Abstract
Abstract Trend removal is an important problem in most communication systems. Here, we show a proposed algorithm for trend (background) removal from Raman Spectra by merging local variance estimation and cubic spline interpolation methods. We found that Raman spectrum does not need a smoothing process to remove trend from noisy signals. Employing this technique results in more speedy and noiseless systems than other techniques that use wavelet transformation to suppress noise. Keywords Raman spectroscopy, Background correction method, Local variance, Cubic spline interpolation. 1. Introduction Spectroscopy is the study of the interaction between matter (a particle that has rest mass) and radiated energy. Spectroscopic data is often represented by a spectrum, a (frequency & intensity) which is the response of intensity to the frequency [1]. Raman spectroscopy is an application of spectroscopy, and it has a lot of advantages. It can be used with solids and liquids. There is no need for sample preparation. It is non-destructive, and it is acquired quickly within seconds. Also, Raman spectroscopy has some disadvantages. It cannot be used for metals or alloys. The detection process needs a sensitive instrumentation. The Sample can be destroyed by heating through the intense laser radiation, and the fluorescence of impurities in the sample itself can hide the Raman spectrum [2]. Raman spectrum is defined as a plot of the intensity of Raman scattered radiation as a function of its frequency difference from the incident radiation. Due to the existence of the background affecting the main spectrum, the detection becomes very difficult. So, it is necessary for applying
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