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

There is an ongoing forensic and security need for rapid, on-scene, easy-to-use, non-invasive chemical identification of intact energetic materials at pre-explosion crime scenes. Recent technological advances in instrument miniaturization, wireless transfer and cloud storage of digital data, and multivariate data analysis have created new and very promising options for the use of near-infrared (NIR) spectroscopy in forensic science. This study shows that in addition to drugs of abuse, portable NIR spectroscopy with multivariate data analysis also offers excellent opportunities to identify intact energetic materials and mixtures. NIR is able to characterize a broad range of chemicals of interest in forensic explosive investigations, covering both organic and inorganic compounds. NIR characterization of actual forensic casework samples convincingly shows that this technique can handle the chemical diversity encountered in forensic explosive investigations. The detailed chemical information contained in the 1350-2550 nm NIR reflectance spectrum allows for correct compound identification within a given class of energetic materials, including nitro-aromatics, nitro-amines, nitrate esters, and peroxides. In addition, the detailed characterization of mixtures of energetic materials, such as plastic formulations containing PETN (pentaerythritol tetranitrate) and RDX (trinitro triazinane), is feasible. The results presented illustrate that the NIR spectra of energetic compounds and mixtures are sufficiently selective to prevent false-positive results for a broad range of food-related products, household chemicals, raw materials used for the production of home-made explosives, drugs of abuse, and products that are sometimes used to create hoax improvised explosive devices. However, for frequently encountered pyrotechnic mixtures, such as black powder, flash powder, and smokeless powder, and some basic inorganic raw materials, the application of NIR spectroscopy remains challenging. Another challenge is presented by casework samples of contaminated, aged, and degraded energetic materials or poor-quality HMEs (home-made explosives), for which the spectral signature deviates significantly from the reference spectra, potentially leading to false-negative outcomes.

Portable near infrared spectroscopy for the isomeric differentiation of new psychoactive substances.

Rapid and efficient identification of the precise isomeric form of new psychoactive substances (NPS) by forensic casework laboratories is a relevant challenge in the forensic field. Differences in legal status occur for ring-isomeric species of the same class, thus leading to different penalties and judicial control. Portable systems such as near-infrared (NIR) spectroscopy recently emerged as suitable techniques for the on-scene identification of common drugs of abuse such as cocaine, MDMA and amphetamine. This way, the overall forensic process becomes more efficient as relevant information on substance identity becomes available directly at the scene of crime. Currently, no NIR-based applications exist for the rapid, on-scene detection of NPS isomers. Herein, we present the differentiation of cathinone and phenethylamine-type NPS analogues based on their NIR spectrum recorded in 2 seconds on a portable 1350 - 2600 nm spectrometer. A prior developed data analysis model was found suitable for the identification of the methylmethcathinone (MMC) isomers 2-MMC, 3-MMC and 4-MMC. In 51 mixtures and 22 seized casework samples, the correct isomeric form was detected in all cases except for a few mixtures with an active ingredient content of 10 wt%. These results show the feasibility of on-site NPS detection as presumptive test performed directly at the scene of crime with a small size NIR-spectrometer. Additionally, in the illicit drug analysis laboratory the combination of NIR and GC-MS analysis might be suitable for robust identification of NPS isomers and analogues.

On-site forensic analysis of colored seized materials: Detection of brown heroin and MDMA-tablets by a portable NIR spectrometer.

The increasing workload for forensic laboratories and the expanding complexity of the drug market necessitates efficient approaches to detect drugs of abuse. Identification directly at the scene of crime enables investigative forces to make rapid decisions. Additionally, on-site identification of the material also leads to considerable efficiency and cost benefits. As such, paperwork, transportation, and time-consuming analysis in a laboratory may be avoided. Near-infrared (NIR) spectroscopy is an analysis technique suitable for rapid drug testing using portable equipment. A possible limitation of spectroscopic analysis concerns the complexity of seized materials. NIR measurements represent composite spectra for mixtures and diagnostic spectral features can be obscured by excipients such as colorants. Herein, a NIR-based (1300-2600 nm) detection of heroin and MDMA in colored casework (i.e., brown powders and ecstasy tablets) using a portable analyzer is presented. The application includes a multistage data analysis model based on the net analyte signal (NAS) approach. This identification model was specifically designed for mixture analysis and requires a limited set of pure reference spectra only. Consequently, model calibration efforts are reduced to a minimum. A total of 549 forensic samples was tested comprising brown heroine samples and a variety of colored tablets with different active ingredients. This investigation led to a >99% true negative and >93% true positive rate for heroin and MDMA. These results show that accurate on-site detection in colored casework is possible using NIR spectroscopy combined with an efficient data analysis model. These findings may eventually help in the transition of routine forensic laboratories from laboratory-based techniques to portable equipment operated on scene.

