Chemical Matrix Effects Research Articles

Determination of alkali metal elements in solid biomass fuel by laser-induced breakdown spectroscopy: Analysis and reduction of chemical matrix effects.

Determination of alkali metal elements in solid biomass fuel by laser-induced breakdown spectroscopy: Analysis and reduction of chemical matrix effects.

Laser-induced breakdown spectroscopy as an analytical tool for total carbon quantification in tropical and subtropical soils: evaluation of calibration algorithms

The demand for efficient, accurate, and cost-effective methods of measuring soil carbon (C) in agriculture is growing. Traditional approaches are time consuming and expensive, highlighting the need for alternatives. This study tackles the challenge of utilizing laser-induced breakdown spectroscopy (LIBS) as a more economical method while managing its potential accuracy issues due to physical–chemical matrix effects. A set of 1,019 soil samples from 11 Brazilian farms was analyzed using various univariate and multivariate calibration strategies. The artificial neural network (ANN) demonstrated the best performance with the lowest root mean square error of prediction (RMSEP) of 0.48 wt% C, a 28% reduction compared to the following best calibration method (matrix-matching calibration – MMC inverse regression and multiple linear regression – MLR at 0.67 wt% C). Furthermore, the study revealed a strong correlation between total C determined by LIBS and the elemental CHNS analyzer for soils samples in nine farms (R² ≥ 0.73). The proposed method offers a reliable, rapid, and cost-efficient means of measuring total soil C content, showing that LIBS and ANN modeling can significantly reduce errors compared to other calibration methods. This research fills the knowledge gap in utilizing LIBS for soil C measurement in agriculture, potentially benefiting producers and the soil C credit market. Specific recommendations include further exploration of ANN modeling for broader applications, ensuring that agricultural soil management becomes more accessible and efficient.

Improving spatially-resolved lithium quantification in drill core samples of spodumene pegmatite by using laser-induced breakdown spectroscopy and pixel-matched reference areas

Improving spatially-resolved lithium quantification in drill core samples of spodumene pegmatite by using laser-induced breakdown spectroscopy and pixel-matched reference areas

A combination of spectrum selection and machine learning regression for minor element determination in gravel stones with LIBS

A combination of spectrum selection and machine learning regression for minor element determination in gravel stones with LIBS

A Comparative Study of the Method to Rapid Identification of the Mural Pigments by Combining LIBS-Based Dataset and Machine Learning Methods

Due to the similar chemical composition and matrix effect, the accurate identification of mineral pigments on wall paintings has brought great challenges. This work implemented an identification study on three mineral pigments with similar chemical compositions by combining LIBS technology with the K-nearest neighbor algorithm (KNN), random forest (RF support vector machine (SVM), back propagation artificial neural network (Bp-ANN) and convolutional neural network (CNN) to find the most suitable identification method for mural research. Using the SelectKBest algorithm, 300 characteristic lines with the largest difference among the three pigments were determined. The identification models of KNN, RF, SVM, Bp-ANN and CNN were established and optimized. The results showed that, except for the KNN model, the identification accuracy of other models for mock-up mural samples was above 99%. However, only the identification accuracy of 2D-CNN models reached above 94% for actual mural samples. Therefore, the 2D-CNN model was determined as the most suitable model for the identification and analysis of mural pigments.

Optimization of vortex-assisted switchable hydrophilicity solvent liquid phase microextraction for the selective extraction of vanillin in different matrices prior to spectrophotometric analysis

Optimization of vortex-assisted switchable hydrophilicity solvent liquid phase microextraction for the selective extraction of vanillin in different matrices prior to spectrophotometric analysis

Machine learning and transfer learning for correction of the chemical and physical matrix effects in the determination of alkali and alkaline earth metals with LIBS in rocks

Machine learning and transfer learning for correction of the chemical and physical matrix effects in the determination of alkali and alkaline earth metals with LIBS in rocks

Assessment of the Effect of Soil Sample Preparation, Water Content and Excitation Time on Proximal X-ray Fluorescence Sensing.

