A Study of the Effectiveness of Neural Networks for Elemental Concentration from Libs Spectra

Laser-induced breakdown spectroscopy (LIBS) is an advanced data analysis technique for spectral analysis based on the direct measurement of the spectrum of optical emission from a laser-induced plasma. Assignment of different atomic and ionic lines, which are signatures of a particular element, is the basis of a qualitative identification of the species present in plasma. The relative intensities of these atomic and ionic lines can be used for the quantitative determination of the corresponding elements present in different samples. Calibration curve based on absolute intensity is the statistical method of determining concentrations of elements in different samples. Since we need an exact knowledge of the sample composition to build the proper calibration curve, this method has some limitations in the case of samples of unknown composition. The current research is to investigate the usefulness of ANN for the determination of the element concentrations from spectral data. From the study it is shown that neural networks predict elemental concentrations that are at least as good as the results obtained from traditional analysis. Also by automating the analysis process, we have achieved a vast saving in the time required for the data analysis

A comparison between Recurrent Neural Networks and classical machine learning approaches In Laser induced breakdown spectroscopy

Recurrent Neural Networks are classes of Artificial Neural Networks that establish connections between different nodes form a directed or undirected graph for temporal dynamical analysis. In this research, the laser induced breakdown spectroscopy (LIBS) technique is used for quantitative analysis of aluminum alloys by different Recurrent Neural Network (RNN) architecture. The fundamental harmonic (1064 nm) of a nanosecond Nd:YAG laser pulse is employed to generate the LIBS plasma for the prediction of constituent concentrations of the aluminum standard samples. Here, Recurrent Neural Networks based on different networks, such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (Simple RNN), and as well as Recurrent Convolutional Networks comprising of Conv-SimpleRNN, Conv-LSTM and Conv-GRU are utilized for concentration prediction. Then a comparison is performed among prediction by classical machine learning methods of support vector regressor (SVR), the Multi Layer Perceptron (MLP), Decision Tree algorithm, Gradient Boosting Regression (GBR), Random Forest Regression (RFR), Linear Regression, and k-Nearest Neighbor (KNN) algorithm. Results showed that the machine learning tools based on Convolutional Recurrent Networks had the best efficiencies in prediction of the most of the elements among other multivariate methods

Correlating LIBS Coal Data for Coal Property Prediction

This report presents results for correlations between coal data derived from laboratory analysis and Laser Induced Breakdown Spectroscopy analysis. LIBS data were used to predict higher order properties of coal using artificial neural network models. Higher order coal properties such as heating value and ash fusion temperature are predicted using LIBS analysis and compared against standard laboratory measurements. Selected formulas for the prediction of coal properties are also presented and compared against the neural network and laboratory results

Signal Optimization and Enhancement of Laser-Induced Breakdown Spectroscopy for Discrimination of Bacterial Organisms

Bacterial pathogens can be differentiated via an elemental analysis techniqueknown as laser-induced breakdown spectroscopy (LIBS). This spectrochemical technique provides a near-instantaneous measurement of the elemental composition of a target. The aim of this work was to demonstrate the feasibility of LIBS for the rapid identification and discrimination of bacteria in simulated clinical specimens based on reproducible differences in the concentration of inorganic elements in bacterial cells. This research will describe the current experimental technique, including bacteria collection and mounting protocols, LIBS data acquisition, and spectral data analysis. These include methods for the collection, concentration, and separation of bacteria from unwanted biological matter, deposition of bacterial cells on a suitable ablation medium, the formation of high temperature laser-induced micro plasmas, collection, and analysis of the atomic emission spectra with a high-resolution spectrometer, and the differentiation of LIBS emission spectra from different bacterial species and genera using computerized chemometric algorithms. The construction of a spectral library database containing the LIBS emission spectra from hundreds of spectra obtained from highly diluted specimens of Staphylococcus epidermidis, Escherichia coli, Mycobacterium smegmatis, Pseudomonas aeruginosa, Enterococcus cloacae and sterile water control specimens is ongoing. Manipulation of this library with outlier elimination techniques, reduction of elemental contaminants contributing to extraneous background signals, and the addition of silver microparticles to enhance signal intensities are all being investigated to produce a standardized protocol that minimizes the bacterial limit of detection while maximizing classification accuracy

