title: [Study on the early detection of Sclerotinia of Brassica napus based on combinational-stimulated bands].
abstract: The combinational-stimulated bands were used to develop linear and nonlinear calibrations for the early detection of sclerotinia of oilseed rape (Brassica napus L.). Eighty healthy and 100 Sclerotinia leaf samples were scanned, and different preprocessing methods combined with successive projections algorithm (SPA) were applied to develop partial least squares (PLS) discriminant models, multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) models. The results indicated that the optimal full-spectrum PLS model was achieved by direct orthogonal signal correction (DOSC), then De-trending and Raw spectra with correct recognition ratio of 100%, 95.7% and 95.7%, respectively. When using combinational-stimulated bands, the optimal linear models were SPA-MLR (DOSC) and SPA-PLS (DOSC) with correct recognition ratio of 100%. All SPA-LSSVM models using DOSC, De-trending and Raw spectra achieved perfect results with recognition of 100%. The overall results demonstrated that it was feasible to use combinational-stimulated bands for the early detection of Sclerotinia of oilseed rape, and DOSC-SPA was a powerful way for informative wavelength selection. This method supplied a new approach to the early detection and portable monitoring instrument of sclerotinia.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Liu
authors.forename: Lei
authors.initials: L
authors.lastname: Feng
authors.forename: Bing-gan
authors.initials: BG
authors.lastname: Lou
authors.forename: Guang-ming
authors.initials: GM
authors.lastname: Sun
authors.forename: Lian-ping
authors.initials: LP
authors.lastname: Wang
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D000465:Algorithms; D001203:Ascomycota; D029688:Brassica napus / Q000382:microbiology*; D002138:Calibration; D016018:Least-Squares Analysis; D016014:Linear Models; D008962:Models, Theoretical; D010935:Plant Diseases / Q000382:microbiology*; D018515:Plant Leaves / Q000382:microbiology; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine