Detection and classification of spongy tissue disorder in mango fruit during ripening by using visible-near infrared spectroscopy and multivariate analysis.

This article focuses on the use of visible-near infrared (Vis-NIR) spectroscopy combined with Soft Independent Modelling of Class Analogy (SIMCA) for the non-destructive detection and classification of spongy tissue disorder in Alphonso mangoes, a prized Indian variety. The study identified specific wavelength ranges (670–750 nm and 900–970 nm) that effectively differentiate healthy mangoes from those affected by spongy tissue, achieving classification accuracies up to 96.7%. By analyzing spectral reflectance data and employing principal component analysis (PCA), the research demonstrates that Vis-NIR spectroscopy can detect internal fruit defects during ripening without damaging the fruit, addressing challenges in export quality control. The findings suggest that this non-invasive method could improve postharvest management and reduce economic losses, though further validation across different mango cultivars and internal disorders is recommended to enhance model robustness.