Propellant’s differentiation using FTIR-photoacoustic detection for forensic studies of improvised explosive devices

Significant reduction of the culturing time required for bacterial identification and antibiotic susceptibility determination by infrared spectroscopy

Rapid testing of bacteria for antibiotic susceptibility is essential for effective treatment and curbing the emergence of multidrug-resistant bacteria. The misuse of antibiotics, coupled with the time-consuming classical testing methods, intensifies the threat of antibiotic resistance, a major global health concern. In this study, employing infrared spectroscopy-based machine learning techniques, we significantly shortened the time required for susceptibility testing to 10 hours, a significant improvement from the 24 hours in our previous studies as well as the conventional methods that typically take at least 48 hours. This remarkable reduction in turnaround time (from 48 hours to 10 hours), achieved by minimizing the culturing period, offers a game-changing advantage for clinical applications. Our study involves a dataset comprising 400 bacterial samples (200 E. coli, 100 Klebsiella pneumoniae, and 100 Pseudomonas aeruginosa) with an impressive 96% accuracy in the taxonomic classification at the species level and up to 82% accuracy in bacterial susceptibility to various antibiotics.

Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics

Among the most prevalent and detrimental bacteria causing urinary tract infections (UTIs) is Klebsiella (K.) pneumoniae. A rapid determination of its antibiotic susceptibility can enhance patient treatment and mitigate the spread of resistant strains. In this study, we assessed the viability of using infrared spectroscopy-based machine learning as a rapid and precise approach for detecting K. pneumoniae bacteria and determining its susceptibility to various antibiotics directly from a patient's urine sample. In this study, 2333 bacterial samples, including 636 K. pneumoniae were investigated using infrared micro-spectroscopy. The obtained spectra (27996spectra) were analyzed with XGBoost classifier, achieving a success rate exceeding 95 % for identifying K. pneumoniae. Moreover, this method allows for the simultaneous determination of K. pneumoniae susceptibility to various antibiotics with sensitivities ranging between 74 % and 81 % within approximately 40 min after receiving the patient's urine sample.

Quick Detection of Proteus and Pseudomonas in Patients' Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning

Bacterial resistance to antibiotics is a primary global healthcare concern as it hampers the effectiveness of commonly used antibiotics used to treat infectious diseases. The development of bacterial resistance continues to escalate over time. Rapid identification of the infecting bacterium and determination of its antibiotic susceptibility are crucial for optimal treatment and can save lives in many cases. Classical methods for determining bacterial susceptibility take at least 48 h, leading physicians to resort to empirical antibiotic treatment based on their experience. This random and excessive use of antibiotics is one of the most significant drivers of the development of multidrug-resistant (MDR) bacteria, posing a severe threat to global healthcare. To address these challenges, considerable efforts are underway to reduce the testing time of taxonomic classification of the infecting bacterium at the species level and its antibiotic susceptibility determination. Infrared spectroscopy is considered a rapid and reliable method for detecting minor molecular changes in cells. Thus, the main goal of this study was the use of infrared spectroscopy to shorten the identification and the susceptibility testing time of Proteus mirabilis and Pseudomonas aeruginosa from 48 h to approximately 40 min, directly from patients' urine samples. It was possible to identify the Proteus mirabilis and Pseudomonas aeruginosa species with 99% accuracy and, simultaneously, to determine their susceptibility to different antibiotics with an accuracy exceeding 80%.

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.

