Abstract
Abstract Plants change the emission of induced volatiles in response to damage and herbivore attack, and monitoring the change of such volatiles could provide a nondestructive means of plant health measurement. Current monitoring techniques for plant volatiles are time-consuming and costly. The main objective of this research is to figure out whether electronic nose (e-nose) technique can be used to differentiate rice plants with different degrees of mechanical damage. A portable e-nose (PEN2) is used to characterize and classify rice plants subjected to three degrees of mechanical damage compared with undamaged control plants. Principle component analysis (PCA), Linear discriminant analysis (LDA), Stepwise discriminant analysis (SDA), and Back-propagation neural network (BPNN) are applied to evaluate the data. Different degrees of damaged rice plants are better distinguished using LDA than using PCA. The average correction ratio of testing set of BPNN is 75%. The results obtained indicate that it is poss...
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