X-ray fluorescence (XRF) spectroscopy offers a fast and efficient method for analysing soil elemental composition, both in the laboratory and the field. However, the technique is sensitive to spectral interference as well as physical and chemical matrix effects, which can reduce the precision of the measurements. We systematically assessed the XRF technique under different sample preparations, water contents, and excitation times. Four different soil samples were used as blocks in a three-way factorial experiment, with three sample preparations (natural aggregates, ground to ≤2 mm and ≤1 mm), three gravimetric water contents (air-dry, 10% and 20%), and three excitation times (15, 30 and 60 s). The XRF spectra were recorded and gave 540 spectra in all. Elemental peaks for Si, K, Ca, Ti, Fe and Cu were identified for analysis. We used analysis of variance (anova) with post hoc tests to identify significant differences between our factors and used the intensity and area of the elemental peaks as the response. Our results indicate that all of these factors significantly affect the XRF spectrum, but longer excitation times appear to be more defined. In most cases, no significant difference was found between air-dry and 10% water content. Moisture has no apparent effect on coarse samples unless ground to 1 mm. We suggested that the XRF measurements that take 60 s from dry samples or only slightly moist ones might be an optimum option under field conditions.

From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration

With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks. The American Perseverance rover and the Chinese Zhurong rover respectively landed on Mars on February 18 and May 15, 2021, further increase the number of LIBS instruments on Mars. Such an unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data analysis. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still lacking a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement in the ability to predict of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for both types of rock samples.

Transfer learning improves the prediction performance of a LIBS model for metals with an irregular surface by effectively correcting the physical matrix effect

Transfer learning allows the effective corrections of both chemical and physical matrix effects in such a way that metals with irregular surfaces can be directly analyzed by LIBS for an accurate elemental determination.

Tourmaline Reference Materials for the In Situ Analysis of Oxygen and Lithium Isotope Ratio Compositions

Three tourmaline reference materials sourced from the Harvard Mineralogical and Geological Museum (schorl 112566, dravite 108796 and elbaite 98144), which are already widely used for the calibration of in situ boron isotope measurements, are characterised here for their oxygen and lithium isotope compositions. Homogeneity tests by secondary ion mass spectrometry (SIMS) showed that at sub‐nanogram test portion masses, their 18O/16O and 7Li/6Li isotope ratios are constant within ± 0.27‰ and ± 2.2‰ (1s), respectively. The lithium mass fractions of the three materials vary over three orders of magnitude. SIMS homogeneity tests showed variations in 7Li/28Si between 8% and 14% (1s), which provides a measure of the heterogeneity of the Li contents in these three materials. Here, we provide recommended values for δ18O, Δ’17O and δ7Li for the three Harvard tourmaline reference materials based on results from bulk mineral analyses from multiple, independent laboratories using laser‐ and stepwise fluorination gas mass spectrometry (for O), and solution multi‐collector inductively coupled plasma‐mass spectroscopy (for Li). These bulk data also allow us to assess the degree of inter‐laboratory bias that might be present in such data sets. This work also re‐evaluates the major element chemical composition of the materials by electron probe microanalysis and investigates these presence of a chemical matrix effect on SIMS instrumental mass fractionation with regard to δ18O determinations, which was found to be < 1.6‰ between these three materials. The final table presented here provides a summary of the isotope ratio values that we have determined for these three materials. Depending on their starting mass, either 128 or 512 splits have been produced of each material, assuring their availability for many years into the future.

Critical review and re-assessment of analyte protectants in gas chromatography

Critical review and re-assessment of analyte protectants in gas chromatography

Quantitative analysis modeling for the ChemCam spectral data based on laser-induced breakdown spectroscopy using convolutional neural network

Laser-induced breakdown spectroscopy (LIBS) has been applied to many fields for the quantitative analysis of diverse materials. Improving the prediction accuracy of LIBS regression models is still of great significance for the Mars exploration in the near future. In this study, we explored the quantitative analysis of LIBS for the one-dimensional ChemCam (an instrument containing a LIBS spectrometer and a Remote Micro-Imager) spectral data whose spectra are produced by the ChemCam team using LIBS under the Mars-like atmospheric conditions. We constructed a convolutional neural network (CNN) regression model with unified parameters for all oxides, which is efficient and concise. CNN that has the excellent capability of feature extraction can effectively overcome the chemical matrix effects that impede the prediction accuracy of regression models. Firstly, we explored the effects of four activation functions on the performance of the CNN model. The results show that the CNN model with the hyperbolic tangent (tanh) function outperforms the CNN models with the other activation functions (the rectified linear unit function, the linear function and the Sigmoid function). Secondly, we compared the performance among the CNN models using different optimization methods. The CNN model with the stochastic gradient descent optimization and the initial learning rate = 0.0005 achieves satisfactory performance compared to the other CNN models. Finally, we compared the performance of the CNN model, the model based on support vector regression (SVR) and the model based on partial least square regression (PLSR). The results exhibit the CNN model is superior to the SVR model and the PLSR model for all oxides. Based on the above analysis, we conclude the CNN regression model can effectively improve the prediction accuracy of LIBS.

