An Approach to Reduce the Sample Consumption for LIBS based Identification of Explosive Materials

Abstract

An experimental design based on spectral construction, which has potential to minimise the sample consumption, the number of laser shots and time required to collect the data from laser induced breakdown spectroscopy for identification of the explosive materials is reported in the study. This approach is an ideal solution in the field of hazardous material detection, where the availability of the sample can be a serious limiting factor. The experimental data recorded on a set of five high energy materials has been considered to test the performance of the proposed methodology. Multiple spectra are constructed by assuming a normal distribution at each wavelength of the spectrum, where random numbers are generated using the mean and standard deviations obtained from arbitrarily chosen five experimental spectra from each class. The newly generated spectra are called as synthetic spectra. The correct classification obtained from -K -nearest neighbour combined with principal component analysis and partial least square -discriminant analysis demonstrated very promising results. The correct classification rates differed by only 4 per cent -7 per cent as compared to conventional approach where experimental spectra alone are considered for the analysis. Further, when RDX is excluded, the obtained results are almost identical with conventional approach.

LIBS as a Spectral Sensor for Monitoring Metallic Molten Phase in Metallurgical Applications—A Review

“…The instrumentation consists of an excitation source (a high-energy pulsed laser), time-gated spectrometers, and optical arrangement for collecting and collimating radiation. Several laser devices such as nanosecond, picosecond and femtosecond lasers are capable of producing plasma with powers exceeding in the order of gigawatts per pulse [17,24,25]. However, for the molten phase application nanosecond lasers are suitable such as Nd-YAG, excimer laser, and many more.…”

Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra

“…22 Moreover, it is also utilized in different spectroscopies, such as hyperspectral image analysis, 23 vibrational spectroscopy, 24,25 molecular excitation spectroscopy, 26 and laser-induced breakdown spectroscopy. 27–29…”

“…22 Moreover, it is also utilized in different spectroscopies, such as hyperspectral image analysis, 23 vibrational spectroscopy, 24,25 molecular excitation spectroscopy, 26 and laser-induced breakdown spectroscopy. [27][28][29] Various deep learning (DL) approaches have also been recently explored via CARS spectroscopy to tackle the NRB removal problem. [30][31][32][33][34][35] Valensise et al have utilized a convolutional neural network (CNN) model to retrieve the imaginary part from the CARS spectral data.…”

Adversarial Data Augmentation and Transfer Net for Scrap Metal Identification Using Laser-Induced Breakdown Spectroscopy Measurement of Standard Reference Materials

“…Thus, it is impossible to experimentally obtain measurements corresponding to each variation. Therefore, augmentation of training data with artificial measurements has recently been proposed 21,22 to extend the training set using data augmentation methods, such as generative adversarial network (GAN). 23,24 An extended training dataset can exhaustively cover a wider variation and achieve better classification performance, 25 especially for in situ scrap metal measurements.…”