数字农科院2.0

Quantitative Analysis of Near-Infrared Spectroscopy by Combined Stationary Wavelet Transform-Support Vector Machine

文献类型: 外文期刊

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

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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