A Fast Detection Method for Wheat Mould Based on Biophotons

Abstract Mould is a common phenomenon in stored wheat. First, mould will decrease the quality of wheat kernels. Second, the mycotoxins metabolized by mycetes are very harmful for humans. Therefore, the fast and accurate examination of wheat mould is vitally important to evaluating its storage quality and subsequent processing safety. Existing methods for examining wheat mould mainly rely on chemical methods, which always involve complex and long pretreatment processes, and the auxiliary chemical materials used in these methods may pollute our environment. To improve the determination of wheat mould, this paper proposed a type of green and nondestructive determination method based on biophotons. The specific implementation process is as follows: first, the ultra-weak luminescence between healthy and mouldy wheat samples are measured repeatedly by a biophotonic analyser, and then, the approximate entropy and multiscale approximate entropy are separately introduced as the main classification features. Finally, the classification performances have been tested using the support vector machine(SVM). The ROC curve of the newly established classification model shows that the highest recognition rate can reach 93.6%, which shows that our proposed classification model is feasible and promising for detecting wheat mould.