Wang Zhenlong, An Wei, Wang Jiaxue, Tao Hui, Wang Xiumin, Han Bing, Wang Jinquan
Key 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, China.
Laboratory of Pet Nutrition and Food, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, China.
Toxins (Basel). 2024 Dec 23;16(12):553. doi: 10.3390/toxins16120553.
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.
在宠物食品原料和成品中均检测到了玉米赤霉烯酮（ZEN），它会对宠物造成急性毒性和慢性健康问题。因此，早期检测宠物食品中的霉菌毒素污染对于确保动物的安全和健康至关重要。本研究旨在开发一种快速且经济高效的方法，利用电子鼻（E-nose）和机器学习算法来预测宠物食品中的ZEN含量是否超过中国宠物食品法规规定的监管限值（250微克/千克）。通过液相色谱-串联质谱法对2021年至2023年间收集的来自不同品牌的142份宠物食品样品进行了ZEN污染分析。此外，使用配备10个金属氧化物传感器的“AIR PEN 3”电子鼻来识别宠物食品样品中的挥发性化合物，这些化合物被分为10个不同的组。包括线性回归、k近邻、支持向量机、随机森林、XGBoost和多层感知器（MLP）在内的机器学习算法被用于根据样品的挥发性特征对其进行分类。MLP算法在区分ZEN阈值以上和以下的宠物食品样品时显示出最高的判别准确率，为86.6%。其他算法显示出中等准确率，范围在77.1%至84.8%之间。结合所有分类器预测结果的集成模型进一步提高了分类性能，达到了最高准确率90.1%。这些结果表明，电子鼻技术和机器学习的结合为在市场准入阶段筛选宠物食品中的ZEN污染提供了一种快速、经济高效的方法。
