Quantitative Analysis of Near-Infrared Spectroscopy by Combined Stationary Wavelet Transform-Support Vector Machine
文献类型: 外文期刊
第一作者: Xing, Li
作者: Xing, Li;Chen, Longjian
作者机构:
关键词: data preprocessing;multiplicative scatter correction;near-infrared spectroscopy;stationary wavelet transform;support vector machine
期刊名称:SPECTROSCOPY LETTERS ( 影响因子:1.179; 五年影响因子:1.176 )
ISSN: 0038-7010
年卷期: 2013 年 46 卷 1 期
页码:
收录情况: SCI
摘要: Data preprocessing and multivariate regression methods are two key factors influencing the model prediction ability of near-infrared (NIR) spectroscopy. The present paper evaluated the application of the combined stationary wavelet transformsupport vector machine method for developing juice NIR models. The performance of this method has been compared with other methods, such as stand normal variatepartial least squares, stationary wavelet transformpartial least squares, and stand normal variatestationary wavelet transform methods. The result showed that compared with other methods, the stationary wavelet transformsupport vector machine method can provide good quantitative analysis on saccharose concentration in juice.
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