Extended total number density compensation for uranium determination by laser-induced breakdown spectroscopy.

BACKGROUND: Variations in plasma properties among spectra and samples lead to significant signal uncertainty and matrix effects in laser-induced breakdown spectroscopy (LIBS). To address this issue, direct compensation for plasma property variations is considered highly desirable. However, reliably compensating for the total number density variation is challenging due to inaccurate spectroscopic parameters. For reliable compensation, a total number density compensation (TNDC) method was presented in our recent work, but its applicability is limited to simple samples because of its strict assumptions. In this study, we propose a new pre-processing method, namely extended TNDC (ETNDC), to reduce signal uncertainty and matrix effects in the more complex analytical task of uranium determination. RESULTS: ETNDC reflects the total number density variation with a weighted combination of spectral lines from all major elements and incorporates temperature and electron density compensation into the weighting coefficients. The method is evaluated on yellow cake samples and combined with regression models for uranium determination. Using the typical validation set and line combination, the mean relative standard deviation (RSD) of U II 417.159 nm in validation samples decreases from 4.92% to 2.27%, and the root mean square error of prediction (RMSEP) and the mean RSD of prediction results decrease from 4.81% to 1.93% and from 1.92% to 1.56%, respectively. Furthermore, the results of 10 validation sets and 216 line combinations show that ETNDC outperforms baseline methods in terms of average performance and robustness. SIGNIFICANCE: For the first time, ETNDC explicitly addresses the temperature and electron density variations while compensating for the total number density variation, where the inaccurate spectroscopic parameters are avoided by fitting related quantities using concentration information. The method demonstrates effective and robust improvement in signal repeatability and analytical performance in uranium determination, facilitating accurate quantification of the LIBS technique.

Long-term repeatability improvement using beam intensity distribution for laser-induced breakdown spectroscopy.

The relatively low measurement repeatability has long been considered as a major obstacle to the widespread use and commercialization of laser-induced breakdown spectroscopy (LIBS). Although many efforts have been made to improve the signal repeatability in the short term, how to improve the long-term signal repeatability is critical in practical applications and has rarely been studied. Moreover, the mechanisms behind the degradation of long-term repeatability are not fully revealed. This study proposes a new method to improve the long-term repeatability of LIBS measurement, which modifies the spectral intensity based on laser beam intensity distribution. It first pre-processes the beam intensity distribution profiles and spectral intensity. Then the relationship between the relative deviations of beam and spectral intensities is modelled using Partial Least Squares Regression (PLSR). The proposed method was tested on copper and silicon samples, and the spectra and laser beam intensity distribution were recorded for more than thirty days. Day-to-day variations in beam intensity distribution were observed. Such variations can lead to changes in spectral intensity, resulting in degraded signal repeatability. By modifying the spectral intensity, the long-term signal repeatability was improved. Specifically, in terms of day-mean spectral intensity, the valid correction rates were above 70% for both of copper silicon sample in most cases. Long-term RSD decreased from ∼13.5% to ∼4% for copper and decreased from ∼10.7% to 6.5% for silicon sample. These results indicate that the proposed method provides a viable method for improving the long-term repeatability of LIBS measurement.

Raw signal improvement using beam shaping plasma modulation for uranium detection in ore using laser-induced breakdown spectroscopy.

Real-time quantitative detection of uranium in ores is one of the major challenges for uranium exploration. Laser-induced breakdown spectroscopy (LIBS) has been regarded as a most promising technique for this application. However, due to the matrix complexity as well as low uranium concentration of ore, the detection sensitivity of LIBS for uranium in ores is still unsatisfactory. This work explored the potential of a beam-shaping plasma modulation method to improve the limit of detection of uranium in ores. By shaping the profile of laser beam from normally Gaussian distribution to flat-top, the plasma was modulated to be more excited with reduced peak electron density at the laser-plasma interaction point for plasma shielding reduction especially at high laser energy as well as to be more morphologically stable for LIBS signal repeatability improvement. It was further found that this method enhanced LIBS signal intensity mainly by increasing the plasma temperature, while the electron density was almost unchanged, which was very attractive for uranium detection in ore since one of the major problem for uranium detection was that it is hard to find clear uranium spectral lines for analysis due to the high dense emission lines of ore samples in real cases and lower electron density, indicated less line broadening and less line overlap or interferences. A clear uranium emission line has been found in crowded ore spectra, and the intensity of U II 409.013 nm based on flat-top beam was about 5 times higher than that of Gaussian beam, and the relative standard deviation (RSD) of the signal was reduced by about 50%. Moreover, the LOD of uranium in ores was estimated to be 21.2 ppm with flat-top beam, indicating that beam shaping is a promising method for rapid and accurate detection of uranium in ores.

Compensation for the variation of total number density to improve signal repeatability for laser-induced breakdown spectroscopy.

