Multispectral AI Detects Artificial Fruit Ripening and Estimates Shelf-Life

The illicit use of industrial-grade Calcium Carbide (CaC2) to ripen climacteric fruits poses significant health risks due to toxic residues. To combat this, researchers have developed a novel, non-invasive multispectral framework. This system is designed to differentiate between safely ripened fruits (naturally or ethephon-induced) and those treated with CaC2, while also providing quantitative estimates of ripening progression and remaining shelf life for fruits such as mango and banana. The framework analyzes spectral profiles of fruits across 18 discrete wavelengths in the visible-near infrared (NIR) range (410 nm - 940 nm) using a specialized spectral triad sensor. CaC2-treated samples exhibit distinct spectral intensity drops in the visible region, indicative of accelerated chlorophyll degradation and carotenoid development. A sophisticated feature engineering strategy integrates inter-method spectral variance, intensity ratios, and environmental parameters. After dimensionality reduction with PCA, these features are fed into three independent XGBoost models. The system achieved 95% classification accuracy for mangoes (0.67 carbide recall) and 81% accuracy for bananas (0.74 carbide recall), demonstrating its effectiveness in both classification and quantitative estimation.