Using LIBS and Advanced Data Processing to Analyze Biomass and Coal Feedstock for Utility Boiler Applications

A study was undertaken to determine the feasibility of using advanced instrumentation and data processing to accurately predict in real-time the properties of biomass to be used as a supplemental fuel in coal-fired electric generating plants. Biomass use would reduce greenhouse gas emissions and also lower the fuel costs for a power plant. However, biomass properties are highly variable and not well characterized with a time scale that can be used for boiler operational control. Laser Induced Breakdown Spectroscopy (LIBS) was the analytical technique used in this study to analyze samples of biomass and coal. Spectral data obtained with LIBS were processed using advanced data processing techniques to determine fuel properties of interest.In this study, ash fusion temperature, high heating value, and ash mineral concentrations were measured. The results were highly successful by comparing the experimental results with independent laboratory analysis. All mineral results showed almost linear calibration curves with little data scatter. The heating value results, ranging from 6,620 Btu/lb to 13,080 Btu/lb, were obtained with root mean square error of approximately ±15 Btu/lb. The initial deformation ash fusion temperature, ranging from 1,590 to 2,800 ° F has a root mean square deviation of approximately ±33.34 ° F. These results showed that even under significant property variations, the combined application of LIBS and advanced data processing provides results that a power plant operator could use to mitigate problems in boilers fired with biomass and coal, which originate from the fuel quality variability of the feedstock

Artificial Intelligence for Spectral Analysis: a Comprehensive Framework

Die Spektralanalyse wird in diversen akademischen und industriellen Bereichen eingesetzt, um relevante Elementinformationen zu extrahieren. Bei der qualitativen Analyse ist eine genaue Identifizierung der vorhandenen Elemente erforderlich, und bei der quantitativen Analyse, die Konzentrationen aller relevanten Elemente müssen präzis bestimmt werden. Obwohl die aktuellen kommerziellen Ansätze hervorragende Ergebnisse bei der Elementquantifizierung liefern können, stoßen sie immer noch an ihre Grenzen: hohe Rechenzeit (insbesondere bei komplexen Aufgaben), personalintensive manuelle Elementidentifizierung und erhebliche Kosten für die Gerätekalibrierung. In dieser Dissertation wird ein umfassendes, auf neuronalen Netzen basierendes System für die Spektralanalyse in großem Maßstab entworfen. Um eine neue und angemessene Baseline zu erstellen, wobei die meisten gängigen Elemente (bis zu 28) abdeckt werden können, werden umfangreiche Experimente durchgeführt, um die erforderliche Trainingsdatengröße zu untersuchen, geeignete Netzwerkarchitekturen auszuwählen und problemspezifische Konfigurationen zu analysieren. Bei den Quantifizierungsaufgaben erreicht der vorgestellte Ansatz im Vergleich zu den klassischen Methoden die gleiche Fehlerquote mit einer signifikanten Geschwindigkeitssteigerung um einen Faktor von über 400. Auch für die qualitative Analyse wird die Klassifizierung von Elementen mit einer ausgezeichneten Genauigkeit von über 99\% bei realen Messungen automatisiert, wobei die Dimension der Eingabedaten auf einer interpretierbaren Weise stark reduziert wird. Darüber hinaus erfordern neuronale Netze in der Regel große Rechen- und Speicherressourcen, so dass die Anwendung mit Problemen in Bezug auf Latenzzeiten, Speicherplatzbedarf und Stromverbrauch konfrontiert sein kann, insbesondere bei Endgeräten mit geringer Leistung. Um dieses Problem zu lösen, wurde ein hybrider Ansatz entwickelt, der die Ausführung neuronaler Netze optimiert, beschleunigt und dennoch die endgültige Leistung beibehält. Die Ergebnisse auf verschiedenen Zielhardwareplattformen zeigen, dass dieser hybride Ansatz in den meisten Fällen eine bis zu 52-fache Komprimierung der Modellgröße und eine 600-fache Beschleunigung mit sogar besserer Performanz erreichen kann, was den Einsatz auf Edge-Geräten mit geringen Kosten ermöglicht. Um schließlich die letzte Hürde des Kalibrierungsproblems auf dem Weg zu einem großflächigen Einsatz auf einer großen Anzahl von Geräten in der Industrie zu überwinden, wird ein auf Meta-Learning basierender Ansatz entwickelt, um hervorragende Kalibrierungsergebnisse mit minimalen Kosten zu erreichen, indem die neuronale Netze lernen zu kalibrieren. Das allgemeine Spektralanalyseproblem wird als Multi-Geräte-Multi-Konfigurationsaufgabe formuliert und es erreicht die beste Fehlerrate vor und nach der Kalibrierung bei verschiedenen unbekannten Geräten. Im Vergleich zu den Basisansätzen mit Kalibrierung, schneidet es auch ohne Kalibrierung gleich gut ab, was in einem realen Szenario sehr praktisch ist, wo ein unbekanntes Gerät ohne verfügbare Referenzproben für die Kalibrierung eingesetzt werden muss. Darüber hinaus zeigt die Ressourcenanalyse, dass der Ansatz deutlich weniger Ressourcen für den industriellen Einsatz erfordert, was zu einem enormen Einsparungs- und Wachstumspotenzial beiträgt

