目的 可溶性固形物含量是评价苹果品质的重要指标，为开发苹果品质快速检测设备提供理论基础。方法 采用高光谱图像采集系统采集“富士”苹果的高光谱图像，并获取感兴趣区域的反射光谱；应用连续投影算法对标准正态变换预处理后的光谱进行降维；基于选取的特征光谱建立预测苹果可溶性固形物含量的多元线性回归模型。结果 采用连续投影算法从256个全光谱中提取了12个波长作为特征光谱，明显提升了多元线性回归预测模型的运行效率；基于特征光谱建立的多元线性回归预测模型具有较好的校正性能（RC=0.804，RCm=0.665%）和预测性能（RP=0.859，RPm=0.413%）。结论 研究建立的苹果可溶性固形物含量预测模型性能较稳定，可以满足实际应用需求。

Soluble solids content is an important index to evaluate apple quality. The works aims to provide theoreti-cal basis for the development of detection equipment for rapidly predicting apple quality. The hyperspectral image acquisition system was used to collect hyperspectral images of "Fuji" apples and obtain the reflectance spectra in the regions of interest. The successive projection algorithm was used for the dimensionality reduction of the reflectance spectra subject to standard normal variation preprocessing. The multi linear regression model was established based on selected characteristic wavelengths to predict soluble solids content of apples. The results showed that 12 wavelengths as characteristic spectra were extracted by successive projection algorithm from 256 full spectra, and the working efficiency of multi linear regression prediction model was obviously improved. The multi linear regression model based on characteristic spectra had better calibration ability (RC=0.804, RCm=0.665%) and prediction ability (RP=0.859, RPm=0.413%). The prediction model established in this study for detection of soluble solids content of apples has stable properties and can meet the requirements of practical application.