Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy.
Department of Livestock Population Genomics, University of Hohenheim, Garbenstraβe 17, 70599 Stuttgart, Germany.
Toxins (Basel). 2022 Sep 3;14(9):617. doi: 10.3390/toxins14090617.
Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014−2015 and 2017−2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose “AIR PEN 3” (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach “Classification and regression trees” (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 μg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method.
真菌毒素是食品安全和饲料安全的重大隐患，小麦是最易受其污染的作物之一。为了解决这个问题，需要快速、可靠且低成本的检测方法来检测法规规定的真菌毒素。本研究旨在评估电子鼻在早期识别小麦样品中脱氧雪腐镰刀菌烯醇（DON）含量超过固定阈值方面的潜在应用。在 2014-2015 年和 2017-2018 年期间，从意大利北部的商业农田中采集了 214 个小麦样本，使用传统方法（GC-MS）和便携式电子鼻“AIR PEN 3”（Airsense Analytics GmbH，Schwerin，德国）对 DON 污染进行了分析，后者配备了 10 个金属氧化物传感器，用于检测不同类别的挥发性物质。使用“分类回归树”（CART）机器学习方法对样品进行分类，根据 DON 污染的四个阈值（1750、1250、750 和 500μg/kg）进行分类。总的来说，该过程的准确率超过 83%（正确预测了小麦样品中 DON 的含量）。这些结果表明，电子鼻结合 CART 可以作为一种有效的快速方法，用于区分合规和 DON 污染的小麦批次。在得出该方法的有效性结论之前，需要进一步验证更多超过法定限量的样本。
