Fourier transform infrared (FTIR) spectroscopy was used to study diseased leaves in broad bean. Results showed that the infrared spectra of different broad bean diseased leaves were similar, which were mainly made u...Fourier transform infrared (FTIR) spectroscopy was used to study diseased leaves in broad bean. Results showed that the infrared spectra of different broad bean diseased leaves were similar, which were mainly made up of the vibrational absorption bands of protein,lipid and polysaccharide.There were minor differences in-cluding the spectral peak position, peak shape and the absorption intensity in the range of 1 800-1 300 cm-1. There were obvious differences among their second derivative spectra in the range of 1 800-1 300 cm-1. After the procedure of the Fourier self-deconvolution and curve fitting of health bean leaves and broad bean diseased leaves in the range of 1 700-1 500 cm-1, three sub-peaks were obtained at 1 550 cm-1 (protein amide Ⅱ band), 1 605 cm-1 (lignin) and 1 650 cm-1 (protein amide I band).The ratios of relative areas of the bands of amide Ⅱ, lignin, and amide I were 38.86%, 28.68% and 32.47% in the spectra of healthy leaves, respec-tively. It was distinguished from the diseased leaves (chocolate spot leaf: 15.42%, 42.98% and 41.61%, ring spot leaf:32.39%, 35.63% and 31.98%, rust leaf: 13.97%, 46.40% and 39.65%, yel owing leaf curl disease leaf: 24.01%,36.55% and 39.44%). For sub-peak area ratios (A1 563/A1 605, A1 650/A1 605 and A1 563/A1 654), those of four kinds of diseased leaves were smal er than that of healthy leaves, and there were also differences among four kinds of diseased leaves. The results proved that FTIR combining with curve fitting might be a potential y useful tool for detecting different kinds of broad bean diseases.展开更多
[Objective] The aim was to indentify diseased leaves of broad bean by vibra- tional spectroscopy. [Method] In this paper, broad bean rust, fusarium rhizome rot, broad bean zonate spot, yellow leaf curl virus and norma...[Objective] The aim was to indentify diseased leaves of broad bean by vibra- tional spectroscopy. [Method] In this paper, broad bean rust, fusarium rhizome rot, broad bean zonate spot, yellow leaf curl virus and normal leaves were studied using Fourier transform infrared spectroscopy combined with chemometrics. [Result] The spectra of the samples were similar, only with minor differences in absorption inten- sity of several peaks. Second derivative analyses show that the significant difference of all samples was in the range of 1 200-700 cm2. The data in the range of 1 200- 700 cm' were selected to evaluate correlation coefficients, hierarchical cluster analy- sis (HCA) and principal component analysis (PCA). Results showed that the correla- tion coefficients are larger than 0.928 not only between the healthy leaves, but also between the same diseased leaves. The values between healthy and diseased leaves, and among diseased leaves, are all declined. HCA and PCA yielded about 73.3% and 82.2% accuracy, respectively. [Conclusion] This study demonstrated that FTIR techniques might be used to detect crop diseases.展开更多
基金Supported by National Natural Science Foundation of China(30960179)Program for Innovative Research Team in Science and Technology in University of Yunnan Province~~
文摘Fourier transform infrared (FTIR) spectroscopy was used to study diseased leaves in broad bean. Results showed that the infrared spectra of different broad bean diseased leaves were similar, which were mainly made up of the vibrational absorption bands of protein,lipid and polysaccharide.There were minor differences in-cluding the spectral peak position, peak shape and the absorption intensity in the range of 1 800-1 300 cm-1. There were obvious differences among their second derivative spectra in the range of 1 800-1 300 cm-1. After the procedure of the Fourier self-deconvolution and curve fitting of health bean leaves and broad bean diseased leaves in the range of 1 700-1 500 cm-1, three sub-peaks were obtained at 1 550 cm-1 (protein amide Ⅱ band), 1 605 cm-1 (lignin) and 1 650 cm-1 (protein amide I band).The ratios of relative areas of the bands of amide Ⅱ, lignin, and amide I were 38.86%, 28.68% and 32.47% in the spectra of healthy leaves, respec-tively. It was distinguished from the diseased leaves (chocolate spot leaf: 15.42%, 42.98% and 41.61%, ring spot leaf:32.39%, 35.63% and 31.98%, rust leaf: 13.97%, 46.40% and 39.65%, yel owing leaf curl disease leaf: 24.01%,36.55% and 39.44%). For sub-peak area ratios (A1 563/A1 605, A1 650/A1 605 and A1 563/A1 654), those of four kinds of diseased leaves were smal er than that of healthy leaves, and there were also differences among four kinds of diseased leaves. The results proved that FTIR combining with curve fitting might be a potential y useful tool for detecting different kinds of broad bean diseases.
基金Supported by National Natural Science Foundation of China(30960179)Natural Science Foundation of Yunnan Province(2007A048M)~~
文摘[Objective] The aim was to indentify diseased leaves of broad bean by vibra- tional spectroscopy. [Method] In this paper, broad bean rust, fusarium rhizome rot, broad bean zonate spot, yellow leaf curl virus and normal leaves were studied using Fourier transform infrared spectroscopy combined with chemometrics. [Result] The spectra of the samples were similar, only with minor differences in absorption inten- sity of several peaks. Second derivative analyses show that the significant difference of all samples was in the range of 1 200-700 cm2. The data in the range of 1 200- 700 cm' were selected to evaluate correlation coefficients, hierarchical cluster analy- sis (HCA) and principal component analysis (PCA). Results showed that the correla- tion coefficients are larger than 0.928 not only between the healthy leaves, but also between the same diseased leaves. The values between healthy and diseased leaves, and among diseased leaves, are all declined. HCA and PCA yielded about 73.3% and 82.2% accuracy, respectively. [Conclusion] This study demonstrated that FTIR techniques might be used to detect crop diseases.