Discrimination of different types damage of rice plants by electronic nose

Abstract

The profiles of volatile compounds emitted by plants varies in response to damage or herbivore attack. The potential of electronic nose technology to monitor such changes, with the aim of diagnosing plant health was investigated. An electronic nose (E-nose) was used to analyse rice plants that were subjected to different types of treatments causing damage, and the results were compared to those of undamaged control plants. Principal component analysis (PCA), linear discrimination analysis (LDA), cluster analysis (CA), back-propagation neural network (BPNN), and learning vector quantisation (LVQ) network were used to evaluate the E-nose data. The results indicated that the E-nose can successfully discriminate between rice plants with different types of damage. The discrimination was more pronounced after the LDA than after the PCA. The front 5 principal component values of the PCA were extracted and they acted as the input date for the neural network analyses. Good discrimination results were obtained using these front 5 principal component values in LVQ and BPNN. The results demonstrated that it is plausible to use E-nose technology as a method for monitoring rice cultivation practices.