农业工程学报2011，Vol.27Issue(8)：350-354,5.DOI:10.3969/j.issn.1002-6819.2011.08.061
近红外光谱结合化学计量学方法检测蜂蜜产地
Detection of geographical origin of honey using near-infrared spectroscopy and chemometrics
摘要
Abstract
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 decomposition levels were evaluated in relation to the total prediction accuracy for the test set. For apple honey samples, when wavelet function was dbl and decomposition level was 2, both WT-RBFNN model and WT-PLS-LDA model produced the largest total prediction accuracy of 96.2%. For rape honey samples, when wavelet function was db4 and decomposition level was 1, WT-RBFNN model made the largest total prediction accuracy of 85.7%; while when wavelet function was db9 and decomposition level was also 1, WT-PLS-LDA model got the largest total prediction accuracy of 90.5%; The results indicated that linear WT-PLS-LDA model was more suitable for geographical classification of honey samples than no-linear WT-RBFNN model. Near infrared spectroscopy technique have a potential for quickly detecting geographical classification of honey samples.关键词
近红外光谱/小波变换/径向基函数神经网络/蜂蜜/产地判别/偏最小二乘-线性判别分析Key words
near-infrared spectroscopy/wavelet transform (WT)/models/honey/geographical classification/radical basis function neural networks (RBFNN)/partial least squares-line discriminant analysis (PLS-LDA)分类
数理科学引用本文复制引用
李水芳,单杨,朱向荣,李忠海..近红外光谱结合化学计量学方法检测蜂蜜产地[J].农业工程学报,2011,27(8):350-354,5.基金项目
“十一五”国家科技支撑计划项目(2009BADB9B07) （2009BADB9B07）
