Classification of the Residues after High and Low Order Explosions Using Machine Learning Techniques on Fourier Transform Infrared (FTIR) Spectra.

Forensic science is a field that requires precise and reliable methods for the detection and analysis of evidence. One such method is Fourier Transform Infrared (FTIR) spectroscopy, which provides high sensitivity and selectivity in the detection of samples. In this study, the use of FTIR spectroscopy and statistical multivariate analysis to identify high explosive (HE) materials (C-4, TNT, and PETN) in the residues after high- and low-order explosions is demonstrated. Additionally, a detailed description of the data pre-treatment process and the use of various machine learning classification techniques to achieve successful identification is also provided. The best results were obtained with the hybrid LDA-PCA technique, which was implemented using the R environment, a code-driven open-source platform that promotes reproducibility and transparency.

Comparing infrared spectroscopic methods for the characterization of Plasmodium falciparum-infected human erythrocytes.

Malaria, caused by parasites of the species Plasmodium, is among the major life-threatening diseases to afflict humanity. The infectious cycle of Plasmodium is very complex involving distinct life stages and transitions characterized by cellular and molecular alterations. Therefore, novel single-cell technologies are warranted to extract details pertinent to Plasmodium-host cell interactions and underpinning biological transformations. Herein, we tested two emerging spectroscopic approaches: (a) Optical Photothermal Infrared spectroscopy and (b) Atomic Force Microscopy combined with infrared spectroscopy in contrast to (c) Fourier Transform InfraRed microspectroscopy, to investigate Plasmodium-infected erythrocytes. Chemical spatial distributions of selected bands and spectra captured using the three modalities for major macromolecules together with advantages and limitations of each method is presented here. These results indicate that O-PTIR and AFM-IR techniques can be explored for extracting sub-micron resolution molecular signatures within heterogeneous and dynamic samples such as Plasmodium-infected human RBCs.

Influence of spectral resolution, spectral range and signal-to-noise ratio of Fourier transform infra-red spectra on identification of high explosive substances.

In the contemporary spectroscopy there is a trend to record spectra with the highest possible spectral resolution. This is clearly justified if the spectral features in the spectrum are very narrow (for example infra-red spectra of gas samples). However there is a plethora of samples (in the liquid and especially in the solid form) where there is a natural spectral peak broadening due to collisions and proximity predominately. Additionally there is a number of portable devices (spectrometers) with inherently restricted spectral resolution, spectral range or both, which are extremely useful in some field applications (archaeology, agriculture, food industry, cultural heritage, forensic science). In this paper the investigation of the influence of spectral resolution, spectral range and signal-to-noise ratio on the identification of high explosive substances by applying multivariate statistical methods on the Fourier transform infra-red spectral data sets is studied. All mathematical procedures on spectral data for dimension reduction, clustering and validation were implemented within R open source environment.

Application of linear discriminant analysis in prostate cancer research by synchrotron radiation-induced X-ray emission.

The ability to visualize an object of interest is one of the cornerstones of advancement in science. For this reason, synchrotron radiation-induced X-ray emission (micro-SRIXE) holds special promise as a imaging technique in structural biology, biochemistry, and medicine. It gives the possibility to image concentration of most of the elements in a sample at high space resolution. Statistical analysis of data obtained for samples of prostate tissues in an experiment at L-beam line HASYLAB (Hamburg, Germany) is presented in this paper. The regions for the measurements were selected according to the histological view of the sample. By histological examination, samples were divided into five groups (from healthy to Gleason4, most advanced stage of cancerogenesis). Data obtained in micro-SRIXE experiments on prostate cancer samples provide information about concentrations of certain elements in these groups. The rising problem is to find out concentrations of which elements allow the researcher to discriminate between different (early mentioned) groups. Linear discriminant analysis, a basic technique for feature extraction, was used in statistical analysis of the data. Our results indicate that the use of synchrotron radiation and discriminant analysis in the study of prostate cancer tissues provide information that can be key to better understanding of biomolecular functions.