ABSTRACT In this study, visible/near-infrared (Vis/NIR) hyperspectral imaging was used for the nondestructive detection of storage time of strawberries. Storage time was calculated immediately after freshly picking. Support vector machine (SVM) with multiplicative scatter correction can differentiate strawberries of different storage time with an accuracy of 100%. Then, the model developed by partial least square regression with full-range spectra was used to predict the storage time of strawberries with a determination coefficient of prediction (Rp 2 ) of 0.9999 and root-mean-square error of prediction (RMSEP) of 0.0721, and deviation was small at different periods. With the spectra of 10 important wavelengths obtained by uninformative variable elimination, the SVM model obtained relatively acceptable results with Rp 2 of 0.9943 and RMSEP of 1.3213. The prediction experiments for the separately picked strawberry samples also got the similar results. Finally, distribution maps of storage time generated based on the pixel-wise spectra and established model clearly show the quality transformation of the strawberries.

中文翻译：

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利用可见/近红外高光谱成像无损检测草莓的贮藏时间

摘要 本研究采用可见光/近红外（Vis/NIR）高光谱成像技术对草莓的贮藏时间进行无损检测。新鲜采摘后立即计算储存时间。具有乘法散射校正的支持向量机 (SVM) 可以以 100% 的准确率区分不同储存时间的草莓。然后，利用全光谱偏最小二乘回归建立的模型预测草莓的贮藏时间，预测决定系数（Rp 2 ）为0.9999，预测均方根误差（RMSEP）为0.0721 ，且不同时期偏差较小。通过无信息变量消除获得的10个重要波长的光谱，SVM模型获得了相对可以接受的结果，Rp 2为0.9943，RMSEP为1。3213. 单独采摘的草莓样本的预测实验也得到了类似的结果。最后，基于逐像素光谱和建立的模型生成的存储时间分布图清楚地显示了草莓的质量转变。