基于电子鼻技术的马铃薯真菌性腐烂病早期检测

确定马铃薯致腐菌种,并建立快速检测方法对腐烂样本进行识别。本研究通过微生物及分子生物学方法对马铃薯致腐菌进行鉴定,采用电子鼻方法对样本进行检测,建立模型对不同感染阶段样本进行识别。结果显示:马铃薯主要致腐烂菌为出芽短梗霉,建立的K最近邻判别模型中,训练集与预测集识别率分别为90%和85%;建立的BP网络判别模型中,训练集与预测集判别率分别达到93.75%和90%,各腐烂阶段能够较好地被识别。研究结果为后期电子鼻技术应用至马铃薯腐烂病检测提供理论基础。

Early Detection of Potato Rot Disease Caused by Fungal Based on Electronic Nose Technology

Determining potato rot species and establishing rapid detection methods to identify rot samples. In this study,the main pathogenic bacteria were identified by microbiological and molecular biology methods. The electronic nose was trained to recognize volatile compounds emitted by potatoes experimentally infected with the pathogens. Models were established to identify different stages of rot samples. The results showed that Aureobasidium pullulans was the main pathogenic bacteria of potatoes. The recognition rate of the K-nearest neighbor model was 90% for training set and 85% for predicting set. The recognition rate of the BP-ANN model was 93.75% for training set and 90% for predicting set. All stages of infection were well identified. The results provided a theoretical basis for the later application of electronic nose technology to the detection of rot disease of potato.