Convolutional neural network as a novel classification approach for laser-induced breakdown spectroscopy applications in lithological recognition

Convolutional neural network as a novel classification approach for laser-induced breakdown spectroscopy applications in lithological recognition

Total alkali silica classification of rocks with LIBS: influences of the chemical and physical matrix effects

The matrix effects in LIBS analyses have been considered with univariate and machine learning based multivariate regression models for TAS classification of rocks.

Mapping of Uranium in Surrogate Nuclear Debris Using Laser-Induced Breakdown Spectroscopy (LIBS).

This work describes the use of a laser-induced breakdown spectroscopy (LIBS) system to conduct macroscopic elemental mapping of uranium and iron on the exterior surface and interior center cross-section of surrogate nuclear debris for the first time. The results suggest that similar LIBS systems could be packaged for use as an effective instrument for screening samples during collection activities in the field or to conduct process control measurements during the production of debris surrogates. The technique focuses on the mitigation of chemical and physical matrix effects of four uranium atomic emission lines, relatively free of interferences and of good analytical value. At a spatial resolution of 0.5 mm, a material fractionation pattern in the surrogate debris is identified and discussed in terms of constituent melting temperatures and thermal gradients experienced.

A first simulation of soil-laser interaction investigation for soil characteristic analysis

A first simulation of soil-laser interaction investigation for soil characteristic analysis

High microscale variability in Raman thermal maturity estimates from shale organic matter

High microscale variability in Raman thermal maturity estimates from shale organic matter

Matrix Effects in Quantitative Analysis of Laser-Induced Breakdown Spectroscopy (LIBS) of Rock Powders Doped with Cr, Mn, Ni, Zn, and Co.

Obtaining quantitative chemical information using laser-induced breakdown spectroscopy is challenging due to the variability in the bulk composition of geological materials. Chemical matrix effects caused by this variability produce changes in the peak area that are not proportional to the changes in minor element concentration. Therefore the use of univariate calibrations to predict trace element concentrations in geological samples is plagued by a high degree of uncertainty. This work evaluated the accuracy of univariate minor element predictions as a function of the composition of the major element matrices of the samples and examined the factors that limit the prediction accuracy of univariate calibrations. Five different sample matrices were doped with 10-85 000 ppm Cr, Mn, Ni, Zn, and Co and then independently measured in 175 mixtures by X-ray fluorescence, inductively coupled plasma atomic emission spectrometry, and laser-induced breakdown spectroscopy, the latter at three different laser energies (1.9, 2.8, and 3.7 mJ). Univariate prediction models for minor element concentrations were created using varying combinations of dopants, matrices, normalization/no normalization, and energy density; the model accuracies were evaluated using root mean square prediction errors and leave-one-out cross-validation. The results showed the superiority of using normalization for predictions of minor elements when the predicted sample and those in the training set had matrices with similar SiO2 contents. Normalization also mitigates differences in spectra arising from laser/sample coupling effects and the use of different energy densities. Prediction of minor elements in matrices that are dissimilar to those in the training set can increase the uncertainty of prediction by an order of magnitude. Overall, the quality of a univariate calibration is primarily determined by the availability of a persistent, measurable peak with a favorable transition probability that has little to no interference from neighboring peaks in the spectra of both the unknown and those used to train it.

A review of Mg isotope analytical methods by MC-ICP-MS

Application of multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) has led to big breakthrough of analytical methods for metal stable isotopes, resulting in rapid progresses in non-traditional stable isotope geochemistry. As a new geological tracer, Mg isotopes have been widely applied in studies of almost all important disciplines of geochemistry. High precision Mg isotope data measured by MC-ICP-MS are now available with precision about 0.05‰ amu−1 (2SD) or better. Because mass bias caused by chemical procedure and instrument can easily cause significant analytical error, it is still a challenge to obtain accurate Mg isotope data for natural samples. In this paper, we systematically review the development of analytical technique of Mg isotopes, with a detailed description of a series of important techniques used in the measurement process, including calibration of instrumental mass-bias, chemical purification process, matrix effect, and pitfalls for high precision isotope analyses. We compare standard data from different labs and establish a guideline for Mg isotope analysis procedure. Additionally, we briefly discuss the behaviors of Mg isotopes during geological processes including equilibrium and kinetic Mg isotope fractionations, such as magma differentiation, chemical and thermal diffusion, and continental weathering. Finally, we propose some future prospects for Mg isotope geochemistry in both high and low temperature geological processes.