Laser-Induced Breakdown Spectroscopy for Rapid Discrimination of Heavy-Metal-Contaminated Seafood Tegillarca granosa

A robust method to improve the regression accuracy of LIBS data: determination of heavy metal Cu in Tegillarca granosa

Tegillarca granosa (T. granosa) is susceptible to contamination by heavy metals, which poses potential health risks for consumers. Laser-induced breakdown spectroscopy (LIBS) combined with the classical partial least squares (PLS) model has shown promise in determining heavy metal concentrations in T. granosa. However, the presence of outliers during calibration can compromise the model's integrity and diminish its predictive capabilities. To address this issue, we propose using a robust method for partial least squares, RSIMPLS, to improve the accuracy of Cu prediction in T. granosa. The RSIMPLS algorithm was employed to analyze and process the high-dimensional LIBS data and utilized diagnostic plots to identify various types of outliers. By selectively eliminating certain outliers, a robust calibration method was achieved. The results showed that LIBS spectroscopy has the potential to predict Cu in T. granosa, with a coefficient of determination (Rp2) of 0.79 and a root mean square error of prediction (RMSEP) of 11.28. RSIMPLS significantly improved the prediction accuracy of Cu concentrations with a 43% decrease in RMSEP compared to the PLS. These findings validated the effectiveness of combining LIBS data with the RSIMPLS algorithm for the prediction of Cu concentrations in T. granosa.

Classification of Copper Minerals by Handheld Laser-Induced Breakdown Spectroscopy and Nonnegative Tensor Factorisation

Laser-induced breakdown spectroscopy (LIBS) analysers are becoming increasingly common for material classification purposes. However, to achieve good classification accuracy, mostly noncompact units are used based on their stability and reproducibility. In addition, computational algorithms that require significant hardware resources are commonly applied. For performing measurement campaigns in hard-to-access environments, such as mining sites, there is a need for compact, portable, or even handheld devices capable of reaching high measurement accuracy. The optics and hardware of small (i.e., handheld) devices are limited by space and power consumption and require a compromise of the achievable spectral quality. As long as the size of such a device is a major constraint, the software is the primary field for improvement. In this study, we propose a novel combination of handheld LIBS with non-negative tensor factorisation to investigate its classification capabilities of copper minerals. The proposed approach is based on the extraction of source spectra for each mineral (with the use of tensor methods) and their labelling based on the percentage contribution within the dataset. These latent spectra are then used in a regression model for validation purposes. The application of such an approach leads to an increase in the classification score by approximately 5% compared to that obtained using commonly used classifiers such as support vector machines, linear discriminant analysis, and the k-nearest neighbours algorithm.

Analysis of Pollution in High Voltage Insulators via Laser-Induced Breakdown Spectroscopy

Surface pollution deposition in a high voltage surface can reduce the surface flashover voltage, which is considered to be a serious accident in the transmission of electric power for the high conductivity of pollution in wet weather, such as rain or fog. Accordingly, a rapid and accurate online pollution detection method is of great importance for monitoring the safe status of transmission lines. Usually, to detect the equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD), the pollution should be collected when power cut off and bring back to lab, time-consuming, low accuracy and unable to meet the online detection. Laser-induced breakdown spectroscopy (LIBS) shows the highest potential for achieving online pollution detection, but its application in high voltage electrical engineering has only just begun to be examined. In this study, a LIBS method for quantitatively detecting the compositions of pollutions on the insulators was investigated, and the spectral characteristics of a natural pollution sample were examined. The energy spectra and LIBS analysis results were compared. LIBS was shown to detect pollution elements that were not detected by conventional energy spectroscopy and had an improved capacity to determine pollution composition. Furthermore, the effects of parameters, such as laser energy intensity and delay time, were investigated for artificial pollutions. Increasing the laser energy intensity and selecting a suitable delay time could enhance the precision and relative spectral intensities of the elements. Additionally, reducing the particle size and increasing the density achieved the same results.

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.

Estimation of the Fe and Cu Contents of the Surface Water in the Ebinur Lake Basin Based on LIBS and a Machine Learning Algorithm

Traditional technology for detecting heavy metals in water is time consuming and difficult and thus is not suitable for quantitative detection of large samples. Laser-induced breakdown spectroscopy (LIBS) can identify multi-state (such as solid, liquid, and gas) substances simultaneously, rapidly and remotely. In this study, water samples were collected from the Ebinur Lake Basin. The water samples were subjected to LIBS to extract the characteristic peaks of iron (Fe) and copper (Cu). Most of the quantitative analysis of LIBS rarely models and estimates the heavy metal contents in natural environments and cannot quickly determine the heavy metals in field water samples. This study creatively uses the Fe and Cu contents in water samples and the characteristics of their spectral curves in LIBS for regression modelling analysis and estimates their contents in an unknown water body by using LIBS technology and a machine learning algorithm, thus improving the detection rate. The results are as follows: (1) The Cu content of the Ebinur Lake Basin is generally higher than the Fe content, the highest Fe and Cu contents found within the basin are in the Ebinur Lake watershed, and the lowest are in the Jing River. (2) A number of peaks from each sample were found of the LIBS curve. The characteristic analysis lines of Fe and Cu were finally determined according to the intensities of the Fe and Cu characteristic lines, transition probabilities and high signal-to-background ratio (S/B). Their wavelengths were 396.3 and 324.7 nm, respectively. (3) The relative percent deviation (RPD) of the Fe content back-propagation (BP) network estimation model is 0.23, and the prediction ability is poor, so it is impossible to accurately predict the Fe content of samples. In the estimation model of BP network of Cu, the coefficient of determination (R²) is 0.8, the root mean squared error (RMSE) is 0.1, and the RPD is 1.79. This result indicates that the BP estimation model of Cu content has good accuracy and strong predictive ability and can accurately predict the Cu content in a sample. In summary, estimation based on LIBS improved the accuracy and efficiency of Fe and Cu content detection in water and provided new ideas and methods for the accurate estimation of Fe and Cu contents in water.