基于多光谱成像技术的玉米赭曲霉菌无损检测

玉米极易感染赭曲霉菌，对人体健康构成严重危害。传统的赭曲霉菌检测方法费时费力且具有破坏性，因此需开发一种快速、无损检测方法来监测玉米中的赭曲霉菌。采用多光谱成像技术结合化学计量学方法，获得赭曲霉菌定量检测和赭曲霉菌感染程度定性判定的最佳模型。结果表明，与偏最小二乘(PLS)和最小二乘支持向量机(LS-SVM)相比，反向传播神经网络(BPNN)的预测性能最好，预测集相关系数(Rp)为0.9494，建模集均方根误差(RMSEC)和预测集均方根误差(RMSEP)最低，分别为2.6693和2.2743 CFU/g。此外，在感染程度的鉴别试验中，BPNN预测效果也最好，其建模集鉴别准确率(Ac)和预测集鉴别准确率(Ap)均达到100%。结论:多光谱成像技术与化学计量学方法相结合，为玉米中赭曲霉菌的监测提供了一个有效的方法。

Non-destructive Detection of Aspergillus ochraceus in Corn Based on Multispectral Imaging Technology

Corn is easily infected by Aspergillus ochraceus， which is severely hazardous to human health. As the traditional methods for Aspergillus ochraceus detection are time-consuming and destructive， it is necessary to develop a rapid and non-destructive method for monitoring the growth of Aspergillus ochraceus in corn during storage. In this study， multispectral imaging technology combined with chemometric methods was used to obtain the optimal model for predicting the count of Aspergillus ochraceus quantitively and classifying the infection degree qualitatively. The results showed that compared with partial least square (PLS) and least square-support vector machine (LS-SVM)， back propagation neural network (BPNN) showed the best prediction performance with correlation coefficient of prediction (Rp) value of 0.9494， and the lowest root-mean-square error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) values of 2.6693 and 2.2743 CFU/g， respectively. In addition， for the classification experiment of the infective degree， BPNN was also the best prediction model with the accuracy of calibration(Ac) and the accuracy of prediction(Ap) both reached 100%. The results indicated that multispectral imaging combined with chemometric methods provided a promising technique to evaluate the infection of Aspergillus ochraceus in corn.