**1. Introduction**

Spectroscopy is a relevant tool for biomedical analysis. Significant progresses in the application of spectroscopy in clinical field were done in the last years. Spectroscopic techniques provide information at the molecular level, and it is possible to evaluate functional groups, bond types and molecular conformations of the biological components of a sample, once spectral signals in vibrational spectra are specific to each molecule and act like a fingerprint.

A spectroscopic-based metabolomic study includes sample collection, sample analysis, statistical analysis and identification of altered metabolites. The resulting data can be translated into defining disease biomarkers/pathways, with the generation of a disease metabolic fingerprint.

Vibrational spectroscopy has been proposed as an approach to diagnosis. Raman has a past research regarding its potential as a diagnostic tool of a wide range of pathologies using a

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wide range of sample types. Although being recognized as not as specific and sensitive as other metabolomics techniques, several works already demonstrated the potential of Raman applied to health sciences for metabolic fingerprinting because it is possible with only one spectra to simultaneously analyze carbohydrates, amino acids, fatty acids, lipids, proteins, nucleic acids and polysaccharides with a minimum sample preparation. As Raman is a scattering technique and it is not perturbed by aqueous media, it is suitable to analyze biological samples.

to obtain good spectroscopic outcome. Raman spectroscopy is also a nondestructive method, and it is possible to evaluate the chemical composition of a sample, and the same aliquot can be used further to extract additional biological information by using other methodologies [1].

Raman Spectroscopy Applied to Health Sciences http://dx.doi.org/10.5772/intechopen.73087 277

In recent past, Raman spectroscopy was known as a technique with low signals requiring longer acquisition times. However, recent developments were made to overcome this limitation in the last years. Nonlinear optical effects and metallic nanoparticles are currently used to improve Raman signals, fiber-optic Raman probes were introduced and are used for real-time *in-vivo* experiments, and multimodal integration with other optical techniques increased the acquisition speed and spatial accuracy. These advances in the accuracy allow the application of Raman spectroscopy into clinical diagnosis, and time of analysis allows its clinical use.

Surface enhanced Raman spectroscopy (SERS), Tip Enhanced Raman Scattering (TERS) or nano-Raman and resonance effects increase significantly Raman sensitivity to study biological samples. SERS increases Raman intensity compared to the usual and weak Raman scattering. These improvement features are sufficient to allow even single molecule detection using Raman. SERS is useful in trace material analysis, flow cytometry and other applications where the traditional sensitivity/speed of a Raman measurement is insufficient [2]. Resonance Raman spectroscopy is a variant of Raman spectroscopy that instead of using laser excitation at any wavelength to measure Raman scattering of the laser light, the excitation wavelength is used to overlap with an electronic transition. The overlap results in an extraordinary increase in scattering intensities, thus detection limits and measurement times can be significantly decreased. It is also possible to couple Raman to an optical microscope. Raman microspectroscopy uses visible and near-infrared excitation lasers and allows to extract molecular properties of the samples with diffraction-limited spatial resolution. The typical method to obtain Raman spectral images is by scanning the sample with the laser spot and then applying a uni- or multivariate spectral model to each Raman spectrum [3]. In order to decrease the time of analysis, Raman spectral imaging can be based on line-mapping (laser beam is expanded

TERS is a chemical imaging technique that is label-free and have enhanced-resolution. TERS imaging is performed with a Raman spectrometer, a scanning probe microscope (SPM) integrated with an optical microspectrometer. The scanning probe microscope provides the means for nanoscale imaging and the optical microscope provides the resources to bring the light to a functionalized probe, and the spectrometer is the sensor analyzing the light output providing chemical specificity. It is possible to increase the signal to obtain high-spatial resolution spectral images for large samples using selective-sampling Raman microspectroscopy. In this approach, it is possible to (1) obtain information about sample spatial features by other optical technique [5] or (2) estimate information in real-time from the Raman spectra [6]. When traditional variations of Raman spectroscopy are used to study tissues, the results are not good due to insufficient penetration depth. The advance of spatially offset Raman spectroscopy (SORS) overcame this limitation enabling spectral measurements until 10–20 mm of

**3. Raman spectroscopic variations**

to form a line spot on the sample surface) [4].

For diagnostic purposes, it is expected that Raman spectra of biological samples result in quantitative data, so it is essential to define some categorical differentiable classes for data by dividing samples in healthy or disease sample classes. For these purposes, chemometric data processing is a valuable tool.

Due to Raman spectroscopy features, it is currently widely used in health sciences for spectral imaging of cells and tissues, for the *in-vivo* and *ex-vivo* diagnosis of tissues, where fiber probes can be used, and for biofluid analysis, contributing to a better knowledge of the disease and disease diagnosis at the molecular level. This chapter describes the most relevant application of Raman in biomedical field.
