用于检测电石催熟及估算呼吸跃变型果实货架期的多光谱框架

中文摘要 AI 辅助

非法使用工业级电石（CaC₂）催熟芒果、香蕉等呼吸跃变型果实的行为普遍存在，会留下砷和磷的痕量残留，带来重大健康风险。为解决该问题，本研究提出一种新型非侵入式多光谱框架，用于区分安全催熟果实（自然催熟和乙烯利催熟）与电石催熟样本，同时估算催熟进度（以百分比计）和剩余货架期（以天计）。使用AS7265x光谱三合传感器，研究芒果（Mangifera indica）和香蕉（Musa acuminata）在可见光-近红外（NIR）范围（410 nm - 940 nm）内18个离散波长的光谱轮廓。经CaC₂处理的样本在可见光区域表现出更剧烈的光谱强度下降，这与加速的叶绿素降解和类胡萝卜素形成一致。为表征这些生理变化，特征工程策略整合了方法间光谱方差、不同波长处的强度比值，以及温度和湿度等环境参数。使用主成分分析（PCA）进行降维，前5-7个主成分保留了>90%的光谱方差。所得特征集用于训练三个独立的基于极端梯度提升（XGBoost）的学习算法，以完成催熟方法分类，以及剩余货架期和催熟进度的定量估算。对于芒果样本，分类准确率为95%，电石类召回率为0.67；对于香蕉，模型准确率为81%，电石类召回率为0.74。这种基于仪器和数据驱动的方法证明了所提出的非侵入式框架的有效性。

英文摘要

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.

发表机构

• National Institute of Technology Delhi（德里国家理工学院）
• ICAR-Indian Agricultural Research Institute（印度农业研究委员会-印度农业研究院）

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