Extraction of information from laser-induced breakdown spectroscopy spectral data by multivariate analysis

Visualization of laser-induced breakdown spectroscopy data of mouse organs based on the feature extraction method

At present, there is no comprehensive and systematic research on laser-induced breakdown spectroscopy (LIBS) data visualization. In particular, the LIBS spectra of biological samples have large noise and weak signals, which seriously affect the feature visualization. Here, three commonly used sample visualization methods were compared, and a new method was applied for tissue sample visualization. We used the LIBS mapping technique to obtain LIBS spectra of different organ slice samples from mice. LIBS spectral distribution was visualized after extracting the region of interest. The three spectral visualization methods are compared, and the performance of visualization algorithms is quantitatively analyzed. The potential of heat-diffusion for the affinity-based transition embedding (PHATE) method highlights the details of the LIBS spectral distribution while maintaining the overall structure of the data. The correlation coefficient between dimensionality reduction data and raw data is 0.97, and the average distance between samples of different categories is 0.64, which are superior to those of traditional principal component analysis (PCA), multidimensional scaling (MDS), and t-distributed stochastic neighbor embedding (t-SNE). The results show that the PHATE method can serve as a very promising LIBS spectral visualization tool.

Visualization and accuracy improvement of soil classification using laser-induced breakdown spectroscopy with deep learning

Deep learning method is applied to spectral detection due to the advantage of not needing feature engineering. In this work, the deep neural network (DNN) model is designed to perform data mining on the laser-induced breakdown spectroscopy (LIBS) spectra of the ore. The potential of heat diffusion for an affinity-based transition embedding model is first used to perform nonlinear mapping of fully connected layer data in the DNN model. Compared with traditional methods, the DNN model has the highest recognition accuracy rate (75.92%). A training set update method based on DNN output is proposed, and the final model has a recognition accuracy of 85.54%. The method of training set update proposed in this work can not only obtain the sample labels quickly but also improve the accuracy of deep learning models. The results demonstrate that LIBS combined with the DNN model is a valuable tool for ore classification at a high accuracy rate.

Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]

Analytical spectroscopy methods have shown many possible uses for nuclear material diagnostics and measurements in recent studies. In particular, the application potential for various atomic spectroscopy techniques is uniquely diverse and generates interest across a wide range of nuclear science areas. Over the last decade, techniques such as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray fluorescence spectroscopy have yielded considerable improvements in the diagnostic analysis of nuclear materials, especially with machine learning implementations. These techniques have been applied for analytical solutions to problems concerning nuclear forensics, nuclear fuel manufacturing, nuclear fuel quality control, and general diagnostic analysis of nuclear materials. The data yielded from atomic spectroscopy methods provide innovative solutions to problems surrounding the characterization of nuclear materials, particularly for compounds with complex chemistry. Implementing these optical spectroscopy techniques can provide comprehensive new insights into the chemical analysis of nuclear materials. In particular, recent advances coupling machine learning methods to the processing of atomic emission spectra have yielded novel, robust solutions for nuclear material characterization. This review paper will provide a summation of several of these recent advances and will discuss key experimental studies that have advanced the use of analytical atomic spectroscopy techniques as active tools for nuclear diagnostic measurements.

Quantitative Compositional Analyses of Calcareous Rocks for Lime Industry Using LIBS

Here, the potential of laser-induced breakdown spectroscopy (LIBS) in grading calcareous rocks for the lime industry was investigated. In particular, we developed a system equipped with non-intensified detectors operating in scanning mode, defined a suitable data acquisition protocol, and implemented quantitative data processing using both partial least squares regression (PLS-R) and a multilayer perceptron (MLP) neural network. Tests were carried out on 32 samples collected in various limestone quarries, which were preliminarily analyzed using traditional laboratory X-ray fluorescence (XRF); then, they were divided into two groups for calibration and validation. Particular attention was dedicated to the development of LIBS methodology providing a reliable basis for precise material grading. The congruence of the results achieved demonstrates the capability of the present approach to precisely quantify major and minor geochemical components of calcareous rocks, thus disclosing a concrete application perspective within the lime industry production chain.

Elemental analysis of fish feed by laser-induced breakdown spectroscopy

In this study, the potential of laser-induced breakdown spectroscopy (LIBS) as an efficient multi-elemental quantification tool for fish feed is determined. A particular focus of this paper is total chromium, an essential element that has the potential to be toxic and carcinogenic. In total six elements, four macro-elements (Ca, Fe, K and Mg) and two micro-elements (Cr and Rb), were modelled using LIBS spectra of aquafeed samples. Reference analysis was conducted via inductively coupled plasma mass spectrometry (ICP-MS) and showed good agreement with LIBS predictions. These results provide evidence that LIBS has the potential to be utilized in the field as a real-time screening tool for establishing the elemental composition of a range of fish feeds.

Classification accuracy improvement of laser-induced breakdown spectroscopy based on histogram of oriented gradients features of spectral images

To improve the classification accuracy of laser-induced breakdown spectroscopy (LIBS), image histogram of oriented gradients (HOG) features method (IHFM) for materials analysis was proposed in this work. 24 rice (Oryza sativa L.) samples were carried out to verify the proposed method. The results showed that the classification accuracy of rice samples by the full-spectra intensities method (FSIM) and IHFM were 60.25% and 81.00% respectively. The classification accuracy was obviously improved by 20.75%. Universality test results showed that this method also achieved good results in the plastics, steel, rock and minerals classification. This study provides an effective method to improve the classification performance of LIBS.