Quantitative analysis of smokeless powder particles in post‐blast debris via gas chromatography/vacuum ultraviolet spectroscopy ( GC / VUV )

Forensic analysis of smokeless powder particles recovered from the debris of an improvised explosive device can provide information about the type of smokeless powder used and can aid investigation efforts. In this study, quantitative methods were used to yield information about the difference in the chemical composition of the particles pre‐ and post‐blast. The technique, gas chromatography/vacuum ultraviolet spectroscopy (GC/VUV), was able to quantify nitroglycerin, 2,4‐dinitrotoluene, diphenylamine, ethyl centralite, and di‐n‐butyl phthalate in pre‐ and post‐blast smokeless powder particles using heptadecane as an internal standard. Post‐blast debris was obtained via controlled explosions with assistance from the Indiana State Police Bomb Squad. Two galvanized steel and two polyvinyl chloride pipe bombs were assembled. Two devices contained single‐base smokeless powder and two contained double‐base smokeless powder. 2,4‐dinitrotoluene and diphenylamine were successfully quantified in the single‐base smokeless powder post‐blast debris while nitroglycerin, diphenylamine, and ethyl centralite were successfully quantified in the double‐base smokeless powder post‐blast debris. Compounds were detected at concentrations as low as 9 μg of 2,4‐dinitrotoluene per mg, <3 μg of diphenylamine per mg, 131 μg of nitroglycerin per mg, and <3 μg of ethyl centralite per mg. Concentration changes between pre‐ and post‐blast smokeless powder particles were determined as well as microscopic differences between pre‐ and post‐blast debris for both smokeless powders in all devices. To our knowledge, this is the first use of GC/VUV for the quantification of explosives.

Preliminary stable isotope analyses for propellant discrimination in shotshells

RATIONALE

This study aimed to develop methods to determine the identity and trace the origin of propellants used in shotshells. Specifically, the use of organic component and stable isotope analysis techniques, such as bulk stable isotope analysis (BSIA) and compound-specific isotope analysis (CSIA) techniques, for the study of shotshell propellants was investigated.

METHODS

Nine samples of shotshell propellants from different manufacturing countries and brands were analyzed for explosive and additive components by gas chromatography/mass spectrometry and thin-layer chromatography. BSIA of the propellants was achieved using elemental analysis/isotope ratio mass spectrometry without a pretreatment process. For the CSIA of nitroglycerin, double-base powder propellants were extracted with ether, and the isotope ratios of carbon and nitrogen were measured by gas chromatography/isotope ratio mass spectrometry.

RESULTS

Nine samples drawn from seven brands in four countries were classified into five groups by organic component analysis, while eight classification groups were identified by BSIA. Thus, two samples could not be distinguished from each other by either BSIA or organic component analysis. Subsequently, with the use of results obtained with CSIA for nitroglycerin, all the samples could be classified into different groups. These findings suggest that the nine propellant samples were all composed of different ingredients or raw materials from different sources.

CONCLUSIONS

Stable isotope ratio analyses were performed for propellant discrimination. The combined BSIA, CSIA and organic component analysis techniques were able to successfully distinguish the nine shotshell propellants from seven brands sourced from four different countries, and the results suggested that the samples contained different ingredients or raw materials from different sources. We therefore can conclude that reliable results can be obtained using combined isotope analysis methods such as CSIA and BSIA for origin tracing and identity determination.

Rapid detection of Klebsiella pneumoniae producing extended spectrum β lactamase enzymes by infrared microspectroscopy and machine learning algorithms

Antimicrobial drugs have played an indispensable role in decreasing morbidity and mortality associated with infectious diseases. However, the resistance of bacteria to a broad spectrum of commonly-used antibiotics has grown to the point of being a global health-care problem. One of the most important classes of multi-drug resistant bacteria is Extended Spectrum Beta-Lactamase-producing (ESBL+) bacteria. This increase in bacterial resistance to antibiotics is mainly due to the long time (about 48 h) that it takes to obtain lab results of detecting ESBL-producing bacteria. Thus, rapid detection of ESBL+ bacteria is highly important for efficient treatment of bacterial infections. In this study, we evaluated the potential of infrared microspectroscopy in tandem with machine learning algorithms for rapid detection of ESBL-producing Klebsiella pneumoniae (K. pneumoniae) obtained from samples of patients with urinary tract infections. 285 ESBL+ and 365 ESBL-K. pneumoniae samples, gathered from cultured colonies, were examined. Our results show that it is possible to determine that K. pneumoniae is ESBL+ with ∼89% accuracy, ∼88% sensitivity and ∼89% specificity, in a time span of ∼20 minutes following the initial culture.

