期刊论文

Shan, Y.

中文

农业工程学报

1002-6819

2011

27

8

350-354

10.3969/j.issn.1002-6819.2011.08.061

“十一五”国家科技支撑计划项目（2009BADB9B07）；

本校为第一机构

为了实现蜂蜜产地的快速判别,应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析.kennard-Stone法划分训练集和预测集.光谱用一阶导数加自归一化预处理后,再用小波变换(WT)进行压缩和滤噪.结合滤波后光谱信息,分别用径向基神经网络(RBFNN)和偏最小二乘—线性判别分析(PLS-LDA)建立了苹果蜜产地和油菜蜜产地的判别模型.对不同小波基和分解尺度进行了讨论.对苹果蜜,WT-RBFNN模型和WT-PLS-LDA模型都是小波基为db1、分解尺度为2时的预测精度较好,都为96.2％.对油菜蜜:WT-RBFNN模型在小波基为db4和分解尺度为1时,预测精度较好,为85.7％;WT-PLS-LDA模型在小波基为db9、分解尺度也为1时,预测精...

Near infrared spectroscopy combined with chemometrics methods has been used to detect the geographical origin of honey samples. The samples were divided into the training set and the test set by kennard-Stone algorithm. After being pre-treated with first derivative and autoscaling, the spectral data were compressed and de-noised using wavelet transform (WT). The radical basis function neural networks (RBFNN) and partial least squares-line discriminant analysis (PLS-LDA) were applied to develop classification models, respectively. The performances of different wavelet functions and decompositio...