Identification and classification of explosives using semi-supervised learning and laser-induced breakdown spectroscopy

Application of Laser-Induced Breakdown Spectroscopy Combined with Chemometrics for Identification of Penicillin Manufacturers

Due to the differences in raw materials and production processes, the quality of the same type of drug produced by different manufacturers is different. In drug supervision, determining the manufacturer can help to trace drug quality issues. In this study, a method for the quick identification of drug manufacturers based on laser-induced breakdown spectroscopy (LIBS) was proposed for the first time. We obtained the LIBS spectra from 12 samples of three types of penicillin (phenoxymethylpenicillin potassium tablets, amoxicillin capsules, and amoxicillin and clavulanate potassium tablets) produced by 10 manufacturers. The LIBS characteristic lines of the three types of penicillin were ranked by importance based on the decrease in the Gini index of random forest (RF). Three classifiers—the linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural network (ANN)—were used to identify the different manufacturers of the three types of penicillin. RF-ANN provided the best classification result and an accuracy of 100% in penicillin manufacturer identification. The results show that LIBS combined with chemometrics could be used in the identification of penicillin manufacturers, and this method has application potential in drug quality supervision.

Determination of detonation characteristics by laser-induced plasma spectra and micro-explosion dynamics

Determination of macroscale detonation parameters of energetic materials (EMs) in a safe and rapid way is highly desirable. However, traditional experimental methods suffer from tedious operation, safety hazards and high cost. Herein, we present a micro-scale approach for high-precision diagnosis of explosion parameters based on radiation spectra and dynamic analysis during the interaction between laser and EMs. The intrinsic natures of micro-explosion dynamics covering nanosecond to millisecond and chemical reactions in laser-induced plasma are revealed, which reveal a tight correlation between micro-detonation and macroscopic detonation based on laser-induced plasma spectra and dynamics combined with statistic ways. As hundreds to thousands of laser pulses ablate on seven typical tetrazole-based high-nitrogen compounds and ten single-compound explosives, macroscale detonation performance can be well estimated with a high-speed and high-accuracy way. Thereby, the detonation pressure and enthalpies of formation can be quantitatively determined by the laser ablation processes for the first time to our knowledge. These results enable us to diagnose the performance of EMs in macroscale domain from microscale domain with small-dose, low-cost and multiple parameters.

Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis

Limited by the lack of training spectral data in different kinds of tissues, the diagnostic accuracy of laser-induced breakdown spectroscopy (LIBS) is hard to reach the desired level with normal supervised learning identification methods. In this paper, we proposed to apply the predictive data clustering methods with supervised learning methods together to identify tissue information accurately. The meanshift clustering method is introduced to compare with three other clustering methods which have been used in LIBS field. We proposed the cluster precision (CP) score as a new criterion to work with Calinski-Harabasz (CH) score together for the evaluation of the clustering effect. The influences of principal component analysis (PCA) on all four kinds of clustering methods are also analyzed. PCA-meanshift shows the best clustering effect based on the comprehensive evaluation combined CH and CP scores. Based on the spatial location and feature similarity information provided by the predictive clustering, the PCA-Meanshift can improve diagnosis accuracy from less than 95% to 100% for all classifiers including support vector machine (SVM), k nearest neighbor (k-NN), soft independent modeling of class analogy (Simca) and random forests (RF) models.

A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network

Estimation of hazardous air pollutants in the urban environment for maintaining public safety is a significant concern to mankind. In this paper, we have developed an efficient air quality warning system based on a low-cost and robust ground-level ozone soft sensor. The soft sensor was developed based on a novel technique of damped least squares neural network (DLSNN) with greedy backward elimination (GBE) for the estimation of hazardous ground-level ozone. Only three meteorological factors were used as input variables in the estimation of ground-level ozone and we have used weighted k-nearest neighbors (WkNN) classifier with fast response for development of air quality warning system. We have chosen the urban areas of Taiwan for this study and have analyzed seasonal variations in the ground-level ozone concentration of various cities in Taiwan as part of this work. Moreover, descriptive statistics and linear dependence of ozone concentration based on Spearman correlation coefficient, Kendall's tau coefficient, and Pearson coefficient are calculated. The proposed DLSNN/GBE method exhibited excellent performance resulting in very low mean square error (MSE), mean absolute error (MAE), and high coefficient of determination (R²) compared to other traditional approaches in ozone concentration estimation. We have achieved a good fit in the determination of ozone concentration from meteorological features of atmosphere. Moreover, the excellent performance of proposed urban air quality warning system was evident from the good F1-score value of 0.952 achieved by the WkNN classifier.

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.

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.

Remote Detection of Uranium Using Self-Focusing Intense Femtosecond Laser Pulses

Optical measurement techniques can address certain important challenges associated with nuclear safety and security. Detection of uranium over long distances presents one such challenge that is difficult to realize with traditional ionizing radiation detection, but may benefit from the use of techniques based on intense femtosecond laser pulses. When a high-power laser pulse propagates in air, it experiences collapse and confinement into filaments over an extended distance even without external focusing. In our experiments, we varied the initial pulse chirp to optimize the emission signal from the laser-produced uranium plasma at an extended distance. While the ablation efficiency of filaments formed by self-focusing is known to be significantly lower when compared to filaments produced by external focusing, we show that filaments formed by self-focusing can still generate luminous spectroscopic signatures of uranium detectable within seconds over a 10-m range. The intensity of uranium emission varies periodically with laser chirp, which is attributed to the interplay among self-focusing, defocusing, and multi-filament fragmentation along the beam propagation axis. Grouping of multi-filaments incident on target is found to be correlated with the uranium emission intensity. The results show promise towards long-range detection, advancing the diagnostics and analytical capabilities in ultrafast laser-based spectroscopy of high-Z elements.

Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores' selection in the metallurgical industry.

Quantitative Analysis of Major Metals in Agricultural Biochar Using Laser-Induced Breakdown Spectroscopy with an Adaboost Artificial Neural Network Algorithm

To promote the green development of agriculture by returning biochar to farmland, it is of great significance to simultaneously detect heavy and nutritional metals in agricultural biochar. This work aimed first to apply laser-induced breakdown spectroscopy (LIBS) for the determination of heavy (Pb, Cr) and nutritional (K, Na, Ca, Mg, Cu, and Zn) metals in agricultural biochar. Each batch of collected biochar was prepared to a standardized sample using the separating and milling method. Two types of univariate analysis model were developed using peak intensity and integration area of the sensitive emission lines, but the performance did not satisfy the requirements of practical application because of the poor correlations between the measured values and predicted values, as well as large relative standard deviation of the prediction (RSDP) values. An ensemble learning algorithm, adaboost backpropagation artificial neural network (BP-Adaboost), was then used to develop the multivariate analysis models, which had a more robust performance than traditional univariate analysis, partial least squares regression (PLSR), and backpropagation artificial neural network (BP-ANN). The optimized RSDP values for K, Ca, Mg, and Cu were less than 10%, while the RSDP values for Pb, Cr, Zn, and Na were in the range of 10–20%. Moreover, the pairwise t-test of its prediction set showed that there was no significant difference between the measurements of LIBS and ICP-MS. The promising results indicate that rapid and simultaneous detection of major heavy and nutritional metals in agricultural biochar can be achieved using LIBS and reasonable chemometric algorithms.