中文摘要：

应用高光谱成像技术结合连续投影算法（SPA）实现葡萄果皮中花色苷含量的快速无损检测.采集60组样本高光谱图像,获取样本光谱曲线,并采用多元散射校正预处理方法提高信噪比.然后采用SPA选择光谱变量,将其作为多元线性回归（MLR）、偏最小二乘（PLS）模型和BP神经网络（BPNN）的输入变量,分别建立SPA-MLR、SPA-PLS和SPA-BPNN模型并与全光谱变量PLS模型相比较.结果表明,SPA-MLR、SPA-BPNN和SPA-PLS模型的预测精度均优于全光谱变量PLS模型,其中SPA-PLS模型获得了最佳预测结果,其预测相关系数Rp和预测均方根误差（RMSEP）分别为0.900 0和0.550 6.结果表明,利用近红外高光谱成像技术能够有效检测酿酒葡萄果皮中花色苷含量.

英文摘要：

This work aimed to determine the anthocyanin content in grape skins based on hyperspectral imaging technology in combination with successive projections algorithm （SPA）.Cabernet Sauvignon （Vitis vinifera L.） grape berries from Shaanxi province were used as experimental materials.Hyperspectral images of 60 groups of grape samples were collected by near infrared hyperspectral camera and the anthocyanin contents in these samples were detected.Multiplicative scatter correction was used to improve the signal-to-noise ratio （SNR）.Moreover,SPA was applied for the extraction of effective wavelengths （EWs）,which showed least collinearity and redudancies in the spectral data.The selected effective wavelengths were used as the inputs of multiple linear regression （MLR）,partial least squares （PLS） and BP neural network （BPNN）.Then SPA-MLR,SPA-PLS and SPA-BPNN models were developed and compared with full-spectrum-PLS model.It was shown that SPA-MLR,SPA-PLS and SPA-BPNN models were better than full-spectrum-PLS model.The best performance was achieved by SPA-PLS model with Rp of 0.900 0 and RMSEP of 0.550 6.These results indicate that anthocyanin contents in grape skins could be measured effectively by using near infrared hyperspectral imaging.