Online accurate detection of soluble solids content in navel orange assisted by automatic orientation correction device

Abstract Non-destructive online sorting system for fruit quality are gaining importance in the global agro-industry as they allow for optimising harvesting, storage and marketing management decisions. The most commonly used sensing technology is visible-near infrared (Vis/NIR) spectroscopy. However, in online detection, the reproducibility of spectra is easily affected by fruit placement attitude. Up to now, little or no work considered the problem of attitude correction before spectral acquisition. This work aims at filling this gap. In this study, Vis/NIR diffuse transmission technology was used to detect the soluble solids content (SSC) in navel oranges, the analysis models of SSC in navel orange with consistent-orientation (T1: navel upward, T2: navel forward, and T3: navel leaning with an angle about 30°-60°), mixed-orientation (mix of T1, T2 and T3) and corrected-orientation (T2 correction) were established. Partial least square regression (PLSR) combined with different spectral pretreatment and effective wavelengths (EWs) selection methods were used to establish the optimal SSC analytical models. Root mean square error of cross-validation (RMSECV), correlation coefficient of calibration (RC), root mean square error of prediction (RMSEP), correlation coefficient of prediction (RP), residual predictive deviation (RPD) and the ratio of error range (RER) are used as the evaluation indicators of the model. The results show that the inconsistency of the placement attitude affects the prediction accuracy. The mixed-orientation model contains inconsistent placement information of the fruit orientation, and the robustness of the model is improved, but the prediction accuracy decreased. In the consistent-orientation model, the best analytical results can be obtained with the T2 placement orientation. The automatic orientation correction device based on image processing technology can correct the attitude of randomly placed navel orange samples according to the T2 orientation. The analytical accuracy of the model is further improved. In the corrected-orientation model, the best SSC analytical model can be obtained using NWS-MSC (Norris Williams smoothing - multiplicative scattering correction) pretreatment and VCPA (variable combination population analysis) variable selection methods, and RP, RMSEP, RPD and RER of the optimal prediction model were 0.981, 0.313 °Brix, 5.176 and 16.613 respectively. This method can be extended to the online analysis of other spherical fruits.