Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.

The work presents a comprehensive vibrational analysis of the process of adsorption of threonine (Thr) onto an Fe surface with deposited Cu nanoparticles (NPs) (of about 4–5 nm in size) in a corrosive environment. The application of surface-enhanced Raman spectroscopy (SERS) and surface-enhanced infrared absorption spectroscopy (SEIRA) provides the opportunity for detailed description of adsorption geometry of amino acid onto a metal surface. The combination of conventional infrared spectroscopy (IR) with atomic force microscopy (AFM) resulted in a nano-SEIRA technique which made it possible to provide a precise description of adsorbate binding to the metal surface. The studies presented confirmed that there is a very good correlation between the spectra recorded by the SERS, SEIRA, and nano-SEIRA techniques. Threonine significantly influenced the process of corrosion of the investigated surface due to the existing strong interaction between the protonated amine and carboxylate groups and the CuNPs deposited onto the Fe surface. In addition, the application of two polarization modulations (s and p) in nano-SEIRA allows subtle changes to be observed in the molecule geometry upon adsorption, with the carboxylate group of Thr being almost horizontally oriented onto the metal surface; whereas the amine group that contains nitrogen is oriented perpendicular to this surface.