model was proposed. For the GBDT classifier, the light gradient boosting machine (LightGBM) was used as the weak classifier. The decision tree (DT) was used as the weak classifier of the AdaBoost classifier. The results showed that the average recognition accuracy of the GBDT-AdaBoost reached 98.0%. At the same time, that of random forest (RF), extremely randomized tree (ET), DT, and LightGBM was 79.9, 71.1, 67.3 and 77.2%, respectively. The recall rate of the GBDT-AdaBoost model was the best performance on all types of cells. The F1-Score of the GBDT-AdaBoost model was also better than the results of any single classifiers. The proposed algorithm can effectively recognize the image of bovine milk somatic cells. Moreover, it may provide a reference for recognizing bovine milk somatic cells with similar shape size characteristics and is difficult to distinguish.</p> </abstract>

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Logistic regression model reveals major factors associated with total bacteria and somatic cell counts in goat bulk milk

Cited by 3 publications

References 40 publications

Factors influencing somatic cell counts and bacterial contamination in unpasteurized milk obtained from water buffalo in Bangladesh

Factors influencing somatic cell counts and bacterial contamination in unpasteurized milk obtained from water buffalo in Bangladesh

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

Recognition of bovine milk somatic cells based on multi-feature extraction and a GBDT-AdaBoost fusion model

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