Introducing Laser Induced Breakdown Spectroscopy (LIBS) as a Novel, Cheap and Non‐destructive Method to Study the Changes of Mechanical Properties of Plastic Bonded Explosives (PBX)

LIBS spectroscopy and random forest: A new approach to explore brightness analysis and the source discrimination of white porcelains excavated from Xi'an, Shaanxi province (Tang dynasty, 618-907 CE).

The enhancement of rice quality stands as a pivotal focus in crop breeding research, with spectral analysis-based non-destructive quality assessment emerging as a widely adopted tool in agriculture. A prevalent trend in this field prioritizes the assessment of effectiveness of individual spectral technologies while overlooking the influence of sample type on spectral quality testing outcomes. Thus, the present study employed Microscopic Hyperspectral Imaging, Raman, and Laser-Induced Breakdown Spectroscopy (LIBS) to acquire spectral data from paddy rice, brown rice, polished rice, and rice flour. The data were then modeled and analyzed with respect to the amylopectin and protein contents of the rice samples via regression methods. Correlation analysis revealed varying degrees of correlation, both positive and negative, among the three spectral techniques and the analytes of interest. LIBS and Raman spectroscopy demonstrated stronger correlations with the analytes compared to microscopic hyperspectral imaging. Based on the selected correlation variables, feature screening and regression modeling were conducted. The modeling results indicated that microscopic hyperspectral data modeling yielded the lowest coefficient of determination of R² = 0.2, followed by Raman data modeling result was higher than it, which was about 0.5. The modeling effect of polished rice is the best. LIBS data modeling performed best, with a coefficient of determination of 0.6. The influence of different sample types on the modeling results was less than that of Raman spectroscopy, and modeling results of grains were better. The feature matching analysis of Raman and libs spectroscopy techniques showed that there were spectral variables that could match amylopectin and protein in the features obtained by multiple modeling statistics, but some modeling variables failed to match. LIBS matched more variables than Raman. These findings provide valuable insights into the application effectiveness of different spectral techniques in detecting rice contents across diverse sample types.

Laser-induced optical breakdown spectroscopy of polymer materials based on evaluation of molecular emission bands

The aluminized plastic-bonded explosive (PBX) is a composite material in which solid explosive particles are dispersed in a polymer matrix, which includes three major components, i.e. polymeric binder, metal fuel (aluminum) and nitramine explosive. This work introduces a new method on the basis of the laser-induced breakdown spectroscopy (LIBS) technique in air and argon atmospheres to investigate the determination of aluminum content and detonation performance of aluminized PBXs. Plasma emissions of aluminized PBXs are recorded where atomic lines of Al, C and H as well as molecular bands of AlO and CN are identified. The experimental results demonstrate that a good discrimination and separation between the aluminized PBXs is possible using LIBS and principle component analysis, although they have similar atomic composition. Relative intensity of the AlO/Al is used to determine aluminum percentage of the aluminized PBXs. The obtained quantitative calibration curve using the relative intensity of the AlO/Al is better than the resulting calibration curve using only the intensity of Al. By using the LIBS method and the measured intensity ratio of CN/C, an Al content of 15% is found to be the optimum value in terms of velocity of detonation of the RDX/Al/HTPB standard samples.

Laser-induced breakdown spectroscopy (LIBS) technique was utilized to investigate the green mineral fluorite (CaF2) samples. The LIBS experiment was implemented by using Nd: YAG Laser system and a linear array CCD detector coupled with an Avantes spectrometer in an air atmosphere. The emission spectra of ten various fluorite samples were studied. The molecular bands correspond to the green B2Σ+-X2Σ+ and orange A2Π-X2Σ systems of diatomic CaF-molecule, specifically at Δν = -1, 0, +1 sequences and the CaO diatomic molecule between 618 nm and 630 nm were detected. It was observed that the CaF-diatomic molecular emission bands exhibited stronger optical emission compared to the fluorine (F) atomic lines. Chemical composition (wt.%) analysis was acquired using CF-LIBS methodology under optically thin and local thermodynamic equilibrium (LTE) conditions. Our CF-LIBS studies revealed that Ca (50–60%) and F (30–40%) were the major elements, while C (3–5%) and O (2–5%) were minor elements present in all samples. For comparison, we employed energy-dispersive X-ray (EDX) technique to study the essential elemental composition (wt.%). The results achieved from CF-LIBS were found to be in good agreement to that obtained from the EDX technique. To validate CF-LIBS results, the calibration curve method was employed using the validated samples (S3, S5, and S7). Furthermore, we utilized a principal component analysis (PCA) technique on the LIBS spectral data to classify the fluorite samples.

