[ Objective] The paper was to study the application d Fourier transform infrared spectroscopy （FTIR technology） in identification of peanut diseases. [Method] By using FFIR technology, combined with the methods of p...[ Objective] The paper was to study the application d Fourier transform infrared spectroscopy （FTIR technology） in identification of peanut diseases. [Method] By using FFIR technology, combined with the methods of principal component analysis and hierarchical cluster analysis, the healthy leaves and three kinds of diseased leaves （ eereospera black spot, cercospora brown spot and web blotch） were identified in the paper. [ Result ] IR spectra of both diseased and healthy samples were similar, but tiny differences in wave-numbers and absorption intensities of peaks were observed in the range of 1 750 -800 cm-1. Significant differences were found in second derivative speclra in the range of 3 600 - 2 800 and 1 750 -650 cm-1 which were selected to perform principle component and hier- archical cluster analysis. Three principal components had the cumulative contribution rate of 94.9% ; the correct rate of principal component analysis for classifica- tion was 100% and the correct rate of hierarchical cluster analysis for identification reached 94.6%. [ Conclusion] Fourier transform infrared spectroscopy has a tmtential to be develoned as a nowerful means for identification of cren diseases.展开更多
基金Supported by National Natural Science Foundation of China (30960179)Scientific Research Fund of Yunnan Provincial Department of Education (2012J096,2011Z013)
文摘[ Objective] The paper was to study the application d Fourier transform infrared spectroscopy （FTIR technology） in identification of peanut diseases. [Method] By using FFIR technology, combined with the methods of principal component analysis and hierarchical cluster analysis, the healthy leaves and three kinds of diseased leaves （ eereospera black spot, cercospora brown spot and web blotch） were identified in the paper. [ Result ] IR spectra of both diseased and healthy samples were similar, but tiny differences in wave-numbers and absorption intensities of peaks were observed in the range of 1 750 -800 cm-1. Significant differences were found in second derivative speclra in the range of 3 600 - 2 800 and 1 750 -650 cm-1 which were selected to perform principle component and hier- archical cluster analysis. Three principal components had the cumulative contribution rate of 94.9% ; the correct rate of principal component analysis for classifica- tion was 100% and the correct rate of hierarchical cluster analysis for identification reached 94.6%. [ Conclusion] Fourier transform infrared spectroscopy has a tmtential to be develoned as a nowerful means for identification of cren diseases.
