Development of a mobile application for rapid detection of meat freshness using deep learning
The freshness or spoilage of meat is critical in terms of meat color and quality criteria. Detecting the condition of the meat is important not only for consumers but also for the processing of the meat itself. Meat quality is influenced by various pre-slaughter factors including housing conditions,...
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| Format: | Article |
| Language: | English |
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The V.M. Gorbatov All-Russian Meat Research Institute
2024-10-01
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| Series: | Теория и практика переработки мяса |
| Subjects: | |
| Online Access: | https://www.meatjournal.ru/jour/article/view/380 |
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| author | H. I. Kozan H. A. Akyürek |
| author_facet | H. I. Kozan H. A. Akyürek |
| author_sort | H. I. Kozan |
| collection | DOAJ |
| description | The freshness or spoilage of meat is critical in terms of meat color and quality criteria. Detecting the condition of the meat is important not only for consumers but also for the processing of the meat itself. Meat quality is influenced by various pre-slaughter factors including housing conditions, diet, age, genetic background, environmental temperature, and stress factors. Additionally, spoilage can occur due to the slaughtering process, though post-slaughter spoilage is more frequent and has a stronger correlation with postslaughter factors. The primary indicator of meat quality is the pH value, which can be high or low. Variations in pH values can lead to adverse effects in the final product such as color defects, microbial issues, short shelf life, reduced quality, and consumer complaints. Many of these characteristics are visible components of quality. This study aimed to develop a mobile application using deep learning-based image processing techniques for the rapid detection of freshness. The attributes of the source and the targeted predictions were found satisfactory, indicating that further advancements could be made in developing future versions of the application. |
| format | Article |
| id | doaj-art-85f0c8c397314fa29a2dbf992902f34d |
| institution | Matheson Library |
| issn | 2414-438X 2414-441X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | The V.M. Gorbatov All-Russian Meat Research Institute |
| record_format | Article |
| series | Теория и практика переработки мяса |
| spelling | doaj-art-85f0c8c397314fa29a2dbf992902f34d2025-08-04T13:11:45ZengThe V.M. Gorbatov All-Russian Meat Research InstituteТеория и практика переработки мяса2414-438X2414-441X2024-10-019324925710.21323/2414-438X-2024-9-3-249-257269Development of a mobile application for rapid detection of meat freshness using deep learningH. I. Kozan0H. A. Akyürek1Department of Food Processing, Meram Vocational School, Necmettin Erbakan UniversityDepartment of Avionics, Faculty of Aviation and Space Sciences, Necmettin Erbakan UniversityThe freshness or spoilage of meat is critical in terms of meat color and quality criteria. Detecting the condition of the meat is important not only for consumers but also for the processing of the meat itself. Meat quality is influenced by various pre-slaughter factors including housing conditions, diet, age, genetic background, environmental temperature, and stress factors. Additionally, spoilage can occur due to the slaughtering process, though post-slaughter spoilage is more frequent and has a stronger correlation with postslaughter factors. The primary indicator of meat quality is the pH value, which can be high or low. Variations in pH values can lead to adverse effects in the final product such as color defects, microbial issues, short shelf life, reduced quality, and consumer complaints. Many of these characteristics are visible components of quality. This study aimed to develop a mobile application using deep learning-based image processing techniques for the rapid detection of freshness. The attributes of the source and the targeted predictions were found satisfactory, indicating that further advancements could be made in developing future versions of the application.https://www.meatjournal.ru/jour/article/view/380meat qualityrapid detectiondeep learningred meat qualityimage processingflutterandroid |
| spellingShingle | H. I. Kozan H. A. Akyürek Development of a mobile application for rapid detection of meat freshness using deep learning Теория и практика переработки мяса meat quality rapid detection deep learning red meat quality image processing flutter android |
| title | Development of a mobile application for rapid detection of meat freshness using deep learning |
| title_full | Development of a mobile application for rapid detection of meat freshness using deep learning |
| title_fullStr | Development of a mobile application for rapid detection of meat freshness using deep learning |
| title_full_unstemmed | Development of a mobile application for rapid detection of meat freshness using deep learning |
| title_short | Development of a mobile application for rapid detection of meat freshness using deep learning |
| title_sort | development of a mobile application for rapid detection of meat freshness using deep learning |
| topic | meat quality rapid detection deep learning red meat quality image processing flutter android |
| url | https://www.meatjournal.ru/jour/article/view/380 |
| work_keys_str_mv | AT hikozan developmentofamobileapplicationforrapiddetectionofmeatfreshnessusingdeeplearning AT haakyurek developmentofamobileapplicationforrapiddetectionofmeatfreshnessusingdeeplearning |