Classification and recognition of hybrid and inbred rough rice based on bionic electronic nose

Abstract: The bionic electronic nose, a machine detection method based on bionic olfaction, enjoys a good application prospect in rice varieties classification and recognition. There are many differences between hybrid and inbred rice. In order to understand the feasibility by using an electronic nose to classify and recognize hybrid and inbred rough rice varieties, the samples' volatiles of 3 inbred rough rice varieties (Zhongxiang 1, Xiangwan 13, Yaopingxiang) and 3 hybrid rough rice varieties (Wufengyou T025, Pin 36, Youyou 122), which grow in the same area and the same season, were collected in this article by using an electronic nose (PEN3). Firstly, the contribution rates of sensors, which in sampling rough rice volatile, were analyzed by using loadings analysis, and the electronic nose's sensors in the array were selected based on feature value extraction and rough rice volatile detection. It is indicated that the sensors are keenly sensitive to sulfur-containing organics, nitrogen oxides, aromatics, and sulfur- and chlorine-containing organics, and it should be mainly used in classified rough rice varieties. After that, classification and recognition algorithms of Hybrid and Inbred Rough Rice, including PCA (principal component analysis), LDA (linear discriminant analysis), and BP neural network analysis, were developed. Results show that PCA and LDA analysis for the classification between 6 rough rice varieties did not achieve the ideal results. Neither did the classification between hybrid and inbred rough rice. There are some overlapping regions between the classification groups. It is easy to cause a blur in practical application. Compared with PCA and LDA, BP neural network has better performance in the classification of 6 different rough rice varieties, the same effect in the classification between hybrid and inbred rough rice. By using BP neural network, test results show that the accuracy of classification between 6 different rough rice varieties reaches 90% in testing samples test. For classification between hybrid and inbred rough rice, it reaches 96.7% in testing samples test. It is indicated that the bionic Electronic Nose could be a new approach, which can conduct a rapid and non-destructive classification of hybrid and inbred rough rice varieties.