Laser-induced breakdown spectroscopy (LIBS) and principal component analysis (PCA) were applied to the classification of LIBS spectra from gold ores prepared as pressed pellets from pulverized bulk samples. For each sample, 5000 single-shot LIBS spectra were obtained. Although the gold concentrations in the samples were as high as 7.7 µg/g, Au emission lines were not observed in most single-shot LIBS spectra, rendering the application of the usual ensemble-averaging approach for spectral processing to be infeasible. Instead, a PCA approach was utilized to analyze the collection of single-shot LIBS spectra. Two spectral ranges of 21 nm and 0.15 nm wide were considered, and LIBS variables (i.e., wavelengths) reduced to no more than three principal components. Single-shot spectra containing Au emission lines (positive spectra) were discriminated by PCA from those without the spectral feature (negative spectra) in a spectral range of less than 1 nm wide around the Au(I) 267.59 nm emission line. Assuming a discrete gold distribution at very low concentration, LIBS sampling of gold particles seemed unlikely; therefore, positive spectra were considered as data outliers. Detection of data outliers was possible using two PCA statistical parameters, i.e., sample residual and Mahalanobis distance. Results from such a classification were compared with a standard database created with positive spectra identified with a filtering algorithm that rejected spectra with an Au intensity below the smallest detectable analytical LIBS signal (i.e., below the LIBS limit of detection). The PCA approach successfully identified 100% of the data outliers when compared with the standard database. False identifications in the multivariate approach were attributed to variations in shot-to-shot intensity and the presence of interfering emission lines.

Laser induced breakdown spectroscopy (LIBS) is an atomic emission based spectroscopy that uses a laser pulse as the source of excitation. The laser is focused to form hot plasma, which atomizes and excites the sample. In the LIBS spectrum each “feature” is the amplitude or intensity detected at different wavelengths in the range of 200–1000 nm. Pattern recognition techniques were applied on samples with similar elemental composition resulting in almost similar LIBS spectra which are visually very difficult to differentiate. It was observed that the classification results obtained from different classifiers were sensitive to data preprocessing. The outlier detection and removal techniques PCA, Dendrogram using Agglomerative Algorithm, Editing by Nearest Neighbour (NN) and Distance Matrix approaches were used in preprocessing step. After removing outlier(s) the resulting training patterns were used to model the k-Nearest Neighbour (k-NN), Principal Component Analysis (PCA), Dendrogram, Multiclass Support Vector Machine (SVM) and Decision Tree classifiers. In k-NN after removing outlier(s) the average classification accuracy was increased by 2% for high energy materials (HEM), but no improvement in non high energy materials (Non HEM) or in top level classification (decide either HEM or Non HEM). But, for other classifiers the classification accuracy gets reduced. Finally instead of removing outlier(s) dimensionality reduction by thresholding was applied and the classification accuracy increased by 4% in k-NN for HEM and 38% in multiclass SVM for HEM and 4% for Non-HEM.

The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.

In the present study, the feasibility of Laser-Induced Breakdown Spectroscopy (LIBS) was investigated to analyze the main constituent chemical elements of 29 edible salt samples from Germany, Australia, Bolivia, Brazil, Cyprus, United States of America, France, the Himalayas, Iran and Pakistan. Such salts comprise iodized refined, iodized marine, ground iodized marine, hyposodic iodized marine, unrefined gourmet, Himalayan gourmet, yolk salt and food additive. The LIBS technique identified and evaluated the elements Al, Ca, Sr, Fe, Mg, Mn, K and Na. The data set obtained by LIBS was used in the developed multivariate classification model, principal components analysis (PCA). In the PCA 4 principal components (PC) were defined, which explain 83.77% of the spectral information matrix that were used to discriminate the origin and identify profiles of edible salts in national and imported salts and hyposodium salts. LIBS then proves to be a tool for origin indication analysis or origin determination, being capable of identifying possible tampering. Therefore, LIBS classification analyzes are very promising and represent a simplification in the handling and preparation of samples for analysis.

The elemental analysis of soil is useful in forensic and environmental sciences. Methods were developed and optimized for two laser-based multi-element analysis techniques: laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser-induced breakdown spectroscopy (LIBS). This work represents the first use of a 266 nm laser for forensic soil analysis by LIBS. Sample preparation methods were developed and optimized for a variety of sample types, including pellets for large bulk soil specimens (470 mg) and sediment-laden filters (47 mg), and tape-mounting for small transfer evidence specimens (10 mg). Analytical performance for sediment filter pellets and tape-mounted soils was similar to that achieved with bulk pellets. An inter-laboratory comparison exercise was designed to evaluate the performance of the LA-ICP-MS and LIBS methods, as well as for micro X-ray fluorescence (μXRF), across multiple laboratories. Limits of detection (LODs) were 0.01-23 ppm for LA-ICP-MS, 0.25-574 ppm for LIBS, 16-4400 ppm for µXRF, and well below the levels normally seen in soils. Good intra-laboratory precision (≤ 6 % relative standard deviation (RSD) for LA-ICP-MS; ≤ 8 % for µXRF; ≤ 17 % for LIBS) and inter-laboratory precision (≤ 19 % for LA-ICP-MS; ≤ 25 % for µXRF) were achieved for most elements, which is encouraging for a first inter-laboratory exercise. While LIBS generally has higher LODs and RSDs than LA-ICP-MS, both were capable of generating good quality multi-element data sufficient for discrimination purposes. Multivariate methods using principal components analysis (PCA) and linear discriminant analysis (LDA) were developed for discriminations of soils from different sources. Specimens from different sites that were indistinguishable by color alone were discriminated by elemental analysis. Correct classification rates of 94.5 % or better were achieved in a simulated forensic discrimination of three similar sites for both LIBS and LA-ICP-MS. Results for tape-mounted specimens were nearly identical to those achieved with pellets. Methods were tested on soils from USA, Canada and Tanzania. Within-site heterogeneity was site-specific. Elemental differences were greatest for specimens separated by large distances, even within the same lithology. Elemental profiles can be used to discriminate soils from different locations and narrow down locations even when mineralogy is similar.

