Sweetness and Antioxidant Indices of Mango (Mangifera indica cv. Nam Dok Mai Sithong) using Red–Green–Blue and Near-Infrared Images
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Abstract
This study aimed to develop a simple method for the non-destructive evaluation of Nam Dok Mai Sithong mango quality indices. One hundred and fifty mangoes (Mangifera indica cv. Nam Dok Mai Sithong) were stored at room temperature for 10 days to evaluate image-based indices for non-destructive quality prediction. Physicochemical properties and image data were collected every two days. Red–green–blue (RGB) and near-infrared images were acquired using visible and infrared filters, respectively. RGB values were extracted using MATLAB. The Nam Dok Mai Sithong index (NDMSTI) was computed using the Normalized Difference Vegetation Index principle. Its predictive performance was compared with that of other RGB-based indices for mango chemical properties. Correlation analysis and partial least squares regression were applied. Model performance was validated using leave-one-out cross-validation. The models were evaluated using the coefficient of determination (R2CV) and root mean square error of cross-validation (RMSECV). Both the Normalized Green Blue Different Index (NGBDI) and NDMSTI showed statistically significant predictive performance for all chemical properties (p < 0.01). Performance was stronger for sweetness (total soluble solids) and antioxidants (ABTS). NDMSTI performed comparably to NGBDI, with slightly lower R2CV and slightly higher RMSECV. These results indicate that NDMSTI has potential as a non-destructive tool for monitoring mango quality during storage.
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