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Singha S, Ceciliani F, Rahman MM, Mannan MA, Chowdhury S, Nath SC, Paul OB, Persson Y, Boqvist S. Factors influencing somatic cell counts and bacterial contamination in unpasteurized milk obtained from water buffalo in Bangladesh. Trop Anim Health Prod 2023; 55:242. [PMID: 37328619 PMCID: PMC10276108 DOI: 10.1007/s11250-023-03644-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 05/30/2023] [Indexed: 06/18/2023]
Abstract
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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Affiliation(s)
- Shuvo Singha
- Department of Veterinary Medicine and Animal Sciences, Università degli Studi di Milano, Via dell'Università 6, 26900, Lodi, Italy.
- Department of Physiology, Faculty of Veterinary, Animal and Biomedical Sciences, Sylhet Agricultural University, Sylhet, 3100, Bangladesh.
- Department of Medicine and Surgery, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University, Chattogram, 4225, Bangladesh.
- Udder Health Bangladesh, Chattogram, 4225, Bangladesh.
| | - Fabrizio Ceciliani
- Department of Veterinary Medicine and Animal Sciences, Università degli Studi di Milano, Via dell'Università 6, 26900, Lodi, Italy
- Udder Health Bangladesh, Chattogram, 4225, Bangladesh
| | - Md Mizanur Rahman
- Department of Medicine and Surgery, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University, Chattogram, 4225, Bangladesh
- Udder Health Bangladesh, Chattogram, 4225, Bangladesh
| | - Mohammad Abdul Mannan
- Department of Medicine and Surgery, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University, Chattogram, 4225, Bangladesh
- Udder Health Bangladesh, Chattogram, 4225, Bangladesh
| | - Salma Chowdhury
- Department of Medicine and Surgery, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University, Chattogram, 4225, Bangladesh
- Udder Health Bangladesh, Chattogram, 4225, Bangladesh
| | - Sanjib Chandra Nath
- Department of Medicine and Surgery, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University, Chattogram, 4225, Bangladesh
- Udder Health Bangladesh, Chattogram, 4225, Bangladesh
- Sustainable Enterprise Project, Sagarika Samaj Unnayan Sangstha, Subarnachar, Noakhali, 3812, Bangladesh
| | - Ovirup Bhushan Paul
- Department of Medicine and Surgery, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University, Chattogram, 4225, Bangladesh
- Udder Health Bangladesh, Chattogram, 4225, Bangladesh
- Buffalo Research and Development Project, Bangladesh Livestock Research Institute, Savar, Dhaka, 1341, Bangladesh
| | - Ylva Persson
- Udder Health Bangladesh, Chattogram, 4225, Bangladesh
- Department of Animal Health and Antimicrobial Strategies, National Veterinary Institute, 75189, Uppsala, Sweden
| | - Sofia Boqvist
- Udder Health Bangladesh, Chattogram, 4225, Bangladesh
- Department of Biomedical Sciences and Veterinary Public Health, The Swedish University of Agricultural Sciences, 75007, Uppsala, Sweden
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Bai J, Xue H, Jiang X, Zhou Y. Recognition of bovine milk somatic cells based on multi-feature extraction and a GBDT-AdaBoost fusion model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5850-5866. [PMID: 35603382 DOI: 10.3934/mbe.2022274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
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.
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Affiliation(s)
- Jie Bai
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot 010018, China
| | - Heru Xue
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot 010018, China
| | - Xinhua Jiang
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot 010018, China
| | - Yanqing Zhou
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot 010018, China
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