Rapid Analysis of Inorganic Species in Herbaceous Materials Using Laser-Induced Breakdown Spectroscopy

Inorganic compounds in biomass, often referred to as ash, are known to be problematic in the thermochemical conversion of biomass to bio-oil or syngas and, ultimately, hydrocarbon fuels because they negatively influence reaction pathways, contribute to fouling and corrosion, poison catalysts, and impact waste streams. The most common ash-analysis methods, such as inductively coupled plasma-optical emission spectrometry/mass spectrometry (ICP-OES/MS), require considerable time and expensive reagents. Laser-induced breakdown spectroscopy (LIBS) is emerging as a technique for rapid analysis of the inorganic constituents in a wide range of biomass materials. This study compares analytical results using LIBS data to results obtained from three separate ICP-OES/MS methods for 12 samples, including six standard reference materials. Analyzed elements include aluminum, calcium, iron, magnesium, manganese, phosphorus, potassium, sodium, and silicon, and results show that concentrations can be measured with an uncertainty of approximately 100 parts per million using univariate calibration models and relatively few calibration samples. These results indicate that the accuracy of LIBS is comparable to that of ICP-OES methods and indicate that some acid-digestion methods for ICP-OES may not be reliable for Na and Al. These results also demonstrate that germanium can be used as an internal standard to improve the reliability and accuracy of measuring many elements of interest, and that LIBS can be used for rapid determination of total ash in biomass samples. Key benefits of LIBS include little sample preparation, no reagent consumption, and the generation of meaningful analytical data instantaneously.

Automatic classification of laser-induced breakdown spectroscopy (LIBS) data of protein biomarker solutions

We perform multi-class classification of laser-induced breakdown spectroscopy data of four commercial samples of proteins diluted in phosphate-buffered saline solution at different concentrations: bovine serum albumin, osteopontin, leptin, and insulin-like growth factor II. We achieve this by using principal component analysis as a method for dimensionality reduction. In addition, we apply several different classification algorithms (K-nearest neighbor, classification and regression trees, neural networks, support vector machines, adaptive local hyperplane, and linear discriminant classifiers) to perform multi-class classification. We achieve classification accuracies above 98% by using the linear classifier with 21-31 principal components. We obtain the best detection performance for neural networks, support vector machines, and adaptive local hyperplanes for a range of the number of principal components with no significant differences in performance except for that of the linear classifier. With the optimal number of principal components, a simplistic K-nearest classifier still provided acceptable results. Our proposed approach demonstrates that highly accurate automatic classification of complex protein samples from laser-induced breakdown spectroscopy data can be successfully achieved using principal component analysis with a sufficiently large number of extracted features, followed by a wrapper technique to determine the optimal number of principal components.

Quantitative measurements of loss on ignition in iron ore using laser-induced breakdown spectroscopy and partial least squares regression analysis

Laser-induced breakdown spectroscopy (LIBS) and partial least squares regression (PLSR) have been applied to perform quantitative measurements of a multiple-species parameter known as loss on ignition (LOI), in a combined set of run-of-mine (ROM) iron ore samples originating from five different iron ore deposits. Global calibration models based on 65 samples and their duplicates from all the deposits with LOI ranging from 0.5 to 10 wt% are shown to be successful for prediction of LOI content in pressed pellets as well as bulk ore samples. A global independent dataset comprising a further 60 samples was used to validate the model resulting in the best validation R(2) of 0.87 and root mean square error of prediction (RMSEP) of 1.1 wt% for bulk samples. A validation R(2) of 0.90 and an RMSEP of 1.0 wt% were demonstrated for pressed pellets. Data preprocessing is shown to improve the quality of the analysis. Spectra normalization options, automatic outlier removal and automatic continuum background correction, which were used to improve the performance of the PLSR method, are discussed in detail.

Laser-induced breakdown spectroscopy and chemometrics: a novel potential method to analyze wheat grains

Laser-induced breakdown spectroscopy (LIBS) has been widely used to evaluate the elemental composition (e.g., minerals or metal accumulation) on vegetal tissues. The main objective of this work was to differentiate wheat outer tissues during the grain ablation using LIBS and univariate/multivariate analysis. A high resolution spectrometer and a Nd:YAG laser (532 nm, 5 ns) was first used in order to easily identify atomic wheat emission lines. Then a pulsed excimer laser ArF (193 nm, 15 ns) and a compact fiber optic spectrometer was used to acquire LIBS spectral data from each pulse. Univariate and multivariate analyses (MW2D, PLS-DA) were carried out to provide more in depth information from the LIBS experiment. The number of pulses needed to ablate wheat tissues was successfully predicted by the supervised pattern recognition procedure. LIBS used in conjunction with multivariate analysis could be an interesting technique for rapid structural analysis of vegetal material.

Laser-induced breakdown spectroscopy for detection of explosives residues: a review of recent advances, challenges, and future prospects

In this review we discuss the application of laser-induced breakdown spectroscopy (LIBS) to the problem of detection of residues of explosives. Research in this area presented in open literature is reviewed. Both laboratory and field-tested standoff LIBS instruments have been used to detect explosive materials. Recent advances in instrumentation and data analysis techniques are discussed, including the use of double-pulse LIBS to reduce air entrainment in the analytical plasma and the application of advanced chemometric techniques such as partial least-squares discriminant analysis to discriminate between residues of explosives and non-explosives on various surfaces. A number of challenges associated with detection of explosives residues using LIBS have been identified, along with their possible solutions. Several groups have investigated methods for improving the sensitivity and selectivity of LIBS for detection of explosives, including the use of femtosecond-pulse lasers, supplemental enhancement of the laser-induced plasma emission, and complementary orthogonal techniques. Despite the associated challenges, researchers have demonstrated the tremendous potential of LIBS for real-time detection of explosives residues at standoff distances.