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In order to apply the bionic electronic nose in classifying the litchi into different classes, there were five different litchi varieties tested by the proposed methods in this study. Firstly, Physical differences of the 5 litchi varieties were compared in this study. Secondly, the response curves from the electronic nose (PEN3) were recorded for all the samples of the five litchi varieties. Variance Analysis (VA) was used for best characteristic value selection. Finally, via different pattern recognition techniques, including the Principal Component Analysis (PCA), the Linear Discrimination Analysis (LDA), the Probabilistic Neural Network (PNN), the Support Vector Machine (SVM) and the loading analysis (Loadings), it is found that PCA and LDA have a poor performance in classifying litchi varieties. The classification accuracy of the PNN model with training set and test set were 100 and 84%, respectively. As to the SVM model, the classification accuracy of training set and test set were 100 and 92%, respectively. According to the Loadings results, the sensors R3, R5, R8 and R1 can be chosen for developing special and simple instruments for the detection of litchi volatiles. The test results has demonstrated the feasibility and effectiveness of using bionic electronic nose for discriminating and classifying litchi varieties, which provides a new method for rapid and nondestructive classification of litchi varieties.

Nondestructive Classification and Recognition of Litchi Varieties Using Bionic Electronic Nose