A calibration friendly approach to identify drugs of abuse mixtures with a portable near-infrared analyzer.

Both the increasing number and diversity of illicit-drug seizures complicate forensic drug identification. Traditionally, colorimetric tests are performed on-site, followed by transport to a laboratory for confirmatory analysis. Higher caseloads increase laboratory workload and associated transport and chain-of-evidence assurance performed by police officers. Colorimetric tests are specific only for a small set of drugs. The rise of new psychoactive substances therefore introduces risks for erroneous results. Near-infrared (NIR)-based analyzers may overcome these encumbrances by their compound-specific spectral selectivity and broad applicability. This work introduces a portable NIR analyzer that combines a broad wavelength range (1300-2600 nm) with a chemometric model developed specifically for forensic samples. The application requires only a limited set of reference spectra for time-efficient model training. This calibration-light approach thus eliminates the need of extensive training sets including mixtures. Performance was demonstrated with 520 casework samples resulting in a 99.6% true negative and 97.6% true positive rate for cocaine. Similar results were obtained for MDMA, methamphetamine, ketamine, and heroin. Additionally, 236 samples were analyzed by scanning directly through their plastic packaging. Also here, a >97% true positive rate was obtained. This allows for non-invasive, operator-safe chemical identification of potentially potent drugs of abuse. Our results demonstrate the applicability for multiple drug-related substances. Ideally, the combination of this NIR approach with other portable techniques, such as Raman and IR spectroscopy and electrochemical tests, may eventually eliminate the need for subsequent laboratory analysis; therefore, saving tremendous resources in the overall forensic process of confirmatory illicit drug identification.

Characterisation of fluorinated copolymers using liquid chromatography coupled on-line to mass spectrometry, with automated data interpretation.

A perfluorinated co-polyether was characterised in terms of the number and type of functional end groups present on the molecule. The polymer was separated chromatographically according to the polarity of the polymer end groups and the separation was coupled on-line to an electrospray ionisation time-of-flight mass spectrometer. Negative-mode electrospray ionisation of the relatively non-polar polymer was achieved by post-column addition of a polar constituent to the mobile phase. LC-MS analysis of polydisperse analytes is a highly data intensive technique and manual interpretation of the resulting data can be extremely complicated, especially for the characterisation of copolymers or polymers with end-group distributions. In order to overcome this problem, an automated data-analysis program was developed that allows the user to quickly determine the probability of the presence of a certain molecular compound. The program evaluated data in terms of the possible combinations of monomeric units and end groups that could be combined to make up the mass values present in the mass spectra. Using the program, the polymer can be characterised according to its molar-mass, chemical-composition and functionality-type distributions. A graphical representation of the LC-MS analyses is presented to give a clear overview of the two-dimensional separation. The identification of various end groups on the polymer is also presented graphically, as (a) a histogram (frequency of matches versus time), (b) a two-dimensional plot (masses that match the particular end group combination versus LC retention time) and (c) a plot of average chemical composition versus LC retention time.

Near-infrared spectroscopic monitoring of a series of industrial batch processes using a bilinear grey model.

A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.

Batch process monitoring using on-line MIR spectroscopy.

Many high quality products are produced in a batch wise manner. One of the characteristics of a batch process is the recipe driven nature. By repeating the recipe in an identical manner a desired end-product is obtained. However, in spite of repeating the recipe in an identical manner, process differences occur. These differences can be caused by a change of feed stock supplier or impurities in the process. Because of this, differences might occur in the end-product quality or unsafe process situations arise. Therefore, the need to monitor an industrial batch process exists. An industrial process is usually monitored by process measurements such as pressures and temperatures. Nowadays, due to technical developments, spectroscopy is more and more used for process monitoring. Spectroscopic measurements have the advantage of giving a direct chemical insight in the process. Multivariate statistical process control (MSPC) is a statistical way of monitoring the behaviour of a process. Combining spectroscopic measurements with MSPC will notice process perturbations or process deviations from normal operating conditions in a very simple manner. In the following an application is given of batch process monitoring. It is shown how a calibration model is developed and used with the principles of MSPC. Statistical control charts are developed and used to detect batches with a process upset.