Machine Learning Techniques for Geochemical Analysis Using Laser-Induced Breakdown Spectroscopy.

Objectives: The objectives of the study is to evaluate the efficacy of QS neodymium-doped yttrium aluminum garnet (Nd:YAG) laser alone versus in combination with ultra-pulse carbon dioxide (CO2) laser along with QS Nd:YAG laser in patients of black tattoo. Material and Methods: Fifty patients having black tattoo were consecutively assigned into two groups, namely group A and group B. Group A was treated with QS Nd:YAG laser (1064 nm) alone, and Group B underwent combination of fixed dose of ultra-pulse CO2 followed by increasing energy dose QS Nd:YAG laser (1064 nm), at 6-week interval till complete clearance or for a maximum of 6 sittings. After each sitting, percentage of improvement was evaluated using Visual Analog Scale (VAS) based on the assessment of the patient, whereas tattoo ink lightening (TIL) was calculated by evaluating the response in serial digital photographs and grading system for TIL. Results: Combination laser (group B) had greater improvement in mean VAS score and mean TIL score in the past three noted visits as compared to 1st 1 - session (P = 0.0001). The number of sessions required was lesser in comparison to Kirby-Desai score. Conclusion: For both professional and amateur tattoos, combination of ultrapulse CO2 laser and QS Nd:YAG laser is more effective in comparison to QS Nd:YAG laser alone.

Melasma is a frequent and difficult to treat skin disorder. Results of laser therapy are inconsistent. To determine the safety and efficacy of low-fluence Q-switched neodymium-doped yttrium aluminum garnet (QS Nd:YAG) laser for melasma treatment and assess recurrence rates and histopathologic findings before and after treatment. Twenty patients were treated with 10 weekly sessions of low-fluence 1064-nm QS Nd:YAG laser at 1-week intervals. The modified Melasma Area and Severity Index (mMASI) score was evaluated at baseline; 1 week; and 1, 3, and 6 months after treatment. Epidermal melanin quantification was performed on 10 biopsy samples and compared before and after treatment. All patients showed improvement by mMASI scores, range (21%-75%) compared with that at baseline. No permanent side effects occurred. The recurrence rate was 81%. By histopathology, a slight, nonsignificant (p = .305) decrease in melanin deposition was seen in all layers of the epidermis 1 week after the laser treatments ended. The results confirm the safety and effectiveness of low-fluence QS Nd:YAG laser for treating melasma; however, the high recurrence suggests poor long-term results when the laser is used as a monotherapy.

In the present work a method was proposed for the direct determination of Ca, Fe, K, and Mg and qualitative analysis of anthropized and cabruca soil samples from different depths (0–20 and 20–40 cm) by laser-induced breakdown spectroscopy (LIBS). The LIBS instrumental parameters were evaluated using a central composite design with a central point. The variables evaluated were as follows: delay time in five levels (0.5, 0.7, 1.2, 1.7, and 1.9 μs) and fluence at five levels (1448, 1811, 2659, 3514, and 3820 J·cm–2). The best results obtained were with a delay time of 1.2 μs and fluence of 2659 J × cm–2. The proposed calibration model for Ca, Fe, K, and Mg obtained using LIBS data presented a good correlation with the reference values obtained by inductively coupled plasma atomic emission spectrometry (ICP OES). In addition, multivariate data analysis from LIBS spectra in the region of 186–1042 nm was performed using principal component analysis (PCA). From the LIBS spectra it was possible to discriminate the different analyzed samples, showing that the combination of LIBS with PCA is an excellent option for the discrimination of soils.

Chromium-doped zinc selenide (Cr:ZnSe) crystals are the gain media of choice for mid-infrared lasers operating over a 1.9-3.4 µm spectral range. In this study, we used laser-induced breakdown spectroscopy (LIBS) of Cr:ZnSe polycrystalline materials to improve the sensitivity of detecting chromium concentration in the laser-active materials. The fundamental harmonic of a Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser was used as an excitation source. After calibration of the LIBS signal, we calculated that chromium's limit of detection (LOD) was 30 parts per million (ppm). Normalization of the Cr(I) intensity peak at 357.9 nm by the square root of the Zn(I) peak at 636.2 nm reduced the LOD to 20 ppm and increased the coefficient of determination to R² ≈ 0.98. These results demonstrate the potential of LIBS for microscale mapping of dopant distributions in laser crystals and for on-site monitoring of material quality during fabrication.

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.

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) 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.

Actual state of affairs, main advantages and problems of Laser-Induced Breakdown Spectroscopy (LIBS) in analysis of industrial materials and environmental samples are discussed. Methods for LIBS sensitivity enhancement such as double-pulse ablation, a combination of LIBS with laser-induced fluorescence, the use of additional sources of excitation (spark) and confinement of plasma by magnetic field or shock wave are compared with respect to figures-of-merit. A set of LIBS approach to qualitative fast classification of materials, especially based on the correlation between parameters of laser plasma and sample properties, are discussed in details. Progress in environmental analysis of soils, sands etc. with the use of LIBS is demonstrated. Detection limits of the most elements in soils and aluminum alloys obtained until now are critically considered.

