利用高光谱散射图像技术研究了苹果的粉质化无损检测.提出了一种无信息变量消除法和局部线性嵌入相结合的苹果粉质化分类的新方法.经无信息变量消除法筛选后的波段降为全谱的23.5%.将波段选择后的原始图像数据用局部线性嵌入降维作为偏最小二乘判别分析的输入变量并建模.无信息变量消除法与局部线性嵌入相结合算法和局部线性嵌入降维方法得到的粉质化分类测试准确度分别是79.0%和79.0%;无信息变量消除法与平均反射法相结合和平均反射法特征提取得到的是77.4%和75.8%.结果表明，无信息变量消除法与局部线性嵌入想结合的方法可以大大地降低高光谱散射图像的数据量，同时保证了分类准确度，为在线检测、分类和高光谱数据的存储提供了一种实时、有效的方法.

Hyperspectral scattering is a promising technique for noninvasive measurement of apple mealiness. An uninformative variable elimination (UVE) coupled with locally linear embedding (LLE) algorithm was proposed for assessing apple mealiness. After the algorithm, the number of effective wavelengths decreased to 23.5% of full wavelengths of hyperspectral scattering images. LLE was utilized to reduce the dimensionality of images composed of effective wavelengths. Partial least squares discriminant analysis was applied to develop classification model. Compared with mean reflectance (75.8%) and UVE coupled with mean reflectance algorithm (77.4%), LLE and UVE coupled with LLE model yielded better results (79.0%). UVE coupled with LLE model with the presevation of classification accuracy only used 23.5% wavelength of LLE model. Therefore, it provides a useful algorithm for online classification and data saving.