Introducing a Rapid and Practical Approach for Determining Fat Content in Cow Milk Using Image Processing
Milk fat content serves as a crucial indicator of milk quality, holding significance for both producers and consumers. Therefore, the development of a swift and viable method for assessing this parameter could greatly enhance monitoring efforts. This study aimed to establish a correlation between mi...
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| Format: | Article |
| Language: | English |
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Shahid Bahonar University of Kerman
2024-12-01
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| Series: | Biomechanism and Bioenergy Research |
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| Online Access: | https://bbr.uk.ac.ir/article_4557_be3752d3e4b78dd546540775348dac48.pdf |
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| author | Lena Beheshti Moghadam Seyed Saeid Mohtasebi Behzad Nouri Mahmoud Omid Seyed Morteza Mohtasebi |
| author_facet | Lena Beheshti Moghadam Seyed Saeid Mohtasebi Behzad Nouri Mahmoud Omid Seyed Morteza Mohtasebi |
| author_sort | Lena Beheshti Moghadam |
| collection | DOAJ |
| description | Milk fat content serves as a crucial indicator of milk quality, holding significance for both producers and consumers. Therefore, the development of a swift and viable method for assessing this parameter could greatly enhance monitoring efforts. This study aimed to establish a correlation between milk fat content and milk color through image analysis techniques. Cow milk samples spanning a fat content range of 0.2% to 3.5% were analyzed under various lighting conditions, employing a fusion of image processing methods with artificial neural networks (ANNs) and particle swarm optimization (PSO) algorithms. Results demonstrated that the most optimal method, determined through comparative analysis against a reference sample, produced accurate estimations of milk fat content. Statistical evaluation revealed a high coefficient of determination (R2=0.99), accompanied by minimal mean absolute error (MAE=0.22) and mean squared error (MSE=0.05). Additionally, a comprehensive examination was conducted into the influence of water content on milk color across different levels of fat concentration. Findings from this investigation provided robust validation for the effectiveness of the proposed method, exhibiting attributes of reliability, efficiency, and cost-effectiveness in the realm of milk fat content assessment. |
| format | Article |
| id | doaj-art-d7a0744e12984c96a4a6164033cb98fc |
| institution | Kabale University |
| issn | 2821-1855 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Shahid Bahonar University of Kerman |
| record_format | Article |
| series | Biomechanism and Bioenergy Research |
| spelling | doaj-art-d7a0744e12984c96a4a6164033cb98fc2025-01-11T18:55:53ZengShahid Bahonar University of KermanBiomechanism and Bioenergy Research2821-18552024-12-0132617410.22103/bbr.2024.23712.10874557Introducing a Rapid and Practical Approach for Determining Fat Content in Cow Milk Using Image ProcessingLena Beheshti Moghadam0Seyed Saeid Mohtasebi1Behzad Nouri2Mahmoud Omid3Seyed Morteza Mohtasebi4Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tehran, Karaj, Iran.Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tehran, Karaj, Iran.Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tehran, Karaj, Iran.Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tehran, Karaj, Iran.Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tehran, Karaj, Iran.Milk fat content serves as a crucial indicator of milk quality, holding significance for both producers and consumers. Therefore, the development of a swift and viable method for assessing this parameter could greatly enhance monitoring efforts. This study aimed to establish a correlation between milk fat content and milk color through image analysis techniques. Cow milk samples spanning a fat content range of 0.2% to 3.5% were analyzed under various lighting conditions, employing a fusion of image processing methods with artificial neural networks (ANNs) and particle swarm optimization (PSO) algorithms. Results demonstrated that the most optimal method, determined through comparative analysis against a reference sample, produced accurate estimations of milk fat content. Statistical evaluation revealed a high coefficient of determination (R2=0.99), accompanied by minimal mean absolute error (MAE=0.22) and mean squared error (MSE=0.05). Additionally, a comprehensive examination was conducted into the influence of water content on milk color across different levels of fat concentration. Findings from this investigation provided robust validation for the effectiveness of the proposed method, exhibiting attributes of reliability, efficiency, and cost-effectiveness in the realm of milk fat content assessment.https://bbr.uk.ac.ir/article_4557_be3752d3e4b78dd546540775348dac48.pdfnon-destructive analysisartificial neural networkcolormilkquality assessmentfat contentcow milk |
| spellingShingle | Lena Beheshti Moghadam Seyed Saeid Mohtasebi Behzad Nouri Mahmoud Omid Seyed Morteza Mohtasebi Introducing a Rapid and Practical Approach for Determining Fat Content in Cow Milk Using Image Processing Biomechanism and Bioenergy Research non-destructive analysis artificial neural network color milk quality assessment fat content cow milk |
| title | Introducing a Rapid and Practical Approach for Determining Fat Content in Cow Milk Using Image Processing |
| title_full | Introducing a Rapid and Practical Approach for Determining Fat Content in Cow Milk Using Image Processing |
| title_fullStr | Introducing a Rapid and Practical Approach for Determining Fat Content in Cow Milk Using Image Processing |
| title_full_unstemmed | Introducing a Rapid and Practical Approach for Determining Fat Content in Cow Milk Using Image Processing |
| title_short | Introducing a Rapid and Practical Approach for Determining Fat Content in Cow Milk Using Image Processing |
| title_sort | introducing a rapid and practical approach for determining fat content in cow milk using image processing |
| topic | non-destructive analysis artificial neural network color milk quality assessment fat content cow milk |
| url | https://bbr.uk.ac.ir/article_4557_be3752d3e4b78dd546540775348dac48.pdf |
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