The burning of incense produces toxic and harmful gases and particulate matters, posing a tremendous threat to both human health and the atmospheric environment. As a consequence, online in situ detection, classification, and traceability of burnt incense are of vital importance. In this paper, taking ambergris, musk, and Tibetan incense as examples, laser-induced breakdown spectroscopy (LIBS) is applied to the online detection of smoke and ash from the burning of three common types of incenses. It is found that metallic elements such as K, Mg, and Ca are present in the smoke. In contrast, more complex metallic elements, such as Fe, Al, Mn, Sr, etc., are present in the incense ash. By comparing the smoke and ash spectra of three different incenses, the feature spectra with large differences are selected, and the data are dimensionality reduced using the principal component analysis. Combined with error back propagation training artificial neural networks, the classification and traceability models of the smoke and ash from different incenses are performed, and the final recognition accuracies are 93.24% and 96.33%, respectively. All the results indicate that the combination of LIBS and machine learning has good application prospects for detecting and online tracing different incense smoke and ash and is also beneficial for human health and the natural environment.

Classification of pork soft tissues, including skin, fat, loin, tenderloin and ham muscles, was achieved using combination of laser-induced breakdown spectroscopy, principal component analysis and k nearest neighbors classification.

Prospects of real-time single-particle biological aerosol analysis: A comparison between laser-induced breakdown spectroscopy and aerosol time-of-flight mass spectrometry

We have successfully demonstrated that although there are significant analytical challenges involved in the qualitative discrimination analysis of sub-mm sized (microfragment) glass samples, the task can be solved with very good accuracy and reliability with the multivariate chemometric evaluation of laser-induced breakdown spectroscopy (LIBS) data or in combination with pre-screening based on refractive index (RI) data. In total, 127 glass samples of four types (fused silica, flint, borosilicate and soda–lime) were involved in the tests. Four multivariate chemometric data evaluation methods (linear discrimination analysis, quadratic discrimination analysis, classification tree and random forest) for LIBS data were evaluated with and without data compression (principal component analysis). Classification tree and random forest methods were found to give the most consistent and most accurate results, with classifications/identifications correct in 92 to 99% of the cases for soda–lime glasses. The developed methods can be used in forensic analysis.

In this research, the utilization of near-infrared (NIR) spectroscopy in conjunction with advanced machine learning methods is investigated for categorizing technical lignins obtained from different biomass sources and industrial procedures. Technical lignins, such as kraft, organosolv and lignosulfonates, have different chemical compositions, which continue to make uniform characterization and application in sustainable sectors extremely difficult. Fast, universally accessible analytics combined with data analysis is still an open question. For the first time three distinct NIR spectrometers—a high-performance benchtop system, a mid-priced compact device, and an economical handheld unit—were utilized to record NIR spectra of 31 unique lignin samples. The spectra underwent pre-processing through standard normal variate (SNV) transformation and Savitzky-Golay derivatives to amplify spectral features and decrease noise. Principal component analysis (PCA) was employed to reduce data complexity and extract crucial characteristics for classification purposes. Subsequently, four machine learning algorithms—Support Vector Machines (SVM), Gaussian Naive Bayes (GNB), Gaussian Process Classification (GPC), and Decision Tree Classification (DTC)—were implemented for the classification of the lignin samples. The DTC model exhibited the highest accuracy among them across different spectrometers. Although the benchtop spectrometer produced the most precise outcomes, the compact NeoSpectra system also displayed potential as a cost-efficient option. Nonetheless, the restricted spectral coverage of the handheld NIRONE spectrometer resulted in reduced classification accuracy. Our discoveries highlight the capability of NIR spectroscopy, combined with robust data analysis techniques, for the swift and non-destructive classification of technical lignins, facilitating their improved utilization in sustainable fields. • ➢NIR spectroscopy combined with robust machine learning techniques as a promising approach for fast and non-destructive classification of technical lignins • For the first time a robust cost-effective method is presented that allows the reproducible use of lignin biomass in sustainable applications. • This reliable techniques should accelerate the substitution of mineral oil-based materials with biomass e.g. in the plastics manufacturing industry

The present paper deals with the detection and quantification of toxic heavy metals like Cd, Co, Pb, Zn, Cr, etc. in environmental samples by using the technique of laser-induced breakdown spectroscopy (LIBS) and calibration-free LIBS (CF-LIBS). A MATLABTM program has been developed based on the CF-LIBS algorithm given by earlier workers and concentrations of pollutants present in industrial area soil have been determined. LIBS spectra of a number of certified reference soil samples with varying concentrations of toxic elements (Cd, Zn) have been recorded to obtain calibration curves. The concentrations of Cd and Zn in soil samples from the Jajmau area, Kanpur (India) have been determined by using these calibration curves and also by the CF-LIBS approach. Our results clearly demonstrate that the combination of LIBS and CF-LIBS is very useful for the study of pollutants in the environment. Some of the results have also been found to be in good agreement with those of ICP-OES.