Search

Identification of plant-pathogenic fungi is time-consuming due to cultivation and microscopic examination and can be influenced by the interpretation of the micro-morphological characters observed. The present investigation aimed to create a simple but sophisticated method for the identification of plant-pathogenic fungi by Fourier transform infrared (FTIR) spectroscopy. In this study, FTIR-attenuated total reflectance (ATR) spectroscopy was used in combination with chemometric analysis for identification of important pathogenic fungi of horticultural plants. Mixtures of mycelia and spores from 27 fungal strains belonging to nine different families were collected from liquid PD or solid PDA media cultures and subjected to FTIR-ATR spectroscopy measurements. The FTIR-ATR spectra ranging from 4 000 to 400 cm-1 were obtained. To classify the FTIR-ATR spectra, cluster analysis was compared with canonical vitiate analysis (CVA) in the spectral regions of 3 050～2 800 and 1 800～900 cm-1. Results showed that the identification accuracies achieved 97.53% and 99.18% for the cluster analysis and CVA analysis, respectively, demonstrating the high potential of this technique for fungal strain identification.

Clubroot, caused by Plasmodiophora brassicae, is considered the most devastating soilborne disease in Brassica crops. It has emerged as a serious disease threatening the cruciferous crop production industry in China. Nowadays, the detection techniques for P. brassicae are laborious, time-consuming and low sensitivity. Rapid and effective detection methods are needed. The objective of this study is to develop a Fourier transform infrared spectrometer (FTIR) technique for detection of P. brassicae effectively and accurately. FTIR and Real-time PCR techniques were applied in quantitative detection of P. brassicae. Chinese cabbages were inoculated with P. brassicae. By analyzing the FTIR spectra of P. brassicae, infected clubroots and healthy roots, three specific bands 1 105, 1 145 and 1 228 cm-1 were selected. According to the correlation between the peak areas at these sensitive bands and Real-time PCR Ct value, quantitative evaluation model of P. brassicae was established based on FTIR y=34. 17 +12. 24x - 9. 81x2 - 6. 05x3, r=0. 98 (p<0. 05). To validate accuracy of the model, 10 clubroot samples were selected randomly from field, and detected by FTIR spectrum model, the results showed that the average error is 1. 60%. This demonstrated that the FTIR technology is an available one for the quantitative detection of P. brassicae in clubroot, and it provides a new method for quantitative and quickly detection of Chinese cabbage clubroot.

Fourier transform infrared (FTIR) attenuated total reflectance (ATR) spectroscopy was used in combination with multivariate statistic analysis for identification of soil-borne fungi that causes severe economic damage to agriculture: Fusarium monili forme, Fusarium semitectum, Fusarium oxysporum, Fusarium solani, Rhizoctonia solani, Sclerotinia sclerotiorum, Pythium aphanidermatum and Phytophthora capsici. The original FTIR spectra were normalized, and the second derivatives were calculated, from which the peak wave numbers showing greatest variability were selected: 2924, 2854, 1745, 1641, 1547, 1466, 1406, 1376, 1306, 1240, 1201, 1152, 1109 and 1028 cm(-1). To discriminate different fungal strains, canonical discriminant analysis and cluster analysis were performed at these characteristic wave numbers. Results showed that the classification accuracies achieved 100% for different species of fungi, and classification accuracies for different fusarium strains achieved 95.56%, demonstrating the high potential of this technique for fungi identification.

Fourier transform infrared spectroscopy (FTIR) technique was applied in the early detection of corynespora spot on cucumber leaves while the symptom had not appeared. The cucumber leaves were inoculated with Corynespora cassiicola. By observing the changes in the FTIR spectra of infected cucumber leaves at various times of post-infection, three sensitive bands, 1 735, 1 545 and 1 240 cm(-1) were selected for the identification of cucumber corynespora leaf spot. According to the peak areas at these sensitive bands, cucumber leaf samples infected with C. cassiicola and control uninfected leaf samples could be classified correctly. Results clearly demonstrated that the FTIR technology is an available one for the early detection of corynespora spot on cucumber leaves while the symptom has not appeared and it provides a new method for the early detection of corynespora spot.

Fourier transform attenuated total reflection infrared spectroscopy (FTIR-ATR) has been a novel technical procedure to identify and classify microorganisms in recent years. In the present study, Fourier transform infrared spectroscopy(FTIR) in combination with an attenuated total reflection (ATR) unit were used to discriminate important plant-destroying fungi. Mycelia of 17 fungal strains belonging to 14 different species were grown on potato dextrose agar (PDA) plants and subjected to FTIR-ATR measurements. High-resolution and well-reproducibility infrared spectra were obtained, and significant spectral differences among these strains were observed in the wavenumber regions of 1800-1485 cm(-1), 1485-1185 cm(-1), and 1185-900 cm(-1). According to the characteristic bands in these regions, cluster analysis was executed to classify the FTIR spectra. The result showed that different fungal strains could be identified correctly, demonstrating the high potential of FTIR-ATR as a tool for fungal strain identification and classification. The method is rapid, inexpensive and reproducible, and requires minimum sample preparation.

Hyperspectral imaging (400-720 nm) and discriminate analysis were investigated for the detection of normal and diseased cucumber leaf samples with powdery mildew (Sphaerotheca fuliginea), angular leaf spot (Pseudomopnas syringae), downy mildew (Pseudoperonospora cubensis), and brown spot (Corynespora cassiicola). A hyperspectral imaging system was es tablished to acquire and pre-process leaf images, as well as to extract leaf spectral properties. Owing to the complexity of the original spectral data, stepwise discriminate and canonical discriminate were executed to reduce the numerous spectral information, in order to decrease the amount of calculation and improve the accuracy. By the stepwise discriminate we selected 12 optimal wavelengths from the original 55 wavelengths, and after the canonical discriminate, the 55 wavelengths were reduced to 2 canonical variables. Then the discriminate models were developed to classify the leaf samples. The result shows that the stepwise discriminate model achieved classification accuracies of 100% and 94% for the training and testing sets, respectively. For the canonical model, the classification accuracies for the training and testing sets were both 100%. These results indicated that it is feasible to identify and classify cucumber diseases using hyperspectral imaging technology and discriminate analysis. The preliminary study, which was done in a closed room with restrictions to avoid interference of the field environment, showed that there is a potential to establish an online field application in cucumber disease detection based on visible spectroscopy.