Fast and sensitive recognition of various explosive compounds using Raman spectroscopy and principal component analysis

Robust nanotube-based nanosensor designed for the detection of explosive molecules

The adequate determination and detection of explosive molecules is key to introducing improvements in areas related to safety, whose progress depends on an adequate and rapid determination of dangerous substances. To detect explosives down to the molecular level and accurately discriminate between different but somehow similar substances, it is necessary to design sensors that can differentiate them uniquely and efficiently. In this study, we present a new generation nanoscale sensor based on carbon nanotubes with an adapted nanopore shape that is capable of effectively discriminating between five types of explosive compounds (TATP, RDX, PENT, HMX and DNT). We show that the interaction of each compound with the walls of the nanotubes induces changes in transmission and current that allows clear differentiation of each type of molecule. Interestingly, the transport properties do not depend on the orientation of the molecules within the nanopore in most cases, making it a robust device with high reproducibility and stability. The results also show that these systems can lead to relatively high thermoelectric performances and, furthermore, the Seebeck coefficient can be used to discriminate between them.

Library-Based Raman Spectral Identification Using Multi-Input Hybrid ResNet

Raman spectroscopy is widely used for its exceptional identification capabilities in various fields. Traditional methods for target identification using Raman spectroscopy rely on signal correlation with moving windows, requiring data preprocessing that can significantly impact identification performance. In recent years, deep-learning approaches have been proposed to leverage data augmentation techniques, such as baseline and additive noise addition, in order to overcome data scarcity. However, these deep-learning methods are limited to the spectra encountered during training and struggle to handle unseen spectra. To address these limitations, we propose a multi-input hybrid deep-learning model trained with simulated spectral data. By employing simulated spectra, our method tackles the challenges of data scarcity and the handling of unseen spectra encountered in traditional and deep-learning methods. Experimental results demonstrate that our proposed method achieves outstanding identification performance and effectively handles spectra obtained from different Raman spectroscopy systems.

The Influence of Biostimulants Used in Sustainable Agriculture for Antifungal Protection on the Chemical Composition of Winter Wheat Grain

Field studies were conducted from 2016 to 2019 (south-eastern Poland; 49°58'40.6″ N 22°33'11.3″ E) with the aim to identify the chemical composition of winter wheat grain upon foliar application of biostimulants, of which PlanTonic BIO (containing nettle and willow extracts) showed antifungal activity. The main chemical compositions and their spatial distribution in wheat grain were characterized by Raman spectroscopy technique. It was established that applied biostimulants and hydro-thermal conditions changed the chemical composition of the grain during all the studied years. A similar chemical composition of the grain was achieved in plants treated with synthetic preparations, including both intensive and extensive variants. The second group, in terms of an increase in fatty acid content, consists of grains of plants treated with biostimulants PlanTonic BIO, PlanTonic BIO + Natural Crop and PlanTonic BIO + Biofol Plex. The future of using biostimulants in crop production, including those containing salicylic acid and nettle extracts, appears to be a promising alternative to synthetic crop protection products.

Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecule identification. However, profiling complex samples remains a challenge because SERS peaks are likely to overlap, confounding features when multiple analytes are present in a single sample. In addition, SERS often suffers from high variability in signal enhancement due to nonuniform SERS substrate. The machine learning classification techniques widely used for facial recognition are excellent tools to overcome the complexity of SERS data interpretation. Herein, we reported a sensor for classifying coffee beverages by integrating SERS, feature extractions, and machine learning classifiers. A versatile and low-cost SERS substrate, called nanopaper, was used to enhance Raman signals of dilute compounds in coffee beverages. Two classic multivariate analysis techniques, Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC), were used to extract the significant spectral features, and the performance of various machine learning classifiers was evaluated. The combination of DAPC with Support Vector Machine (SVM) or K-Nearest Neighbor (KNN) shows the best performance for classifying coffee beverages. This user-friendly and versatile sensor has the potential to be a practical quality-control tool for the food industry.

Research on the Comprehensive Evaluation Method for the Automatic Recognition of Raman Spectrum under Multidimensional Constraint

Raman spectrum contains abundant substance information with fingerprint characteristics. However, due to the huge variety of substances and their complex characteristic information, it is difficult to recognize the Raman spectrum accurately. Starting from dimensions like the Raman shift, the relative peak intensity, and the overall hit ratio of characteristic peaks, we extracted and recognized the characteristics in the Raman spectrum and analyzed these characteristics from local and global perspectives and then proposed a comprehensive evaluation method for the recognition of Raman spectrum on the basis of the data fusion of the recognition results under multidimensional constraint. Based on the common spectrum database of the normal Raman and surface-enhanced Raman of thousands of substances, we analyzed the performance of the evaluation method. It shows that even for the identification of spectra from instruments of low technical specifications, the automatic recognition rate of the sample can reach 98% and above, a great improvement compared with that of the common identification algorithms, which proves the effectiveness of the comprehensive evaluation method under multidimensional constraint.

