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Abstract：
A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e-nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e-nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion-based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion-based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R 2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e-nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.
摘要：
本研究开发了一种基于高光谱成像（HSI）和电子鼻（e-nose）系统的数据融合策略，以检查海沃德猕猴桃的采后成熟过程。从电子鼻和HSI技术中提取的特征，在单一或组合模式下，用于开发机器学习算法。性能评估证明，嗅觉和反射率数据的融合提高了判别和预测算法的性能。因此，在校准和测试阶段具有100%和94.44%的高分类精度，基于数据融合的支持向量机（SVM）优于偏最小二乘判别分析（PLSDA），可以根据存储时间将猕猴桃样品区分为八类。此外，基于数据融合的支持向量回归(SVR)比偏最小二乘回归(PLSR)对猕猴桃硬度的预测效果更好,可溶性固形物含量（SSC），和可滴定酸度（TA）措施。SVR算法对测试数据的预测R2和RMSE标准分别为0.962和0.408，对于SSC，为0.964和0.337，TA为0.955和0.039，分别。结论是，电子鼻和HSI系统的混合以及SVM算法为猕猴桃在储存过程中的准确和无损监测质量提供了有效的工具。
