基于SVD的苹果粉质化高光谱散射图像特征提取

粉质化是影响苹果等级的重要口感参数.采用高光谱散射图像进行了苹果粉质化的无损检测研究.利用奇异值分解方法对样本600～1 000 nm 共81个波长20 mm范围内的敝射图像进行奇异值分解,将获得的奇异值作为粉质化表征参数,结介偏微分最小二乘判别分析建立苹果粉质化分类模型.结果缸示,对不同产地和不同储藏条件下的样本,其两分类模型(粉质化和非粉质化)的分类精度为76.1%～80.6%,优于平均值特征提取方法(75.3%～76.5%).分析表明,奇异值分解可以有效地提取高光谱敞射图像的特征,用此特征建立粉质化分类模型可以区分粉质化和非粉质化的苹果,但分类精度有待于进一步提高.

Feature extraction of hyperspectral scattering image for apple mealiness based on singular value decomposition

Apple mealiness is an important sensory parameter for classification of apple quality. Hyperspectral scattering technique was investigated for noninvasive detection of apple mealiness. A singular value decomposition (SVD) method was proposed to extract the feature/ or singular values of the hyperspectral scattering images between 600 and 1000 nm for 20 mm distance including 81 wavelengths. As characteristic parameters of apple mealiness, singular values were applied to develop the classification model coupled with partial least squares discriminant analysis (PLSDA) using the samples from different origin and different storage conditions. The classification accuracies for the two-class ("mealy" and "non-mealy") model were between 76.1% and 80.6% better than mean method (75.3%-76.5%). The results indicated that SVD method was potentially useful for the feature extraction of hyperspectral scattering images and the model developed with these features can detect the mealy and non-mealy apple, but the classification accuracies need to be improved.