Optimized machine learning and artificial neural networks for NIRS-based prediction of mango internal quality

Abstract

Abstract A robust and updated approach for non-destructive prediction of key quality attributes in intact mangoes was developed by integrating near-infrared reflectance spectroscopy (NIRS) with advanced machine learning and artificial neural network (ANN) algorithms. In this study, NIR spectral data of 136 mango samples were collected and processed using standard normal variate (SNV) correction. Five regression methods namely partial least squares regression (PLSR), support vector machine regression (SVMR), random forest regression (RFR), extreme gradient boosting (XG-Boost), and generalized regression neural network (GRNN), were optimized and evaluated for predicting total acidity (TA) and ascorbic acid (AA). Results indicate that while traditional linear methods like PLSR achieved reasonable predictive power (RPD > 2.5), nonlinear models, especially XGBoost and GRNN, significantly outperformed PLSR, with GRNN models achieving the highest accuracy, with maximum performance reaching R 2 up to 0.98 and RPD > 5.5 across the evaluated parameters (TA and AA). The findings demonstrate that optimized machine learning and ANN models offer robust, accurate, and practical solutions for rapid, non-invasive mango quality assessment. This integrated methodology supports advanced quality control, sorting, and breeding programs, providing substantial benefits for industry and supply chain management through rapid, reliable assessment of fruit nutritional and chemical properties.