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                <title xml:lang="en">Application of LS-SVM and Variable Selection Methods on Predicting SSC of Nanfeng Mandarin Fruit</title>
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                <editor>Daoliang Li</editor>
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                <term xml:lang="en">Nanfeng mandarin fruit</term>
                <term xml:lang="en">soluble solids content</term>
                <term xml:lang="en">variable selection</term>
                <term xml:lang="en">LS-SVM</term>
                <term xml:lang="en">Vis/NIR</term>
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              <p>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.</p>
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