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Application of LS-SVM and Variable Selection Methods on Predicting SSC of Nanfeng Mandarin Fruit
Abstract : The objective of this research was to investigate the performance of
LS-SVM combined with several variable selection methods to assess
soluble solids content (SSC) of Nanfeng mandarin fruit. Visible/near
infrared (Vis/NIR) diffuse reflectance spectra of samples were acquired
by a QualitySpec spectrometer in the wavelength range of 350~1800 nm.
Four variable selection methods were conducted to select informative
variables for SSC, and least squares-support vector machine (LS-SVM)
with radial basis function (RBF) kernel was used develop calibration
models. The results indicate that four variable selection methods are
useful and effective to select informative variables, and the results of
LS-SVM with these variable selection methods are comparable to the
results of full-spectrum partial least squares (PLS). Genetic algorithm
(GA) combined with successive projections algorithm (SPA) is the best
variable selection method among these four methods. The correlation
coefficients and RMSEs in LS-SVM with GA-SPA model for calibration,
validation and prediction sets are 0.935, 0.560%, 0.912, 0.631% and
0.933, 0.594%, respectively.
Cited literature [27 references]
https://hal.inria.fr/hal-01220926
Contributor : Hal Ifip <>
Submitted on : Tuesday, October 27, 2015 - 10:11:05 AM
Last modification on : Wednesday, January 17, 2018 - 10:43:34 AM
Long-term archiving on: : Thursday, January 28, 2016 - 10:35:21 AM
Contributor : Hal Ifip <>
Submitted on : Tuesday, October 27, 2015 - 10:11:05 AM
Last modification on : Wednesday, January 17, 2018 - 10:43:34 AM
Long-term archiving on: : Thursday, January 28, 2016 - 10:35:21 AM
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