Mapping portuguese soils using spectroscopic techniques with a machine learning approach

Dissertação de mestrado Erasmus Mundus para obtenção do grau de mestre em Técnicas Laboratoriais ForensesSoil analysis is an important part of forensic science as it can provide vital links between a suspect and a crime scene based on its characteristics. The use of soil in a forensic context can be characterised into two categories: intelligence purposes or court purposes. The core basis of the comparison of sites to determine the provenance is that soil composition, type etc. vary from one place to another. The aim of this project is to ‘map’ soils and predict the location of a sample of unknown origin based on the chemometric profiles of Fourier transform infrared (FTIR) spectra, micro x-ray fluorescence profiles and visible spectra. Thirty one samples were collected in triplicate from Monsanto Park in Lisbon for each predetermined collection point on a defined grid. Full FTIR spectra (400-4000cm-1), Visible (1100-401cm-1) spectra, UV (400-200cm-1) spectra and μXRF profiles were collected for all samples. A subset of 43 discriminant features was selected from a total of 1430 using the Boruta feature selection algorithm from the FTIR, μXRF and visible spectra. These discriminant features acted as input data that was used to create a neural network which allowed the prediction of Cartesian co-ordinates (or location) of the samples with a high degree of accuracy (86%) and has shown to be a very useful approach to predict soil location

Deep Spectral CNN for Laser Induced Breakdown Spectroscopy

This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, 'Curiosity'

Modified Firearm Discharge Residue Analysis utilizing Advanced Analytical Techniques, Complexing Agents, and Quantum Chemical Calculations

The use of gunshot residue (GSR) or firearm discharge residue (FDR) evidence faces some challenges because of instrumental and analytical limitations and the difficulties in evaluating and communicating evidentiary value. For instance, the categorization of GSR based only on elemental analysis of single, spherical particles is becoming insufficient because newer ammunition formulations produce residues with varying particle morphology and composition. Also, one common criticism about GSR practitioners is that their reports focus on the presence or absence of GSR in an item without providing an assessment of the weight of the evidence. Such reports leave the end-used with unanswered questions, such as “Who fired the gun?” Thus, there is a critical need to expand analytical capabilities and enhance the impact of the forensic scientist’s conclusions. To maximize the evidential value of GSR evidence, detection methods exploiting modern advancements in instrumentation must be explored and developed. This collection of work reviews the current literature review and illustrates a trend to investigate emerging methods to enhance IGSR analysis with a wider emphasis on OGSR compounds. Combining IGSR and OGSR components increases the confidence of detecting GSR on a collected sample. Overall, the development of novel analytical methods for GSR detection, the application of ground-breaking statistical methods to interpret GSR evidence using artificial intelligence (neural networks) and likelihood ratios to estimate the weight of the evidence, and the understating of the host-guest chemistry of GSR species is anticipated to provide a needed leap of knowledge in the community