Abstract

Abstract The feasibility of using visible and near infrared full transmittance hyperspectral imaging for predicting soluble solids content (SSC) in oranges has been assessed. A combination of competitive adaptive reweighted sampling and successive projections algorithm (CARS-SPA) was used to select the effective wavelengths. Size of fruit was used as a compensation factor to establish a calibration model coupled with spectral information. Full transmittance spectra and physiochemical parameters (SSC and size) of samples were extracted. The potential outliers in samples were eliminated by Monte-Carlo outlier detection method. Effective wavelengths were selected by CARS algorithm and the newly proposed CARS-SPA combination method. Three types of models including partial least squares (PLS), multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) were established for SSC analysis of fruit based on different inputs. Results indicated that all models can realize the satisfactory prediction of SSC in oranges. Ranges of coefficient of determination ( R p r e 2 ) and root mean square error of prediction (RMSEP) were 0.88-0.89 and 0.48-0.48 % for PLS models, 0.83-0.85 and 0.49-0.55 % for MLR models, 0.86-0.90 and 0.40-0.48 % for LS-SVM. Compared among all SSC analysis models, CARS-SPA was a powerful effective wavelength selection combination and CARS-SPA-LS-SVM model with size had the optimal prediction accuracy ( R p r e 2 = 0.90, RMSEP = 0.40, RPD = 3.18). Overall, the results revealed that full transmittance hyperspectral imaging can be used to non-invasively to rapidly measure the SSC of oranges. A robust and accurate model could be established based on CARS-SPA-LS-SVM method with size compensation. These results may provide a useful reference for assessment of other internal quality attributes, such as acidity, of the thick-skinned fruit.

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