Optimization of soluble solids content prediction models in 'Hami' melons by means of Vis-NIR spectroscopy and chemometric tools

Nondestructive assessment of internal quality in fruit with thick rind and large size was always a challenge in terms of the fruit quality automatic grading application. This study compared different spectral measurement positions and prediction models for determining soluble solids content (SSC) of 'Hami' melon. Visible and short wavelength near-infrared with the spectral range of 550-950 run was used and three local positions (calyx, equator and stem regions) in each sample were selected for spectral collection, respectively. The partial least squares (PLS) was employed to establish three local calibration models (calyx-model, equator-model and stemmodel) and calyx-model was proved to be optimal one for SSC prediction of 'Hami' melon. In order to optimize the calyx-model, four algorithms including successive projections algorithm (SPA), Monte Carlo-uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS) and combination algorithm MC-UVE-SPA were applied to select effective variables from full spectra, respectively. And, the non-linear least square support vector machine (LSSVM), linear multiple linear regression (MLR) and PLS models were also developed based on those selected effective variables, respectively. The results indicated that MC-UVE-SPA was the optimal method for selecting the effective variable. Based on 18 variables selected by MC-UVE-SPA, all established models including MC-UVE-SPA-PLS, MC-UVE-SPA-LSSVM and MC-UVE-SPA-MLR can successfully predict the SSC of all 'Hami' melon fruit samples with the prediction accuracy of RMSEP = 0.95-0.99 degrees Brix.