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Fluorescence background removal method for biological Raman spectroscopy based on empirical mode decomposition

机译:基于经验模态分解的生物拉曼光谱荧光背景去除方法

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Raman spectroscopy of biological tissue presents fluorescence background, an undesirable effect that generates false Raman intensities. This paper proposes the application of the Empirical Mode Decomposition (EMD) method to baseline correction. EMD is a suitable approach since it is an adaptive signal processing method for nonlinear and non-stationary signal analysis that does not require parameters selection such as polynomial methods. EMD performance was assessed through synthetic Raman spectra with different signal to noise ratio (SNR). The correlation coefficient between synthetic Raman spectra and the recovered one after EMD denoising was higher than 0.92. Additionally, twenty Raman spectra from skin were used to evaluate EMD performance and the results were compared with Vancouver Raman algorithm (VRA). The comparison resulted in a mean square error (MSE) of 0.001554. High correlation coefficient using synthetic spectra and low MSE in the comparison between EMD and VRA suggest that EMD could be an effective method to remove fluorescence background in biological Raman spectra.
机译:生物组织的拉曼光谱显示荧光背景,这是产生假拉曼强度的不良影响。本文提出了经验模态分解(EMD)方法在基线校正中的应用。 EMD是一种合适的方法,因为它是一种用于非线性和非平稳信号分析的自适应信号处理方法,不需要诸如多项式方法之类的参数选择。 EMD性能通过具有不同信噪比(SNR)的合成拉曼光谱进行评估。 EMD去噪后,合成拉曼光谱与恢复的拉曼光谱之间的相关系数高于0.92。另外,使用皮肤的二十个拉曼光谱来评估EMD性能,并将结果与​​温哥华拉曼算法(VRA)进行比较。比较得出的均方误差(MSE)为0.001554。使用EMD和VRA进行比较时,使用合成光谱具有较高的相关系数,而MSE较低,这表明EMD可能是去除生物拉曼光谱中荧光背景的有效方法。

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