title: [Huanghua pear soluble solids contents Vis/NIR spectroscopy by analysis of variables optimization and FICA].
abstract: The purpose of this study was to establish a mathematical model of the visible/near-infrared (Vis/NIR) diffuse transmission spectroscopy with fine stability and precise predictability for the non destructive testing of the soluble solids content of huanghua pear, through comparing the effects of various pretreatment methods, variable optimization method, fast independent principal component analysis (FICA) and least squares support vector machines (LS-SVM) on mathematica model for SSC of huanghua pear, and the best combination of methods to establish model for SSC of huanghua pear was got. Vis/NIR diffuse transmission spectra of huanghua pear were acquired by a Quality Spec spectrometer, three methods including genetic algorithm, successive projections algorithm and competitive adaptive reweighted sampling (CARS) were used firstly to select characteristic variables from spectral data of huanghua pears in the wavelength range of 550~950 nm, and then FICA was used to extract factors from the characteristic variables, finally, validation model for SSC in huanghua pears was built by LS-SVM on the basic of those parameters got above. The results showed that using LS-SVM on the foundation of the 21 variables screened by CARS and the 12 factors selected by FICA, the CARS-FICA-LS-SVM regression model for SSC in huanghua pears was built and performed best, the coefficient of determination and root mean square error of calibration and prediction sets were RC(2)=0.974, RMSEC=0.116%, RP(2)=0.918, and RMSEP=0.158% respectively, and compared with the mathematical model which uses PLS as modeling method, the number of variables was down from 401 to 21, the factors were also down from 14 to 12, the coefficient of determination of modeling and prediction sets were up to 0.023 and 0.019 respectively, while the root mean square errors of calibration and prediction sets were reduced by 0.042% and 0.010% respectively. These experimental results showed that using CARS-FICA-LS-SVM to build regression model for the forecast of SSC in huanghua pears can simplify the prediction model and improve the detection precision.
authors.forename: Wen-li
authors.initials: WL
authors.lastname: Xu
authors.forename: Tong
authors.initials: T
authors.lastname: Sun
authors.forename: Tian
authors.initials: T
authors.lastname: Hu
authors.forename: Tao
authors.initials: T
authors.lastname: Hu
authors.forename: Mu-hua
authors.initials: MH
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D002138:Calibration; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D025341:Principal Component Analysis; D031989:Pyrus / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine