title: [Study on the detection of gray mold of tomato leave based on Vis-near infrared spectra].
abstract: Visible and near-infrared reflectance spectroscopy (Vis/NIRS) technique was applied to the detection of disease level of grey mold on tomato leave. Chemometrics was used to build the relationship between the reflectance spectra and disease level. In order to decrease the amount of calculation and improve the accuracy of the model, principal component analysis (PCA) was executed to reduce numerous wavebands into several principal components (PCs) as input variables of BP neural network. The loading value of PC1 was applied to qualitatively analyze which wavebands were more important for disease detection. Prediction results showed that when the number of primary PCs was 8 and the hidden nodes of BP neural network were 11, the detection performance of the model was good as correlation coefficient (r) was 0.930 while standard error of prediction (SEP) was 0.068 7. Thus, it is concluded that spectroscopy technology is an available technique for the detection of disease level of grey mold on tomato leave based on chemometrics used for data analysis.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
authors.forename: Wu
authors.initials: W
authors.lastname: Di
authors.forename: Lei
authors.initials: L
authors.lastname: Feng
authors.forename: Chuan-Qing
authors.initials: CQ
authors.lastname: Zhang
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D005658:Fungi / Q000502:physiology; D018551:Solanum lycopersicum / Q000737:chemistry* / Q000382:microbiology; D010935:Plant Diseases / Q000382:microbiology*; D018515:Plant Leaves / Q000737:chemistry* / Q000382:microbiology; D019265:Spectroscopy, Near-Infrared / Q000379:methods*