Laser-Induced Breakdown Spectroscopy and Principal Component Analysis for the Classification of Spectra from Gold-Bearing Ores

Abstract

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.

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

“…7 As one of the many LIBS applications, metal sorting 8 has recently attracted significant attention because of its commercial value. [9][10][11] For a reliable identification model, LIBS can be combined with multivariate chemometric analysis to distinguish similar types of materials using machine learning (ML) techniques, such as principal component analysis (PCA), 12,13 partial least square-discriminant analysis, [14][15][16] and artificial neural network. 17 To obtain a desirable classification accuracy, a robust ML model should be trained with extensive LIBS training data under the basic assumption that training and test data follow the same distribution.…”

Application of laser-induced breakdown spectroscopy with a generalized regression neural network and LASSO-type methods for estimation of arsenic and chromium in soil

“…The most popular method for quantitative analysis of LIBS is to establish calibration curves from the correspondence between known element concentrations and their characteristic spectral line intensities. [18][19][20] However, the prediction accuracy of current quantitative methods is oen affected by matrix effects. 21 Therefore, reducing the inuence of the matrix effect in quantitative analysis requires the establishment of relevant mathematical models.…”

In situ detection of spices with medicinal value based on laser‐induced breakdown spectroscopy

“…[18][19][20][21][22] LIBS is an atomic emission spectroscopy (AES) technique which has a wide range of applications in various fields, especially in the field of in situ detection of food, [23][24][25][26] medical, [27][28][29] and environment. 30,31 Besides, principal component analysis (PCA) [32][33][34][35] and back propagation artificial neural network (BP-ANN) [36][37][38] has been applied much in the data exploration of LIBS, which can be effectively applied to identify and classify different samples.…”