Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food
Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. Therefore, the early detection of mycotoxin contamination in pet food is crucial for ensuring the safety and well-being of animals. This study aims to deve...
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MDPI AG
2024-12-01
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| Series: | Toxins |
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| author | Zhenlong Wang Wei An Jiaxue Wang Hui Tao Xiumin Wang Bing Han Jinquan Wang |
| author_facet | Zhenlong Wang Wei An Jiaxue Wang Hui Tao Xiumin Wang Bing Han Jinquan Wang |
| author_sort | Zhenlong Wang |
| collection | DOAJ |
| description | Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. Therefore, the early detection of mycotoxin contamination in pet food is crucial for ensuring the safety and well-being of animals. This study aims to develop a rapid and cost-effective method using an electronic nose (E-nose) and machine learning algorithms to predict whether ZEN levels in pet food exceed the regulatory limits (250 µg/kg), as set by Chinese pet food legislation. A total of 142 pet food samples from various brands, collected between 2021 and 2023, were analyzed for ZEN contamination via liquid chromatography–tandem mass spectrometry. Additionally, the “AIR PEN 3” E-nose, equipped with 10 metal oxide sensors, was employed to identify volatile compounds in the pet food samples, categorized into 10 different groups. Machine learning algorithms, including liner regression, k-nearest neighbors, support vector machines, random forests, XGBoost, and multi-layer perceptron (MLP), were used to classify the samples based on their volatile profiles. The MLP algorithm showed the highest discrimination accuracy at 86.6% in differentiating between pet food samples above and below the ZEN threshold. Other algorithms showed moderate accuracy, ranging from 77.1% to 84.8%. The ensemble model, which combined the predictions from all classifiers, further improved the classification performance, achieving the highest accuracy at 90.1%. These results suggest that the combination of E-nose technology and machine learning provides a rapid, cost-effective approach for screening ZEN contamination in pet food at the market entry stage. |
| format | Article |
| id | doaj-art-a09f4e9c7b904b74ba4301a36524d153 |
| institution | Kabale University |
| issn | 2072-6651 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Toxins |
| spelling | doaj-art-a09f4e9c7b904b74ba4301a36524d1532024-12-27T14:57:01ZengMDPI AGToxins2072-66512024-12-01161255310.3390/toxins16120553Machine Learning for Predicting Zearalenone Contamination Levels in Pet FoodZhenlong Wang0Wei An1Jiaxue Wang2Hui Tao3Xiumin Wang4Bing Han5Jinquan Wang6Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, ChinaKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, ChinaKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, ChinaKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, ChinaKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, ChinaKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, ChinaKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, ChinaZearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. Therefore, the early detection of mycotoxin contamination in pet food is crucial for ensuring the safety and well-being of animals. This study aims to develop a rapid and cost-effective method using an electronic nose (E-nose) and machine learning algorithms to predict whether ZEN levels in pet food exceed the regulatory limits (250 µg/kg), as set by Chinese pet food legislation. A total of 142 pet food samples from various brands, collected between 2021 and 2023, were analyzed for ZEN contamination via liquid chromatography–tandem mass spectrometry. Additionally, the “AIR PEN 3” E-nose, equipped with 10 metal oxide sensors, was employed to identify volatile compounds in the pet food samples, categorized into 10 different groups. Machine learning algorithms, including liner regression, k-nearest neighbors, support vector machines, random forests, XGBoost, and multi-layer perceptron (MLP), were used to classify the samples based on their volatile profiles. The MLP algorithm showed the highest discrimination accuracy at 86.6% in differentiating between pet food samples above and below the ZEN threshold. Other algorithms showed moderate accuracy, ranging from 77.1% to 84.8%. The ensemble model, which combined the predictions from all classifiers, further improved the classification performance, achieving the highest accuracy at 90.1%. These results suggest that the combination of E-nose technology and machine learning provides a rapid, cost-effective approach for screening ZEN contamination in pet food at the market entry stage.https://www.mdpi.com/2072-6651/16/12/553mycotoxinzearalenonemachine learningE-nosepet food |
| spellingShingle | Zhenlong Wang Wei An Jiaxue Wang Hui Tao Xiumin Wang Bing Han Jinquan Wang Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food Toxins mycotoxin zearalenone machine learning E-nose pet food |
| title | Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food |
| title_full | Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food |
| title_fullStr | Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food |
| title_full_unstemmed | Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food |
| title_short | Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food |
| title_sort | machine learning for predicting zearalenone contamination levels in pet food |
| topic | mycotoxin zearalenone machine learning E-nose pet food |
| url | https://www.mdpi.com/2072-6651/16/12/553 |
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