Interpol review of detection and characterization of explosives and explosives residues 2016-2019

This review paper covers the forensic-relevant literature for the analysis and detection of explosives and explosives residues from 2016-2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/Resources/Documents#Publications.

Screening of Gunshot Residue in Skin Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Hyperspectral Microscopy

The detection of gunshot residues (GSR) in skin is important in criminal forensic investigations related with firearms. Conventionally, the procedure is based on the detection of metallic or inorganic residues (IGSR). In this work, we propose attenuated total reflectance Fourier transform infrared (ATR FT-IR) hyperspectral microscopy as a complementary and nondestructive technique for detection of organic GSR (OGSR). The spectra were acquired from GSR of three ammunition manufacturers, which were collected from shooter's hands by the tape-lifting method. Before spectroscopic analysis, a Na-Ca bleach solution was added to all GSR samples on the tape for destroying skin debris. Positive detection of OGSR spectra were achieved by ATR FT-IR hyperspectral microscopy. Spectra show characteristic patterns of nitrate ester compounds which agrees with the propellant chemical composition. Characteristic ATR FT-IR spectral patterns of OGSR were measured from visualized GSR particles demonstrating the potential of ATR FT-IR hyperspectral microscopy.

Estimation of the age of human semen stains by attenuated total reflection Fourier transform infrared spectroscopy: a preliminary study

Semen stain is one of the most important biological evidence at sexual crime scenes. Age estimation of human semen stains plays an important role in forensic work, and it is rarely studied due to lack of well-established methods. In this study, the technique called attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) coupled with advanced chemometric methods was employed to determine the age of semen stains on three different substrates: glass slides, tissues and fabric made of regenerated cellulose fibres up to 6 d. Partial least squares regression (PLSR) was used in conjunction with spectral analysis for age estimation, and the results generated high R² values (cross-validation: 0.81, external validation: 0.74) but a narrow margin of error for root mean square error (RMSE) (RMSE of cross-validation: 0.77 d, RMSE of prediction: 1.02 d). Additionally, our results indicated the robustness of PLSR model was not weaken by the influence of different substrates in this study. Our results indicate that ATR-FTIR, combined with chemometric methods, shows great potential as a convenient and efficient tool for age estimation of semen stains. Moreover, the method could be applied to routine forensic investigations in the future.

"Raincoat for explosives": Surface chemistry approach to control wetting of nitrocellulose with nitroglycerin

HYPOTHESIS

Wicking and percolation of a liquid in porous media is, among other factors, strongly affected by wettability of the pores. Nitrocellulose (NC) fibrous matrix impregnated with nitroglycerin (NG) is a main component of propellant formulations. Over time, NG, being a wetting liquid, leaks out from the matrix causing fires and explosions resulting from the storage. Here, we propose a chemical functionalization approach aimed at preparation of lyophobic coatings of NC to control wetting and to inhibit leakage of NG.

EXPERIMENTAL

NC was functionalized by a two-step process using the reaction of 3-(triethoxysilyl-propyl) isocyanate followed by reactions with either alkyl-, fluoroalkyl-, or phenyl-silane producing NC surfaces ranging in lyophobicity. The wettability of the NC pellets was characterized using water, hexadecane, and NG as probe fluids.

FINDINGS

FTIR and chemical analysis supported the covalent attachment of organosilanes to NC via robust carbamate and siloxane bonds. SEM and DSC of the modified NC materials suggested that the bulk properties of NC were not affected by the reactions with silanes. The contact angles of the modified NC changed over a wide range demonstrating surfaces with "tunable" wetting. NC grafted with fluoroalkyl-groups showed the most NG-repelling properties (θ_Adv/θ_Rec ∼ 90°/50°): NG beaded up and did not penetrate these surfaces.