利用高光谱成像技术提取可同时检测苹果糖度与硬度的最佳波长。首先双面采集苹果的高光谱图像, 获取亮度相近感兴趣区域（ROIs）的反射波形, 采用二阶导数结合标准正态变量(SD+SNV)的方法平滑波形, 测试ROIs的糖度与硬度; 之后采用连续投影算法（SPA）提取两项指标的特征波长, 根据特征波长的分布提出二次连续投影算法, 结合波形集特征与两次投影结果确定不同取样面的最佳波长; 最后采用遗传算法开发神经网络（GA-BP）建立预测模型, 双面取样波长（543 nm和674 nm）效果最优, 糖度相关系数（R）为0.847 6, 均方误差（MSE）为3.32; 硬度R为0.793 8, MSE为9.6。结果表明, 相同波长信息可以检测苹果糖度与硬度。

Hyperspectral imaging technology was used to extract the optimal wavelength for apple brix and firmness test. Firstly, the hyperspectral images of apples were acquired from double-sided sampling. The reflection waveforms of the regions of interest (RIOs) with similar brightness were acquired and smoothed by the second derivation and standard normal variate (SD+SNV) method. The brix and firmness values of RIOs were also tested. Then, the characteristic wavelengths of two indicators were extracted by using the successive projections agorithm(SPA). According to the distribution of characteristics wavelengths, two times SPA was proposed. Combined the feature of waveforms and the results of two projections, the optimal wavelengths of different sampling facets were determined. Finally, the genetic algorithm for back propagation(GA-BP) was used to build the prediction model. The best results were obtained from the double-sided sampling wavelengths (543 nm and 674 nm). The correlation coefficient of brix (R) is 0.847 6 and the mean square error (MSE) is 3.32, and for the firmness, R is 0.793 8 and MSE is 9.6. The results show that the brix and firmness can be detected by the same wavelength information.