LIBS Protocol for the Assessment of Depth Profile, Homogeneity, and Quantification of Fe/Co - based Bilayer Ribbon

Laser-Induced Breakdown Spectroscopy (LIBS) combined with Artificial Intelligence (AI) offers a powerful method for analyzing and comparing spectral data. This study presents a comparative analysis of conventional and AI-developed methods for processing and interpreting LIBS data, especially in forensic applications, focusing on toner sample discrimination. We propose a novel AI-developed approach that combines normalization, interpolation, and peak detection techniques to simplify LIBS spectral analysis without user preprocessing and easily identify unique spectral features. This method was compared with conventional principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), which are commonly used for LIBS data analysis. The AI-developed method demonstrated superior performance in discriminating between toner samples from various brands and models of printers and photocopiers. The quantitative evaluation of the performance of the AI-developed approach was performed using statistical analysis, including accuracy difference percentage, component-wise variance analysis, paired t-test, and cross-validation test. The results confirmed a significant improvement in accuracy with the AI-developed method compared to conventional approaches. This proposed work highlights the potential of AI in enhancing spectroscopic analysis for forensic applications, offering increased efficiency and accuracy in sample discrimination and classification. Additionally, it accelerates the analysis of LIBS data with no need for user preprocessing.

Systematic comparison of unsupervised feature extraction methods for enhancing discrimination accuracy of brown rice flour adulteration in LIBS

Precision agriculture aims to increase yield and profits while reducing costs, waste, and environmental side-effects. This is achieved through a process of measuring, modelling and acting; for example, laser-induced breakdown spectroscopy (LIBS) can be used to measure macro and micro nutrients in crops to determine nutrient requirements. The limiting factor with quantitative LIBS analysis of plant nutrient levels is the variation between shots on the same sample. Following a review of current literature relevant to LIBS for agriculture, this work investigates whether different chemometric methods can mitigate these variations and can create quantitative calibrations for nutrient levels in fresh and dried pelletised pasture under laboratory conditions. The methods explored were Savitzky Golay filtering, multiple linear regression, principal component regression, partial least-squares regression, gaussian process regression, and artificial neural networks. The algorithms that performed best were partial least-squares with gaussian process regression (R2 of 0.93, 0.95, and 0.92 for K, Na, and Mn, respectively), principal components analysis with artificial neural networks (R2 of 0.94, 0.83, and 0.80 for Fe, Ca, and Mg, respectively), and partial least-squares with artificial neural networks (R2 of 0.77 for B). Removing the moisture from the pasture improved model R2 values by 4–5% on average. Acquiring spectra under an argon purge produced a small reduction in accuracy for some nutrients compared to models acquired in air. Including categorical data in the principal component regression and the artificial neural networks produced negligible improvements in prediction. This chapter will give an introduction of using different types of chemometric analyses on spectra generated by LIBS to measure micro and macro nutrients in pasture under laboratory conditions. It discusses the challenges faced when building models for each nutrient.

A method for the quantitative elemental analysis of surface soil samples using laser-induced breakdown spectroscopy (LIBS) was developed and applied to the analysis of bulk soil samples for discrimination between specimens. The use of a 266 nm laser for LIBS analysis is reported for the first time in forensic soil analysis. Optimization of the LIBS method is discussed, and the results compared favorably to a laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) method previously developed. Precision for both methods was <10% for most elements. LIBS limits of detection were <33 ppm and bias <40% for most elements. In a proof of principle study, the LIBS method successfully discriminated samples from two different sites in Dade County, FL. Analysis of variance, Tukey's post hoc test and Student's t test resulted in 100% discrimination with no type I or type II errors. Principal components analysis (PCA) resulted in clear groupings of the two sites. A correct classification rate of 99.4% was obtained with linear discriminant analysis using leave-one-out validation. Similar results were obtained when the same samples were analyzed by LA-ICP-MS, showing that LIBS can provide similar information to LA-ICP-MS. In a forensic sampling/spatial heterogeneity study, the variation between sites, between sub-plots, between samples and within samples was examined on three similar Dade sites. The closer the sampling locations, the closer the grouping on a PCA plot and the higher the misclassification rate. These results underscore the importance of careful sampling for geographic site characterization.