Non-destructive Prediction of Quality Parameters in Alphonso Mangoes Using Near-infrared Spectroscopy: A Comprehensive Study on Physicochemical Characteristics and Ripening Dynamics

This study focuses on the application of Near-Infrared Spectroscopy (NIRS) to predict key quality parameters in Alphonso mangoes, namely Total Soluble Solids (TSS), Titratable Acidity (TA), pulp firmness, and carotenoid content. Alphonso mangoes, known as the "king of fruits," are a significant tropical fruit, with India being the leading global producer. Quality control is crucial for fruit export, and the mango supply chain involves various procedures. Physiological transformations occur in mangoes from production to consumption, affecting their internal quality. The challenges in assessing internal quality factors such as soluble solids, dry matter, firmness, starch, and sugar content hinder the exportation of mangoes.

The ripening process in mangoes involves metabolic changes leading to modifications in physical, mechanical, and chemical attributes. Non-destructive identification of internal defects in Alphonso mangoes is an underexplored field, with limited research on internal quality factors. The study aims to enhance understanding of light penetration into fruit tissues using numerical simulation techniques. NIR spectroscopy's significance in post-harvest technology is highlighted, and the study develops prediction models for quality parameters using Partial Least Squares (PLS) regression. The calibration models are optimized with spectral pre-processing techniques, and the Savitzky–Golay first derivative method yields favorable results. The results indicate changes in TSS, carotenoid content, firmness, and acidity during storage, providing insights into mango ripening dynamics. The Unscrambler software is employed for multivariate analysis, emphasizing the importance of the 600–1000 nm wavelength range. The study contributes to the development of portable instruments for rapid and accurate quality prediction of Alphonso mangoes in the supply chain.