Gao Jun-Feng, Zhang Hai-Liang, Kong Wen-Wen, He Yong
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 Jul;33(7):1922-6.
The potential of hyperspectral imaging technology was evaluated for discriminating three types of waxed apples. Three types of apples smeared with fruit wax, with industrial wax, and not waxed respectively were imaged by a hyperspectral imaging system with a spectral range of 308-1 024 nm. ENVI software processing platform was used for extracting hyperspectral image object of diffuse reflection spectral response characteristics. Eighty four of 126 apple samples were selected randomly as calibration set and the rest were prediction set. After different preprocess, the related mathematical models were established by using the partial least squares (PLS), the least squares support vector machine (LS-SVM) and BP neural network methods and so on. The results showed that the model of MSC-SPA-LSSVM was the best to discriminate three kinds of waxed apples with 100%, 100% and 92.86% correct prediction respectively.
评估了高光谱成像技术鉴别三种类型打蜡苹果的潜力。分别用水果蜡、工业蜡涂抹以及未打蜡的三种类型苹果，通过光谱范围为308 - 1024 nm的高光谱成像系统进行成像。利用ENVI软件处理平台提取具有漫反射光谱响应特征的高光谱图像对象。从126个苹果样本中随机选取84个作为校正集，其余作为预测集。经过不同预处理后，采用偏最小二乘法（PLS）、最小二乘支持向量机（LS - SVM）和BP神经网络等方法建立相关数学模型。结果表明，MSC - SPA - LSSVM模型鉴别三种打蜡苹果的效果最佳，预测正确率分别为100%、100%和92.86%。