Baseline correction using a deep-learning model combining ResNet and UNet

A baseline correction method based on deep-learning model is proposed, which combines ResNet and UNet. Compared with the traditional methods, this method has relatively excellent performance.

Chemometrics in forensic science: approaches and applications

Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field.

Adaptive Hit-Quality Index for Raman Spectrum Identification

The recognition capability of the identification system using Raman spectroscopy is increasing with the demands in the field. Among the various approaches that determine the identity of a target, signal correlation using a moving window is one of the most effective and intuitive methods. In this paper, we report a new correlation method that is robust to spectral intensity variations. Using the peak distribution of a given spectrum, this method adaptively determines meaningful spectral regions for the identification target. Three commercial Raman spectrometer and a 14 033 library were included in the study, which was used for a library-based chemical discrimination test and mixed material analysis experiments. According to the identification experimental results, the proposed method correctly identified all of the spectra and maintained a mean correlation score above 0.95 while maintaining the correlation score of nontarget materials as low as possible.

Raman spectroscopy for detection of ammonium nitrate as an explosive precursor used in improvised explosive devices

Raman spectroscopy was evaluated as a sensor for detection of ammonium nitrate (NH₄NO₃, AN), fuel oil (FO), AN-water solutions, and AN- and FO-soil mixtures deposited on materials such as glass, synthetic fabric, cardboard and electrical tape to simulate field conditions of explosives detection. AN is an inorganic oxidizing salt that is commonly used in fertilizers and mining explosives, however, due to its widespread accessibility, AN-based explosives are also utilized for the manufacture of improvised explosive devices (IED). Pure AN crystals were ground to powder size and deposited on several substrates for Raman analysis, whereas FO was analysed in a quartz cuvette. To simulate field conditions samples of powdered AN, AN-water solutions (0.1% to 10.0% AN w/w), AN-soil (50% to 90% AN w/w) and FO-soil (50% to 75% FO w/w) were prepared and deposited on the clutter materials. Raman spectra were acquired at integration times between 0.1 and 30 s, and 3 replicate Raman measurements were carried out for each sample. The spectral window observed ranged from 300 to 3800 cm^-1. Several characteristic Raman bands were found, namely, at 710 cm^-1 (NO₃^-) and 1040 cm^-1 (NO₃^-) for AN; 1440-1470 cm^-1 (CH) and 2800-3000 cm^-1 (CH) for FO; 3000-3500 cm^-1 (OH) for water; and 615 cm^-1 (CCl), 1254 cm^-1 (CH), 1400 cm^-1 (CH₂) and 1600 cm^-1 (aromatic ring) for polyvinyl chloride (PVC, electrical tape). The effect of the AN concentration and integration time on the total and net Raman intensities, relative standard deviation, signal-to-noise ratio and relative limit of detection was evaluated. The relative limit of detection of AN in water was 0.1% (1 mg/g), and absolute limit of detection was 1.0 μg. The optimum integration time (≈10 s) for the Raman sensor to capture the analyte signals was estimated based on the Raman figures of merit as a function of the integration time.

Chemical Classification of Explosives

This work comprehensively reviews some fundamental concepts about explosives and their two commonly used classifications based on either their velocity of detonation or their application. These classifications are highly useful in the military/legal field, but completely useless for the chemical determination of explosives. Because of this reason, a classification of explosives based on their chemical composition is comprehensively revised, discussed and updated. This classification seeks to merge those dispersed chemical classifications of explosives found in literature into a unique general classification, which might be useful for every researcher dealing with the analytical chemical identification of explosives. In the knowledge of the chemical composition of explosives, the most adequate analytical techniques to determine them are finally discussed.

Instantaneous trace detection of nitro-explosives and mixtures with nanotextured silicon decorated with Ag-Au alloy nanoparticles using the SERS technique

The development of recyclable surface enhanced Raman scattering (SERS) based sensors has been in huge demand for trace level explosives detection. A simple, hybrid Silicon (Si) nanotextured target-based SERS platform is fabricated through patterning micro square arrays (MSA) on Si using femtosecond (fs) laser ablation technique at different fluences. Using the hybrid target Si MSA substrate loaded/decorated with Ag-Au alloy NPs (obtained using femtosecond ablation in liquids) we demonstrate the trace level detection of organic nitro-explosives [picric acid (PA), 2,4-dinitrotoluene (DNT), and 1, 3, 5-trinitroperhydro-1, 3, 5-triazine (RDX)] and their mixtures. The microstructures/nanostructures of MSA fabricated at an input fluence of 9.55 J/cm², and decorated with Ag-Au alloy NPs, exhibited exceptional SERS enhancement factors (EFs) up to ∼10¹⁰ for MB, ∼10⁶ for PA, and ∼10⁴ for RDX with the detection limits obtained being ∼5 pM, ∼36 nM, and ∼400 nM for MB, PA and RDX respectively. Furthermore, we demonstrate these SERS substrates possess good reproducibility (RSD values < 15%) and a superior performance compared to a commercial Ag substrate (SERSitive, Poland). Three binary mixtures, i.e. MB-PA, MB-DNT, PA-DNT at different concentrations, were also investigated using the same SERS substrate to test the efficacy. Further, the SERS spectra of dyes, explosives, and complex mixtures were utilized for discrimination/classification using principal component analysis.

