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结合小波变换与微分法改善近红外光谱分析精度
Near Infrared Spectra (NIR) Analysis of Octane Number by WaveletDenoising-Derivative Method
【摘要】 微分法可以有效消除光谱背景和基线漂移,同时会增加光谱噪音;小波变换具有很好的去噪功能,文章将微分法和小波变换结合用于重整汽油辛烷值近红外光谱分析。考察了微分噪音对辛烷值分析精度的影响以及小波去噪对微分光谱的噪音扣除以及对辛烷值分析精度改善情况。结果表明,微分光谱可以扣除原始光谱的基线漂移,提高分析精度,同时增加光谱的噪音;噪音对分析精度影响很大。微分光谱经过小波去噪处理后信噪比增加,辛烷值分析精度得到改善。
【Abstract】 Derivative can correct baseline effects and also increase the level of noise. Wavelet transform has been proven an efficient tool for de-noising. This paper is directed to the application of wavelet transfer and derivative in the NIR analysis of octane number (RON). The derivative parameters, as well as their effects on the noise level and analytic accuracy of RON, have been studied in detail. The results show that derivative can correct the baseline effects and increase the analytic accuracy. Noise from the derivative spectra has great detriment to the analysis of RON. De-noising of wavelet transform can increase the S/N and improve the analytical accuracy .
【Key words】 Near infrared analysis; Gasoline; Octane number; Wavelet transfer; Derivative; De-noising;
- 【文献出处】 光谱学与光谱分析 ,Spectroscopy and Spectral Analysis , 编辑部邮箱 ,2005年04期
- 【分类号】O657.33
- 【被引频次】83
- 【下载频次】679