**Raman Spectroscopy for In Vivo Medical Diagnosis** Raman Spectroscopy for In Vivo Medical Diagnosis

DOI: 10.5772/intechopen.72933

Miguel Ghebré Ramírez-Elías and Francisco Javier González Miguel Ghebré Ramírez-Elías and

Francisco Javier González

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72933

#### Abstract

Raman spectroscopy is a noninvasive optical technique that can be used as an aid in diagnosing certain diseases and as an alternative to more invasive diagnostic techniques such as the biopsy. Due to these characteristics, Raman spectroscopy is also known as an optical biopsy technique. The success of Raman spectroscopy in biomedical applications is based on the fact that the molecular composition of healthy tissue is different from diseased tissue; also, several disease biomarkers can be identified in Raman spectra, which can be used to diagnose or monitor the progress of certain medical conditions. This chapter outlines an overview of the use of Raman spectroscopy for in vivo medical diagnostics and demonstrates the potential of this technique to address biomedical issues related to human health.

Keywords: Raman spectroscopy, biomedical, chemometrics

#### 1. Introduction

Raman spectroscopy is based on the inelastic scattering of photons, also known as Raman effect, discovered by C. V. Raman in 1928 [1]. When a sample is illuminated with a light source, the incoming photons are absorbed or scattered. If absorbed, the photon energy is transferred to the molecules, whereas if a photon is scattered and the energy is conserved, it is called elastic scattering. However, a small portion of scattered photons (1 in every 10 billion photons) can be scattered inelastically, which means a slight change in the photon energy. This small energy difference between the incident and the scattered photon is the Raman effect. Raman spectroscopy has several advantages for biomedical applications, including being nondestructive and relatively fast to acquire, and provides information at the molecular level. Additionally, water produces weak Raman scattering, which means the presence of water in the sample does not interfere with the spectrum that is being analyzed. The main disadvantages of Raman

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

spectroscopy include the extremely weak Raman signal and the presence of undesirable noise sources such as the intense fluorescence background present in biological samples.

typical Raman detection system used for biomedical applications consists of a spectrograph attached to a cooled charge coupled device (CCD). Most CCDs use a thermoelectric (TE) system to cool the detector down to 70C in order to reduce thermal noise. The detection system also requires a spectrograph coupled to the Raman probe and to the CCD. It is recommended the spectrograph have a spectral resolution of 8–10 cm<sup>1</sup> in order to provide detailed information of biological Raman bands. The spectral resolution depends on spectrograph optical parameters, the diffraction grating, and the CCD pixel size. A schematic of the

Raman Spectroscopy for In Vivo Medical Diagnosis http://dx.doi.org/10.5772/intechopen.72933 295

A big issue in biological Raman spectroscopy is the presence of undesirable background elements related to different sources such as intrinsic fluorescence, noise introduced by the

The main sources of noise present in Raman spectra from biological samples are the shot noise, fluorescence background, flicker noise, dark current, and thermal noise. One alternative to reduce the thermal noise and dark signal is the use of a Raman system with high quality, thermoelectric cooled spectrometers. In Raman spectra, most of the time, the shot noise is the predominant noise associated with the particle nature of light. The approximate shot noise associated with measurement of n counts is n1/2. Thus the signal to noise ratio (S/N) can be improved incrementing the number of counts n. In other words, S/N can be improved by increasing averaging time due to the fact the signal increases proportionally with time. There are several multitude noise removal techniques that can be applied to Raman spectra. Smoothing is often employed for the removal of high-frequency components from Raman spectra, based on the fact that noise appears as high-frequency fluctuations, whereas signals are assumed to be low frequency. One smoothing technique is Fourier filtering [3]. In this technique, the higher frequency fluctuations, which are considered only noise, can be removed and the lower frequency ones can be used to reconstruct Raman spectra without noise. One drawback of this method is that the removal of the higher frequency noise may often introduce artifacts and distortion in Raman spectra. A commonly used smoothing technique is Savitzky-Golay (SG) filtering. The SG filter is a moving window–based local polynomial fitting procedure [4]. As the moving window size increases, some of the Raman bands may disappear. Therefore, it is very important to choose the appropriate parameters such as the polynomial order and the moving window size to avoid loss of Raman data. Other smoothing methods are locally weighted scatter plot smoothing (LOWESS) [5] and wavelet filtering [6] whereby the spectrum is decomposed using the discrete wavelet transform in order to isolate the noise by localizing it in space and frequency. Once it is isolated, it can be set to zero and the inverse wavelet transform is used to reconstruct the data. In all the mentioned methods, parameters have to be chosen carefully to avoid the important

typical arrangement of these components is shown in Figure 1.

equipment used, and the noise generated by external sources.

Raman bands being eliminated during smoothing.

3. Data preprocessing

3.1. Smoothing and denoising