Deep learning-based component identification for the Raman spectra of mixtures

Raman spectroscopy is widely used as a fingerprint technique for molecular identification. However, Raman spectra contain molecular information from multiple components and interferences from noise and instrumentation. Thus, component identification using Raman spectra is still challenging, especially for mixtures. In this study, a novel approach entitled deep learning-based component identification (DeepCID) was proposed to solve this problem. Convolution neural network (CNN) models were established to predict the presence of components in mixtures. Comparative studies showed that DeepCID could learn spectral features and identify components in both simulated and real Raman spectral datasets of mixtures with higher accuracy and significantly lower false positive rates. In addition, DeepCID showed better sensitivity when compared with the logistic regression (LR) with L1-regularization, k-nearest neighbor (kNN), random forest (RF) and back propagation artificial neural network (BP-ANN) models for ternary mixture spectral datasets. In conclusion, DeepCID is a promising method for solving the component identification problem in the Raman spectra of mixtures.

Recent Developments in Spectroscopic Techniques for the Detection of Explosives

Trace detection of explosives has been an ongoing challenge for decades and has become one of several critical problems in defense science; public safety; and global counter-terrorism. As a result, there is a growing interest in employing a wide variety of approaches to detect trace explosive residues. Spectroscopy-based techniques play an irreplaceable role for the detection of energetic substances due to the advantages of rapid, automatic, and non-contact. The present work provides a comprehensive review of the advances made over the past few years in the fields of the applications of terahertz (THz) spectroscopy; laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy; and ion mobility spectrometry (IMS) for trace explosives detection. Furthermore, the advantages and limitations of various spectroscopy-based detection techniques are summarized. Finally, the future development for the detection of explosives is discussed.

The application of principal component analysis to drug discovery and biomedical data

There is a neat distinction between general purpose statistical techniques and quantitative models developed for specific problems. Principal Component Analysis (PCA) blurs this distinction: while being a general purpose statistical technique, it implies a peculiar style of reasoning. PCA is a 'hypothesis generating' tool creating a statistical mechanics frame for biological systems modeling without the need for strong a priori theoretical assumptions. This makes PCA of utmost importance for approaching drug discovery by a systemic perspective overcoming too narrow reductionist approaches.

Raman spectrum identification based on the correlation score using the weighted segmental hit quality index

In this paper, we consider a novel method for identification of Raman spectra recorded on different instruments with different wavelengths.

A facile strategy applied to simultaneous qualitative-detection on multiple components of mixture samples: a joint study of infrared spectroscopy and multi-label algorithms on PBX explosives

We combined infrared spectroscopy with multi-label algorithms to propose a facile yet efficient strategy to realize simultaneous qualitative-detection on multiple components of mixture explosives without pre-separation.

Baseline correction using asymmetrically reweighted penalized least squares smoothing

Baseline correction methods based on penalized least squares are successfully applied to various spectral analyses. The methods change the weights iteratively by estimating a baseline. If a signal is below a previously fitted baseline, large weight is given. On the other hand, no weight or small weight is given when a signal is above a fitted baseline as it could be assumed to be a part of the peak. As noise is distributed above the baseline as well as below the baseline, however, it is desirable to give the same or similar weights in either case. For the purpose, we propose a new weighting scheme based on the generalized logistic function. The proposed method estimates the noise level iteratively and adjusts the weights correspondingly. According to the experimental results with simulated spectra and measured Raman spectra, the proposed method outperforms the existing methods for baseline correction and peak height estimation.

Overcoming selectivity and sensitivity issues of direct inject electrospray mass spectrometry via DAPNe-NSI-MS

Direct inject electrospray mass spectrometry offers minimal sample preparation and a "shotgun" approach to analyzing samples. However, complex matrix effects often make direct inject an undesirable sample introduction technique, particularly for trace level analytes. Highlighted here is our solution to the pitfalls of direct inject mass spectrometry and other ambient ionization methods with a focus on trace explosives. Direct analyte-probed nanoextraction coupled to nanospray ionization mass spectrometry solves selectivity issues and reduces matrix effects while maintaining minimal sample preparation requirements. With appropriate solvent conditions, most explosive residues can be analyzed with this technique regardless of the nature of the substance (i.e., nitroaromatic, oxidizing salt, or peroxide).