Potential of vibrational spectroscopy for rapid and accurate determination of the hydrogen peroxide treatment of plant leaves.

Abstract

Detection and characterization of interactions between crop plants and hydrogen peroxide (H2O2) is significant for the exploration of the mechanisms in plant pathology. The objective of this research is to estimate spectral characteristics of rapeseed leaves (Brassica napus L.) during treatment with different H2O2 concentrations (0, 0.5, 1.0, and 3.0mmol/L) by using Raman spectroscopy (RS) (800-1800cm-1) and hyperspectral imaging (HSI) (400-1000nm). Cluster analysis of RS and HSI data between the control and treated samples was conducted using kernel principal component analysis (KPCA) and principal component analysis (PCA), respectively. Characteristic Raman shifts at 1012, 1163, and 1530cm-1 and hyperspectral featured wavelengths at 452, 558, 655, and 703nm were selected for discriminating control and treated samples. The one-way analysis of variance (ANOVA) was applied to demonstrate the significant difference in spectral signatures of samples, and results showed that 452nm is promising to assess the control and treated samples at the p<0.05 level. The featured Raman shifts and hyperspectral wavelengths were employed to establish least squares-support vector machine (LS-SVM) discriminative models. The approach of multiple-level data fusion of 1163cm-1 combined with 452nm produced the best recognize rate (RR) of 81.7% to detect the control and treated leaves than other models. Therefore, the results encouraged multiple sensor fusion to improve models for better model performance and to detect plant treatment situations with H2O2 solutions.