化学计量学
可追溯性
傅里叶变换红外光谱
傅里叶变换光谱学
傅里叶变换
分析化学(期刊)
化学
数学
统计
环境化学
工程类
色谱法
化学工程
数学分析
作者
Chao Li,Shengchao Yang,Qiao-Sheng Guo,Kaiyan Zheng,Pingli Wang,Zhen‐Gui Meng
标识
DOI:10.1016/j.saa.2015.07.086
摘要
A combination of Fourier transform infrared spectroscopy with chemometrics tools provided an approach for studying Marsdenia tenacissima according to its geographical origin. A total of 128 M. tenacissima samples from four provinces in China were analyzed with FTIR spectroscopy. Six pattern recognition methods were used to construct the discrimination models: support vector machine–genetic algorithms, support vector machine–particle swarm optimization, K-nearest neighbors, radial basis function neural network, random forest and support vector machine–grid search. Experimental results showed that K-nearest neighbors was superior to other mathematical algorithms after data were preprocessed with wavelet de-noising, with a discrimination rate of 100% in both the training and prediction sets. This study demonstrated that FTIR spectroscopy coupled with K-nearest neighbors could be successfully applied to determine the geographical origins of M. tenacissima samples, thereby providing reliable authentication in a rapid, cheap and noninvasive way.
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