A support vector machine-based analysis method with wavelet denoised near-infrared spectroscopy
A support vector machine-based analysis method with wavelet denoised near-infrared spectroscopy
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DOI：
10.1016/j.vibspec.2008.10.008
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发表时间：
2009-03-18
影响因子：
2.5
通讯作者：
Ren, Yulin
中科院分区：
文献类型：
--
作者：
Liang, Liwen;Wang, Bin;Ren, Yulin
本文研究了用漫反射光谱法对单片完整片剂中的西咪替丁进行快速、非破坏性定量分析。支持向量机（SVM）被引入到建模的多元，非线性系统的校准样本的径向基函数。用支持向量机对760-1100 nm的短波近红外光谱进行处理。采用小波变换的方法消除了噪声的影响。与偏最小二乘法（PLS）相比，SVM对独立测试集的测量误差给出了相对合理的结果。实验结果表明，小波去噪预处理的支持向量机是一种有效的方法，需要较少的校准样本。(C)2008 Elsevier B. V.保留所有权利。
This paper is concerned with the rapid and non-destructive quantitative analysis of cimetidine in single intact tablets by diffuse reflectance spectroscopy. Support vector machines (SVM) are introduced to model multivariate, non-linear systems of calibration samples by radical basis functions. Short-wave near-infrared spectra ranging 760-1100 nm are processed by SVM. Wavelet method has been employed to minimize the influence of noise. Measurement errors of independent testing set by SVM compared to partial least squares (PLS) give relatively reasonable results. Experiments show that SVM with wavelet denoising pretreatment is an effective method and requires less number of calibration samples. (C) 2008 Elsevier B.V. All rights reserved.