目的 为了实现基于高光谱成像以及误差反向传播（BP）网络模型的苹果硬度快速无损检测。方法 利用高光谱成像采集系统采集采后“富士”苹果的高光谱图像，然后提取整个苹果样本区域的平均反射光谱；利用连续投影算法（SPA）和竞争性自适应重加权算法（CARS）实现对标准正态变换预处理后光谱数据的降维；研究基于全光谱以及特征光谱的预测苹果硬度BP网络模型。结果 采用SPA和CARS分别从256个全光谱中提取了18个和16个特征波长，明显提升了预测模型的运行效率，且SPA+BP网络模型具有相对较好的苹果硬度预测能力（rp=0.728，RPm=0.282 kg/cm2）。结论 研究表明基于高光谱成像技术和BP网络建立的预测模型可快速无损预测苹果的硬度。

The work aims to realize rapid nondestructive detection of firmness of apples based on hyperspectral imaging technology and error back propagation (BP) network. The hyperspectral imaging acquisition system was used to acquire the hyperspectral images of postharvest 'Fuji' apples, and then the average reflectance spectra in the whole region of apple samples was extracted. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) method were used to conduct data mining of spectral data preprocessed by the standard normal variation. A BP network model for predicting firmness of apples based on full spectra and characteristic spectra was studied. The results showed that, 18 and 16 characteristic wavelengths were extracted by SPA and CARS from 256 full wavelengths, which obviously improved the working efficiency of prediction model. Moreover, SPA+BP network model had a relatively good prediction ability for firmness of apples (rp=0.728, RPm=0.282 kg/cm2). This study indicates that the prediction model based on hyperspectral imaging technology and BP network can be applied in the rapid nondestructive prediction of firmness of apples.