High signal uncertainty has been regarded as a critical obstacle for the quantitative analysis of laser-induced breakdown spectroscopy (LIBS). One of the most effective ways for uncertainty reduction is to directly compensate for the variation of plasma properties, especially total number density. However, reliable compensation for the variation of total number density is hard to implement. In this work, we propose a data pre-processing method, called total number density compensation (TNDC), to reduce signal uncertainty. It is established on an assumption extended from the internal standard method and utilizes a weighted sum of emission lines from all major elements to reflect the variation of total number density. The TNDC method is tested on 29 brass samples and outperforms common normalization methods based on the spectral area in terms of signal repeatability and analytical performance. For Cu, the mean pulse-to-pulse relative standard deviation (RSD) of signals is greatly decreased from 5.10% to 1.03%, which is almost the best signal repeatability that LIBS can achieve and is comparable to that of ICP-OES. The root mean square error of prediction (RMSEP) and the mean RSD of prediction are decreased from 6.56% to 0.60% and from 12.00% to 1.03%, respectively. While for Zn, the mean RSD of signals improves from 6.43% to 4.12%, and the RMSEP is reduced from 1.57% to 0.59% with the RSD of prediction from 5.41% to 4.18%. Results demonstrate that TNDC can be an effective method for LIBS analysis especially for repeatability improvement.

Improvement of sample discrimination using laser-induced breakdown spectroscopy with multiple-setting spectra.

Laser-induced breakdown spectroscopy (LIBS) is a promising multi-elemental analysis technique and has the advantages of rapidness and minimal sample preparation. In traditional LIBS measurement, sample spectra are generally collected based on a single set of fixed experimental parameters, such as laser energy and delay time. When samples have the same main components and similar component concentrations, the difference in their spectral intensities becomes less obvious. This can lower the sensitivity of LIBS measurement and pose a threat to the accuracy and robustness of LIBS qualitative analysis. In this work, we propose a new method to increase the spectral difference between similar samples, namely multiple-setting spectra. For each sample, it adopts different sets of experimental parameters and obtains a group of spectra to increase the fingerprint spectral information. The effectiveness of the proposed method is theoretically verified and then tested on 11 similar coal samples. Specifically, the sample spectra were collected with different laser energy and delay time, and processed by principal component analysis (PCA) and Davies-Bouldin index (DBI). The results show that the use of multiple-settings spectra can significantly improve the sample discrimination accuracy from 81.8% to 96.4%. In addition, the proposed method can maintain the efficiency and cost of LIBS measurement.

Quantification of extra virgin olive oil adulteration using smartphone videos.

Edible oil adulteration is a main concern for consumers. This paper presents a study on the use of smartphone, coupled with image processing and chemometrics, to quantify adulterant levels in extra virgin olive oil. A sequence of light with varying colours is generated on the phone screen, which is used to illuminate oil samples. Videos are recorded to capture the colour changes on sample surface and are subsequently converted into spectral data for analysis. To evaluate the performance of this video approach, partial least squares regression models constructed from such video data as well as near-infrared, ultraviolet-visible and digital imaging data are compared in the task of quantifying the level of vegetable oil in extra virgin olive oil in the range 5%-50% (v/v). The results show that the video approach (R2 = 0.98 and RMSE = 0.02) yields comparable performance to baseline spectroscopy techniques and outperforms computer vision system approach. Since the smartphone-based sensor system is low-cost and easy to operate, it has high potential to become a consumer-oriented solution for detecting edible oil adulteration.

Differentiation Between Organic and Non-Organic Apples Using Diffraction Grating and Image Processing-A Cost-Effective Approach.

As the expectation for higher quality of life increases, consumers have higher demands for quality food. Food authentication is the technical means of ensuring food is what it says it is. A popular approach to food authentication is based on spectroscopy, which has been widely used for identifying and quantifying the chemical components of an object. This approach is non-destructive and effective but expensive. This paper presents a computer vision-based sensor system for food authentication, i.e., differentiating organic from non-organic apples. This sensor system consists of low-cost hardware and pattern recognition software. We use a flashlight to illuminate apples and capture their images through a diffraction grating. These diffraction images are then converted into a data matrix for classification by pattern recognition algorithms, including k-nearest neighbors (k-NN), support vector machine (SVM) and three partial least squares discriminant analysis (PLS-DA)- based methods. We carry out experiments on a reasonable collection of apple samples and employ a proper pre-processing, resulting in a highest classification accuracy of 94%. Our studies conclude that this sensor system has the potential to provide a viable solution to empower consumers in food authentication.

Nearest clusters based partial least squares discriminant analysis for the classification of spectral data.

Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most effective multivariate analysis methods for spectral data analysis, which extracts latent variables and uses them to predict responses. In particular, it is an effective method for handling high-dimensional and collinear spectral data. However, PLS-DA does not explicitly address data multimodality, i.e., within-class multimodal distribution of data. In this paper, we present a novel method termed nearest clusters based PLS-DA (NCPLS-DA) for addressing the multimodality and nonlinearity issues explicitly and improving the performance of PLS-DA on spectral data classification. The new method applies hierarchical clustering to divide samples into clusters and calculates the corresponding centre of every cluster. For a given query point, only clusters whose centres are nearest to such a query point are used for PLS-DA. Such a method can provide a simple and effective tool for separating multimodal and nonlinear classes into clusters which are locally linear and unimodal. Experimental results on 17 datasets, including 12 UCI and 5 spectral datasets, show that NCPLS-DA can outperform 4 baseline methods, namely, PLS-DA, kernel PLS-DA, local PLS-DA and k-NN, achieving the highest classification accuracy most of the time.