# Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins
## 1 Introduction
Unfortunately, there is no good strategy developed thus far to deal with this issue for FTIR analysis of protein secondary structure [27]. In Raman spectroscopy, interference with a strong H\({}_{2}\)O signal is less of an issue as the intensity of the scattering depends on changes in polarizability of a molecule [10]. Raman is inherently more sensitive towards non-polar groups like C=C, C-C, and S-S [28]. Water as a strongly polar molecule with weak polarizability only gives very weak Raman signals [29]. As such, Raman is considered to be a more suitable technique to study the protein structure in complex high moisture matrices and aqueous solutions [10]. Raman, however, suffers from other limitations such as low signal to noise ratios and fluorescence interference. Fluorescence interference is especially worrisome for proteins as the aromatic amino acids (i.e., tryptophan, tyrosine, and phenylalanine) display strong fluorescent properties [10; 28]. The fluorescence issue can be overcome by using more red-shifted laser light for the Raman analysis as these typically do not contain enough energy to cause excitation of the intrinsic fluorophores in proteins and, hence, reduce fluorescence emission [28]. The amide I band in FTIR and FT-Raman spectra of protein samples is literally composed as a superimposition of C=O stretching signals of the different secondary structures in a protein. This results in a complex signal that is challenging to study [30]. Overlapping of peaks will be caused by two factors: (i) the finite instrumental resolution and (ii) the intrinsic physical nature of the sample (i.e., solid or liquid) [31]. Increasing the instrument's resolution can partly solve the first factor and give better-resolved peaks. However, this leads to a lower signal to noise ratio and typically noisier scans [27]. In addition, choosing resolutions higher than 4 cm\({}^{-1}\) for solid and liquid samples does not necessarily add more information and generally only results in noisier spectra [27]. The decreased spectral resolution due to the intrinsic physical nature of the sample can be partially overcome by mathematical band narrowing/resolution enhancement methods [30]. Fourier self-deconvolution, second derivative analysis, and band curve-fitting are three mathematical resolution enhancement methods that enable studying individual secondary structures within the highly complex amide I band [2; 5; 24]. These techniques allow the quantitative estimation of protein secondary structures [2; 24; 32]. Fourier self-deconvolution is a mathematical resolution enhancement method by which the spectrum is divided by the instrumental function to reverse signal distortion in the Fourier domain [4; 31; 33]. Hence, it pulls apart and sharpens the overlapping hidden peaks. However, deconvolution can only be applied if the peaks are broader than the instrumental resolution [27]. In general, the deconvolution is an unstable numerical process that can produce hundreds or thousands of new peaks that are not real, a phenomenon known as overdeconvolution [27; 31]. Therefore, precautions need to be taken while doing deconvolution as it might distort and destroy the original spectral data. Second derivative analysis can be used as a guide in the deconvolution process to distinguish the real peaks. Derivative spectroscopy involves the generation of the nth derivative of the original spectrum. While the first order derivative visualizes the rate of intensity change with respect to the wavenumber of an FTIR spectrum, the second derivative contains information on the change of this rate (slope) of the spectrum [6]. The presence of overlapping peaks and shoulders affects the change in the slope of the spectrum; which explains why studying the second derivative is a perfect tool to unveil overlapping features [27]. A minimum in the second derivative of a spectrum corresponds to a local maximum in the original spectrum [27].