自制电子鼻检测霉变大米

为对不同霉变程度的大米实现快速鉴别，研制了一套以LabVIEW为平台用于检测大米霉变的电子鼻系统。通过霉菌孢子液侵染正常大米，使用该电子鼻系统对不同天数掺入不同比例霉米的大米样品挥发物进行检测，对采集数据进行主成分分析（PCA）、线性判别分析（LDA），最后使用反向传播（back propagation，BP）神经网络建立预测模型。结果表明，得分图显示正常大米和霉变大米挥发物差异性显著，LDA分类效果优于PCA；所建立的模型预测值和实际值相关性达0.953以上，训练集和测试集平均相对误差分别为3.56%、4.18%，训练集和测试集对于正常大米样本识别率为100%。综上，电子鼻系统可以作为霉变大米无损检测的有效手段，在大米品质鉴别方面具有实际应用意义。

Detection of Moldy Rice by Self-made Electronic Nose

In order to identify the moldy rice rapidly, an electronic nose system based on LabVIEW was developed. The volatiles of rice samples mixed with different proportions of moldy rice in different days were detected by the electronic nose system. Principal component analysis (PCA) and linear discriminant analysis (LDA) were performed on the collected data. Finally, back propagation (BP) neural network was used to establish the prediction model. The results showed that, there was significant difference in volatile matter between normal rice and moldy rice volatiles, and the LDA classification effect was better than PCA. The correlation between predicted value and actual value of the model was more than 0.953, the average relative error of training set and test set was 3.56% and 4.18%, and the recognition rate of training set and test set was 100% for normal rice samples. In conclusion, the electronic nose system could be used as an effective means of non-destructive detection of moldy rice, and had practical significance in rice quality identification.