Key Laboratory of Grain Information Processing and Control, Henan University of Technology, Ministry of Education, Zhengzhou, China.
Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou, China.
Sci Rep. 2022 Jun 21;12(1):10425. doi: 10.1038/s41598-022-14344-1.
It is widely known that mold is one of important indices in assessing the quality of stored wheat. First, mold will decrease the quality of wheat kernels; the wheat kernels infected by mold can produce secondary metabolites, such as aflatoxins, ochratoxin A, zearalenone, fumonisins and so on. Second, the mycotoxins metabolized by mycetes are extremely harmful to humans; once the food or feed is made of by those wheat kernels infected by mold, it will cause serious health problems on human beings as well as animals. Therefore, the effective and accurate detection of wheat mold is vitally important to evaluate the storage and subsequent processing quality of wheat kernels. However, traditional methods for detecting wheat mold mainly rely on biochemical methods, which always involve complex and long pretreatment processes, and waste part of wheat samples for each detection. In view of this, this paper proposes a type of eco-friendly and nondestructive wheat mold detection method based on ultra weak luminescence. The specific implementation process is as follows: firstly, ultra weak luminescence signals of the healthy and the moldy wheat subsamples are measured by a photon analyzer; secondly, the approximate entropy and multiscale approximate entropy are introduced as the main classification features separately; finally, the detection model has been established based on the support vector machine in order to classify two types of wheat subsamples. The receiver operating characteristic curve of the newly established detection model shows that the highest classification accuracy rate can reach 93.1%, which illustrates that our proposed detection model is feasible and promising for detecting wheat mold.
众所周知，霉菌是评价储存小麦质量的重要指标之一。首先，霉菌会降低麦粒的品质；被霉菌感染的麦粒会产生次级代谢物，如黄曲霉毒素、赭曲霉毒素 A、玉米赤霉烯酮、伏马菌素等。其次，霉菌代谢产生的真菌毒素对人类危害极大；一旦由这些被霉菌感染的麦粒制成的食物或饲料，会对人类和动物造成严重的健康问题。因此，有效准确地检测小麦霉菌对评估麦粒的储存和后续加工质量至关重要。然而，传统的小麦霉菌检测方法主要依赖于生化方法，这些方法往往涉及复杂且冗长的预处理过程，并且每次检测都会浪费一部分小麦样本。针对这一问题，本文提出了一种基于超弱发光的环保、无损的小麦霉菌检测方法。具体实施过程如下：首先，用光子分析仪测量健康和霉变小麦样本的超弱发光信号；其次，分别引入近似熵和多尺度近似熵作为主要分类特征；最后，基于支持向量机建立检测模型，以对两种类型的小麦样本进行分类。新建立的检测模型的受试者工作特征曲线表明，最高分类准确率可达 93.1%，这表明我们提出的检测模型对于检测小麦霉菌是可行且有前景的。
