Fourier transform infrared (FTIR) spectroscopy is a well-known method of analysis, with various applications, including promising potential for analyzing biological samples. In the bio-spectroscopy of cells, Mie scattering may increase, which then causes spectral distortion, due to the similarity of cell size with the IR medium-wavelength. These changes make the spectrum unreliable. In previous scattering elimination studies, questionable estimations were considered. For instance, all cells were considered as spherical objects or cell size was estimated randomly. In an attempt to provide the best equation based on the natural existence of cells for the FTIR Mie scattering correction, we examined the actual biological data of cells – as opposed to those yielded from mathematical manipulations. So five biological factors: cell size, shape, granularity, circularity, and edge irregularities, for each cell line were considered as factors which cause scattering. For measuring cell size, roundness and edge irregularity, microscopy images were obtained and processed. For evaluating cell line granularity, flow cytometry was used. Finally, by including these factors, an algorithm was designed. To assess the accuracy of the proposed algorithm, the trypsinized cell spectrum was considered as the high scattering spectrum. Cells were also cultured on a MirrIR slide, and their ATR-FTIR spectrum was considered as the minimum scattering spectrum. The algorithm using the abovementioned five characteristics was used for 13 different cell lines, and in some cases the corrected spectrum demonstrated more than 97% resemblance with the ATR spectra of the same cells. A comparison between the results of this algorithm with the Bassan et al. (2017) algorithm for scattering correction that is freely available on the Internet was then conducted on two different cell lines, clearly showing the advantages of our algorithm, in terms of accuracy and precision. Therefore, this method can be viewed as a more suitable solution for scattering correction in cell investigations.

Background: Alprazolam, a commonly prescribed benzodiazepine, poses risks of toxicity and severe withdrawal symptoms. There is an urgent need for a rapid and sensitive diagnostic method for detecting alprazolam poisoning. Objectives: This study aimed to detect alprazolam poisoning through Fourier-transform infrared (FTIR) analysis of saliva, addressing the need for a quick, cost-effective, and sensitive diagnostic method for poison control and differential diagnosis. Methods: Saliva samples were collected from 45 individuals with benzodiazepine toxicity, therapeutic consumption, and normal health status, as well as from a control group. The samples were analyzed using FTIR spectroscopy. The resulting spectra were processed with OriginPro software, and statistical analyses were performed using receiver operating characteristic (ROC) and analysis of variance (ANOVA). Results: The average age of the studied population was approximately 45 years, with women being the most affected by poisoning. Fourier-transform infrared analysis revealed significant differences in the structure of lipids between poisoned individuals, therapeutic receivers, and healthy individuals (P < 0.0001). Conclusions: Fourier-transform infrared analysis of saliva is a fast and accurate method for diagnosing alprazolam poisoning within minutes, enabling prompt and appropriate treatment during critical life-threatening situations. This non-invasive technique has the potential to guide treatment staff toward effective treatment options.