A novel detection method for wheat aging based on the delayed luminescence.

Wheat aging plays an important role in assessing storage wheat quality and its subsequent processing purposes. The conventional detection methods for wheat aging are mainly involved in chemical techniques, which are time-consuming as well as waste part of wheat samples for each detection. Although some physical detection methods have obtained gratifying results, it is extremely hard to expand their application fields but to stay in the theory stage. For this reason, a novel nondestructive detection model for wheat aging based on the delayed luminescence (DL) has been proposed in this paper. Specifically, after collecting enough sample data, we first took advantage of certain hyperbolic function to fit DL signal, and then used four parameters of the hyperbolic function to feature the decay trend of the DL signal. Secondly, in order to better feature the DL signal, we extracted other six features together with above four features to form the input feature vector. Finally, as the bidirectional long short-term memory (Bi-LSTM) network lacked error-correcting performance, the Bi-LSTM network based on Walsh coding (Walsh-Bi-LSTM) mechanism was proposed to establish the detection model, which made the detection model have the error-correcting performance by reasonably splitting the multi-classification target task. Shown by experimental results, the newly proposed wheat aging detection model is able to achieve 94.00% accuracy in the testing dataset, which can be used as a green and nondestructive method to timely reflect wheat aging states.

An effective detection method for wheat mold based on ultra weak luminescence.

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.