Rapid and On-Scene Chemical Identification of Intact Explosives with Portable Near-Infrared Spectroscopy and Multivariate Data Analysis

Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics

As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour.

Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review

The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.

Forensic analytical aspects of homemade explosives containing grocery powders and hydrogen peroxide

Homemade explosives become a significant challenge for forensic scientists and investigators. In addition to well-known materials such as acetone peroxide trimer, black powder, or lead azides, perpetrators often produce more exotic and less recognized Homemade Explosives (HMEs). Mixtures of hydrogen peroxide with liquid fuels are widely acknowledged as powerful explosives. Interestingly, similar explosive properties are found in mixtures of numerous solid materials with H2O2. Notably, powdered groceries, such as coffee, tea, grounded spices, and flour, are particularly interesting to pyrotechnics enthusiasts due to their easy production using accessible precursors, which do not attract the attention of security agencies. H2O2-based HMEs may become a dangerous component of improvised explosive devices for terrorists and ordinary offenders. For the four most powerful mixtures-HMEs based on coffee, tea, paprika, and turmeric-molecular markers useful for identification using the GC-MS technique have been proposed. Furthermore, the observed time-dependent changes in mixtures of H2O2 with these food products were studied and evaluated as a potential method for assessing the age of the evidence and reconstructing timelines of crimes. The paper also discusses the usefulness of FT-IR spectroscopy for identifying H2O2-based HMEs.

Fluorescent Photoelectric Detection of Peroxide Explosives Based on a Time Series Similarity Measurement Method

Due to the characteristics of peroxide explosives, which are difficult to detect via conventional detection methods and have high explosive power, a fluorescent photoelectric detection system based on fluorescence detection technology was designed in this study to achieve the high-sensitivity detection of trace peroxide explosives in practical applications. Through actual measurement experiments and numerical simulation methods, the derivative dynamic time warping (DDTW) algorithm and the Spearman correlation coefficient were used to calculate the DDTW-Spearman distance to achieve time series correlation measurements. The detection sensitivity of triacetone triperoxide (TATP) and H2O2 was studied, and the detection of organic substances of acetone, acetylene, ethanol, ethyl acetate, and petroleum ether was carried out. The stability and specific detection ability of the fluorescent photoelectric detection system were determined. The research results showed that the fluorescence photoelectric detection system can effectively identify the detection data of TATP, H2O2, acetone, acetonitrile, ethanol, ethyl acetate, and petroleum ether. The detection limit of 0.01 mg/mL of TATP and 0.0046 mg/mL of H2O2 was less than 10 ppb. The time series similarity measurement method improves the analytical capabilities of fluorescence photoelectric detection technology.