## 1 Introduction  
During last years, Fourier Transform Infrared (FTIR) micro-spectroscopy has been widely used to investigate different types of biomedical samples, as tissues [1; 2; 3], biological fluids [4; 5; 6], and cells [7; 8; 6; 9]. One of the main biomedical applications of FTIR micro-spectroscopy concerns the possibility to discriminate cancer cells from their normal counterpart according to specific spectral changes. [PERSON] et al. discriminated liver cancer cells from normal liver cells according to the intensity ratios of some specific FTIR peaks related to cellular components [10]. [PERSON] et al. compared FTIR spectra of normal and several heterogeneous ovarian cancer cell lines and tissues, and they found spectral markers (consisting of relative FTIR band intensities) that were able to discriminate cancer samples from normal samples [11]. [PERSON] et al. used FTIR to distinguish between human astrocytes, microglia, glioma, and glioblastoma cells in vitro; they found that the protein secondary structure was an indicator to distinguish tumor cells from glial cells [12]. As these spectral differences are very small, multivariate statistical techniques, like Principal Component Analysis (PCA), are commonly used to analyze and successfully discriminate the spectral data [13,14]. In fact, PCA transforms N original variables (as the absorption values for the N wavenumber values in a group of all the M measured FTIR spectra) into a new set of N variables, called principal components (PCs), each one is a linear combination of the original variables. The PCs maintain all the variance of the original data, but the first PC contains the most of the variance in the dataset, and each subsequent PC contains less variance. The value that each PC takes for each spectrum is called the score for that PC. A score plot, reporting the score values of two different PC for all the M cellular spectra, describes differences among the M spectra, allowing separation of them into clusters based on similarities or differences in the spectral characteristics. The accuracy of FTIR micro-spectroscopy in the identification of pathological samples with respect to healthy samples has suggested the possibility of using this technique as a diagnostic tool in the clinical practice [15]. However, a right use of FTIR technique requires that samples are deposited on suitable substrates, which must be transparent or reflective to the IR radiation, whereas IR absorbing materials are to be avoided as substrates. Although thick glass slides (about 1 mm thickness) are widely used in cytology and histology, they absorb incident radiation at wavenumbers lower than 2000 cm\({}^{-1}\), i.e., in the fingerprint region, where the absorption peaks of most cellular components (DNA, lipids, and proteins) are included [16]. Moreover, glass coverslips, which are thin glass slides (about 0.17 mm thickness) usually used to cover biological samples deposited onto thick glass slides, absorb IR radiation at wavenumbers lower than 1500 cm\({}^{-1}\), and, consequently, they cannot measure the fingerprint region [17]. On the contrary, suitable substrates for measuring the cells samples' absorption in the fingerprint region are calcium fluoride (CaF\({}_{2}\)) crystals and IR-reflective substrates, such as MirrIR low-emissivity (low-e) substrates. The former are thick mid-IR transparent slides, whereas the latter consist of thick glass slides over which a thin Ag/SnO\({}_{2}\) coating has been deposited. The IR absorption measurement with such two substrates is carried out in transmission (as for CaF\({}_{2}\)) and transfection (as for low-e) mode. However, both such substrates are more expensive than glass and coverslip slides (particularly as far as CaF\({}_{2}\) is concerned); this is a drawback for the adoption of FTIR micro-spectroscopy in the clinical diagnostics, because the conventional diagnostic techniques make use of biological samples deposited on cheap substrates.