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 application of metal nanoparticles as an efficient drug delivery system is one of the directions of cancer therapy development. However, this strategy requires precise information about how the drug interacts with the applied nanocarrier. In this study, atomic force microscopy combined with infrared spectroscopy (AFM-IR) was used for the first time to investigate the erlotinib adsorption structure on two different types of 15 nm metal nanoparticle mono-layers, namely, silver nanoparticle (AgNP) and gold nanoparticle (AuNP) mono-layers. Because the metal nanoparticles are loosely bound samples, only the tapping AFM-IR mode is suitable for the collection of IR maps and spectra for such a system. The obtained results indicated the relevance of the AFM-IR technique for characterizing drug interactions with a metal mono-layer surface. The investigated drug interacts with the AgNPs mainly through phenyl rings and methoxy moieties, while quinazoline, amino, and ethoxy moieties appear to be farther from the surface. For the AuNPs, the interaction occurs through both the phenyl ring and the quinazoline moiety. Additionally, the aliphatic groups of erlotinib directly participate in this interaction. The novelty of the present work is also related to the use of the tapping AFM-IR mode to study metal NP mono-layers with a drug adsorbed on them. The collected IR maps for the most enhanced erlotinib bands show specific areas with very high signal intensity. The connection between these areas and the “hot spots” typical for the surface plasmon resonance phenomenon of metals is considered.