苹果粉质化程度是衡量其内部品质的一个重要因素, 采用了高光谱散射图像技术进行苹果粉质化的无损检测。 针对高光谱散射图像数据量大的特点, 提出了局部线性嵌入(local linear embedded, LLE)和支持向量机(support vector machine, SVM)相结合的用于检测苹果粉质化的新分类方法。 LLE是一种通过局部线性关系的联合来揭示全局非线性结构的非线性降维方法, 能有效计算高维输入数据在低维空间的嵌入流形。 对降维后的高光谱数据采用SVM进行分类。 将LLE-SVM分类方法与传统的SVM分类方法比较, 仿真结果表明, 对高光谱数据而言, 用LLE-SVM得到的训练精度高于单纯使用SVM的训练精度； 降维前后, 分类器的测试精度变化不大, 波动范围不超过5％。 LLE-SVM为高光谱散射图像技术进行苹果粉质化无损检测提供了一个有效的分类方法。

Apple mealiness degree is an important factor for its internal quality. hyperspectral scattering, as a promising technique, was investigated for noninvasive measurement of apple mealiness. In the present paper, a locally linear embedding (LLE) coupled with support vector machine (SVM) was proposed to achieve classification because of large number of image data. LLE is a nonlinear lowering dimension method, which reveals the structure of the global nonlinearity by the local linear joint. This method can effectively calculate high-dimensional input data embedded in a low-dimensional space manifold. The dimension reduction of hyperspectral data was classified by SVM. Comparing the LLE-SVM classification method with the traditional SVM classification, the results indicated that the training accuracy obtained with the LLE-SVM was higher than that just with SVM; and the testing accuracy of the classifier changed a little before and after dimensionality reduction, and the range of fluctuation was less than 5%. It is expected that LLE-SVM method would provide an effective classification method for apple mealiness nondestructive detection using hyperspectral scattering image technique.