Quality assessment of chicken using machine learning and electronic nose
Meat is highly perishable food and prone to microbial contamination under various storage conditions. Quality assessment at both retail and industrial levels often relies on organoleptic properties, gas chromatography, and total bacterial count, all of which require trained personnel and significant...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Sensing and Bio-Sensing Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214180425000054 |
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| author | Hassan Anwar Talha Anwar |
| author_facet | Hassan Anwar Talha Anwar |
| author_sort | Hassan Anwar |
| collection | DOAJ |
| description | Meat is highly perishable food and prone to microbial contamination under various storage conditions. Quality assessment at both retail and industrial levels often relies on organoleptic properties, gas chromatography, and total bacterial count, all of which require trained personnel and significant resources. As a result, there is a need for a more efficient and reliable system to determine chicken quality. This study investigates the use of an electronic nose system—a sensor array that detects odors and generates data, which is then analyzed by machine learning algorithms to predict chicken freshness. An electronic nose system was developed using six MQ gas sensors and one humidity temperature sensor. Data was collected from chicken samples over a period of 15 days. To evaluate the performance of the machine learning algorithms, different data splitting approaches were tested to understand their impact on model accuracy. Random Forest achieved 100 % accuracy with randomly split data and 69 % accuracy with non-randomly split data. Support Vector Machine, using the recursive feature elimination technique, attained 78.5 % accuracy without random splitting. The study also reviewed existing literature, highlighting that random data splitting is not suitable for electronic nose data. Overall, the findings suggest that the electronic nose system, combined with appropriate data handling and machine learning techniques, can effectively assess chicken freshness, potentially offering a valuable tool for the poultry industry. |
| format | Article |
| id | doaj-art-c6612849c3844dd0b4ad017b6ab6e072 |
| institution | Kabale University |
| issn | 2214-1804 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sensing and Bio-Sensing Research |
| spelling | doaj-art-c6612849c3844dd0b4ad017b6ab6e0722025-01-11T06:41:24ZengElsevierSensing and Bio-Sensing Research2214-18042025-02-0147100739Quality assessment of chicken using machine learning and electronic noseHassan Anwar0Talha Anwar1Department of Food Safety & Quality Management, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan 60000, Pakistan; Corresponding author.Independent Researcher, Multan, PakistanMeat is highly perishable food and prone to microbial contamination under various storage conditions. Quality assessment at both retail and industrial levels often relies on organoleptic properties, gas chromatography, and total bacterial count, all of which require trained personnel and significant resources. As a result, there is a need for a more efficient and reliable system to determine chicken quality. This study investigates the use of an electronic nose system—a sensor array that detects odors and generates data, which is then analyzed by machine learning algorithms to predict chicken freshness. An electronic nose system was developed using six MQ gas sensors and one humidity temperature sensor. Data was collected from chicken samples over a period of 15 days. To evaluate the performance of the machine learning algorithms, different data splitting approaches were tested to understand their impact on model accuracy. Random Forest achieved 100 % accuracy with randomly split data and 69 % accuracy with non-randomly split data. Support Vector Machine, using the recursive feature elimination technique, attained 78.5 % accuracy without random splitting. The study also reviewed existing literature, highlighting that random data splitting is not suitable for electronic nose data. Overall, the findings suggest that the electronic nose system, combined with appropriate data handling and machine learning techniques, can effectively assess chicken freshness, potentially offering a valuable tool for the poultry industry.http://www.sciencedirect.com/science/article/pii/S2214180425000054Artificial intelligenceMeat quality assessmentMachine learningSensors |
| spellingShingle | Hassan Anwar Talha Anwar Quality assessment of chicken using machine learning and electronic nose Sensing and Bio-Sensing Research Artificial intelligence Meat quality assessment Machine learning Sensors |
| title | Quality assessment of chicken using machine learning and electronic nose |
| title_full | Quality assessment of chicken using machine learning and electronic nose |
| title_fullStr | Quality assessment of chicken using machine learning and electronic nose |
| title_full_unstemmed | Quality assessment of chicken using machine learning and electronic nose |
| title_short | Quality assessment of chicken using machine learning and electronic nose |
| title_sort | quality assessment of chicken using machine learning and electronic nose |
| topic | Artificial intelligence Meat quality assessment Machine learning Sensors |
| url | http://www.sciencedirect.com/science/article/pii/S2214180425000054 |
| work_keys_str_mv | AT hassananwar qualityassessmentofchickenusingmachinelearningandelectronicnose AT talhaanwar qualityassessmentofchickenusingmachinelearningandelectronicnose |