**4. Multivariate analysis applied to spectroscopic data analysis**

Multivariate analysis tools are used to extract information from spectral data set and to assist in biomedical interpretation. Chemometric methods are diverse and offer different approaches to extract specific information from the data. These methods used for data interpretation and to extract information from complex datasets were usually univariate and therefore they were not suitable to describe the sample variation or composition. A range of processing methods can be applied to a Raman data set; usually baseline corrections should be performed as a first approach, prior to normalization methods and then a wide range of multivariate analysis tolls can be applied [12].

**5. Raman spectroscopy in cancer diagnosis**

for diagnosis of different cancer types (**Figure 2**).

cases and cancer mortality.

from [13, 14].

**5.1. Skin cancers**

A preeminent application of Raman spectroscopy in health sciences is its use in cancer diagnosis. According to Cancer Research UK, in 2012, an estimated 14.1 million new cancer cases occurred worldwide, resulting in 8.2 million people died [15]. Despite the majority of new cancer cases was registered in less-developed countries, a significative number (more than 6 million cases) occurs in developed regions, with access to advanced medical care and treatments [16]. These data reveal the urgent need of reliable diagnostic tools to reduce cancer

**Figure 1.** Most frequently applied multivariate data analysis techniques in combination with spectroscopic methods. MVA: Multivariate data analysis; PCR: Principal component regression; PLS: Partial least-squares regression; PCA: Principal-component analysis; CA: Cluster analysis; LDA: Linear discriminant analysis; PLS-DA: Partial least-squares discriminant analysis; SIMCA: Soft independent modeling of class analogy; ANNS: Artificial neural networks. Adapted

Raman Spectroscopy Applied to Health Sciences http://dx.doi.org/10.5772/intechopen.73087 279

The common methodologies for cancer diagnosis are based on invasive histological analysis and biomedical imaging, which are expensive, time-consuming and can give rise to subjective diagnosis [17]. Raman spectroscopy has been extensively studied in an attempt to replace or complement current methods and increase sensitivity and specificity of the diagnosis [18]. In this section, we discuss the advances in Raman spectroscopy applied to biofluids and tissue

Raman has been widely investigated to discriminate benign skin lesions from malignant lesions, mainly basal cell carcinoma and squamous cell carcinoma (**Table 1**). Despite the high complexity of skin tissue, studies *in vivo* show huge potential as a routine procedure in hospitals for cancer screening [19]. In 2008, a preliminary report used 289 patients with different

Multivariate methods include techniques of multivariate classification (or pattern recognition techniques) and multivariate regression. Techniques of multivariate classification can be divided into unsupervised and supervised learning procedures. In unsupervised pattern recognition techniques such as principal-component analysis (PCA) and cluster analysis (CA), there is no need for *a priori* knowledge about the training set samples (spectra). These methods are used to consider differences and similarities between spectra. By contrast, supervised pattern recognition techniques such as linear discriminant analysis (LDA) and artificial neural networks (ANNS) require some *a priori* knowledge, for example, undoubtedly identifying samples from disease and samples from healthy cases. In this way, supervised procedures allow a more precise classification, while unsupervised methods are useful for an exploratory analysis of data.

On the other hand, multivariate regression techniques (or multivariate calibration methods) are usually applied to analyze one or multiple molecules of a complex sample that possess overlapping spectroscopic signals. These techniques include principal components regression (PCR) and partial least squares regression (PLS) (**Figure 1**).

**Figure 1.** Most frequently applied multivariate data analysis techniques in combination with spectroscopic methods. MVA: Multivariate data analysis; PCR: Principal component regression; PLS: Partial least-squares regression; PCA: Principal-component analysis; CA: Cluster analysis; LDA: Linear discriminant analysis; PLS-DA: Partial least-squares discriminant analysis; SIMCA: Soft independent modeling of class analogy; ANNS: Artificial neural networks. Adapted from [13, 14].
