【目的】运用高光谱成像技术检测成熟期酿酒葡萄果皮的花色苷含量。【方法】利用900~1 700 nm近红外高光谱成像和多元回归模型对多品种酿酒葡萄成熟期不同阶段果皮花色苷含量进行预测建模。采集成熟期4~5个阶段的6个品种共75组酿酒葡萄样本的高光谱图像，运用不同预处理方法对光谱数据进行处理。基于主成分分析(PCA)和连续投影法(SPA)降维，将化学方法测量结果作为花色苷含量的参考值，采用支持向量回归(SVR)建立花色苷含量预测模型。【结果】SPA-SVR模型性能优于其他模型，其预测决定系数(Rp2)为0.869 1，均方根误差(RMSEp)为0.135 9。【结论】将近红外高光谱成像技术应用于多品种成熟期酿酒葡萄果皮的花色苷含量的快速无损检测具有良好的可行性。
【Objective】 To detect the anthocyanin contents of winegrape skins during ripening stages using hyperspectral imaging technology.【Method】 The 900-1 700 nm near-infrared hyperspectral imaging technology and multiple regression methods were used to build prediction models for anthocyanin contents in skins of different winegrape varieties during ripening stage. Totally 75 groups of grape samples belonging to 6 varieties were collected at 4-5 phases of mature stage, and their hyperspectral images were scanned. The spectrum data were enhanced by different preprocessing methods. Dimensionality reduction was then performed by principal component analysis (PCA) and successive projections algorithm (SPA). The anthocyanin contents measured by chemical method were used as reference values, and the prediction models of anthocyanin contents were built using support vector regression (SVR) method.【Result】 The SPA-SVR model had the best performance of prediction with the determination coefficient (Rp2) being 0.869 1 and the root mean square error of prediction (RMSEp) being 0.135 9.【Conclusion】 It is feasible to use the hyperspectral imaging technology to realize non-destructive and fast detection of the anthocyanin contents in winegrape skins of different varieties during ripening.