用多光谱技术无损检测催熟水果是否用了工业电石，还能估算成熟度和保质期。
A Multispectral Framework for the Detection of Calcium Carbide-Induced Ripening and Shelf-Life Estimation in Climacteric Fruits
- 通过可见光到近红外波段的光谱分析，识别电石催熟导致的叶绿素快速降解特征。
- 对芒果分类准确率达95%，香蕉达81%，能有效识别电石催熟样本（召回率超67%）。
- 适合食品安全监管、水果质检人员使用，可快速无损筛查高风险催熟水果。
工业级电石（CaC2）非法用于芒果和香蕉等跃变型水果催熟，会残留砷和磷，带来严重健康风险。本文提出一种非侵入式多光谱框架，用于区分自然催熟、乙烯利催熟与电石催熟的果实，并估算其成熟进度（百分比）和剩余货架期（天数）。利用AS7265x光谱三联传感器，在410 nm–940 nm范围内采集芒果（Mangifera indica）和香蕉（Musa acuminata）在18个离散波长处的光谱数据。电石处理样本在可见光区表现出更陡峭的光强下降，反映叶绿素加速降解和类胡萝卜素发育。通过整合多方法光谱方差、特定波长强度比及温湿度环境参数进行特征工程，经主成分分析（PCA）保留>90%光谱方差于前5-7个主成分。基于此特征集，训练三个独立的基于梯度提升（XGBoost）的学习模型，分别完成催熟方式分类、剩余货架期与成熟度定量估计。芒果分类准确率为95%，电石类召回率为0.67；香蕉分类准确率为81%，电石类召回率为0.74。该仪器与数据驱动方法证明了所提框架的有效性。
原文摘要 · Abstract (English)
Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of arsenic and phosphorus. To address this, the proposed study explores a novel, non-invasive multispectral framework for distinguishing safely ripened fruits (naturally ripened and ethephon-induced) from calcium carbide-ripened samples, while also estimating their ripening progression (in percentage) and remaining shelf life (in days). The spectral profiles of mango (Mangifera indica) and banana (Musa acuminata) at 18 discrete wavelengths in the visible-near infrared (NIR) range (410 nm - 940 nm) are studied using the AS7265x spectral triad sensor. CaC2-treated samples exhibit sharper spectral intensity drops in the visible region, consistent with accelerated chlorophyll degradation and carotenoid development. To characterize these physiological changes, the feature engineering strategy integrates inter-method spectral variance, intensity ratios at distinct wavelengths, and environmental parameters including temperature and humidity. Dimensionality reduction using Principal Component Analysis (PCA) retains >90% of spectral variance within the first 5-7 components. The resulting feature set is used to train three independent eXtreme Gradient Boosting (XGBoost)-based learning algorithms for ripening method classification, along with quantitative estimation of remaining shelf life and ripening progression. A classification accuracy of 95% along with carbide class recall of 0.67 is observed for mango samples, while the model achieves an accuracy of 81% and carbide class recall of 0.74 for banana. This instrumentation and data-driven approach demonstrates the effectiveness of the proposed non-invasive framework.
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