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Wavelet analysis of near infrared spectral data in the application of denoising

机译:近红外光谱数据近红外光谱数据的小波分析

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Spectrum signal may contain many peaks or mutations and noise also is not smooth white noise, to this kind of signal analysis, must do signal pretreatment, remove part of signal and extract useful part of signal. Based on the data of blood glucose near-infrared spectrum as the research object to explore the application of wavelet transform in the near infrared spectrum signal denoising, and through the simulation results show that using wavelet analysis of near infrared spectral data pretreatment than the traditional Fourier method can be higher precision of prediction.
机译:频谱信号可以包含许多峰值或突变,噪声也不是光滑的白噪声,对于这种信号分析,必须做信号预处理,删除一部分信号并提取信号的有用部分。基于血糖近红外光谱作为研究对象,探讨了小波变换在近红外频谱信号去噪的应用,并通过模拟结果表明,使用近红外光谱数据预处理的小波分析比传统的傅里叶方法可以是更高的预测精度。

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