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

Abstract

The esteemed Alphonso mango, cherished in India for its taste, saffron color, texture, and extended shelf life, holds global commercial appeal. Unfortunately, the prevalent spongy tissue disorder in Alphonso mangoes results in a soft and corky texture, with up to 30% of mangoes within a single batch affected. This issue leads to outright rejection during export due to delayed disorder identification. The current assessment method involves destructive sampling, causing substantial fruit loss, and lacks assurance for overall batch quality. In light of the mentioned challenges, this current study focuses on utilizing visible-near infrared (Vis-NIR) spectroscopy as a non-invasive method to assess the internal quality of mangoes. It also enables innovative classification models for automated binary categorization (healthy vs spongy tissue-affected). Through preprocessing and principal component analysis of spectral reflectance data, wavelength ranges of 670–750 nm, 900–970 nm, and 1100–1170 nm were identified for distinguishing healthy and damaged mangoes. Soft independent modelling of class analogy (SIMCA) modelling is a novel approach that can be used to classify mango into healthy and spongy tissue-affected categories for better postharvest management. The accuracy of SIMCA models for classifying mangoes into healthy and spongy tissue-affected classes was highest in the wavelength regions of 670–750 nm and 900–970 nm, being 94.4% and 96.7%, respectively. The spectral reflectance between wavelength region 650–970 nm gave significant and visible differentiation between all stages of spongy tissue, that is, mild, medium, and severe. Furthermore, the application of Vis-NIR spectroscopy alongside SIMCA modelling offers a viable avenue for examining internal abnormalities resulting from diseases or injuries in fruits, broadening its utility for diverse inspection needs.