Determination of SSC in pears by establishing the multi-cultivar models based on visible-NIR spectroscopy

Soluble solids content (SSC) is one of the most important quality attributes affecting the price of fresh fruit. The individual-cultivar model is the most common SSC analysis model. However, this type of model is not the optimal for assessment of SSC in the different cultivars of fruit. In this study, the feasibility of using multi-cultivar model for quantitatively determining SSC in three cultivars of pears was observed based on visible-NIR spectroscopy. The multi-cultivar and individual-cultivar models were developed and different variable selection algorithms were used to optimize models. Results showed that the multi-cultivar model was superior to individual-cultivar models for SSC prediction of all samples and competitive adaptive reweighted sampling (CARS) did better than Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) for selection of effective variables. Based on the selected variables, CARS-PLS and CARS-MLR multi-cultivar models can achieve effective prediction for SSC of three cultivars of pears with similar detection accuracy. The coefficients of determination for prediction set (R-P(2)) and root mean square error of prediction (RMSEP) obtained by these two types of models are 0.90-0.92 and 0.23-0.30 for three cultivars of pears. The overall results demonstrated that it was feasible to accurately determine SSC of different cultivars of pears using the multi-cultivar model, CARS was a powerful tool to select the efficient variables, and CARS-PLS and CARS-MLR were simple and excellent for the spectral calibration.