Sulfur Detection in Soil by Laser Induced Breakdown Spectroscopy Assisted by Multivariate Analysis

Gazeli, Odhisea; Stefas, Dimitrios; Couris, Stelios

doi:10.3390/ma14030541

Cited by 15 publications

(4 citation statements)

References 26 publications

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“…Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy technique with the advantages of in situ, real-time, rapid, and simultaneous multi-element detection [ 7 , 8 ]. The LIBS technique is applied in various fields, such as biological tissue detection [ 9 , 10 , 11 ], explosives detection [ 12 , 13 , 14 ], coal analysis [ 15 , 16 , 17 ], polymer identification analysis [ 18 , 19 , 20 ], food analysis [ 21 , 22 , 23 ], alloy analysis [ 24 , 25 , 26 ], ore identification analysis [ 27 , 28 , 29 ], and soil element detection [ 30 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy

Wei

Teng

Wang

et al. 2023

Foods

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Fritillaria has a long history in China, and it can be consumed as medicine and food. Owing to the high cost of Fritillaria cirrhosa, traders sometimes mix it with the cheaper Fritillaria thunbergii powder to make profit. Herein, we proposed a laser-induced breakdown spectroscopy (LIBS) technique to test the adulteration present in the sample of Fritillaria cirrhosa powder. Experimental samples with different adulteration levels were prepared, and their LIBS spectra were obtained. Partial least squares regression (PLSR) was adopted as the quantitative analysis model to compare the effects of four data standardization methods, namely, mean centring, normalization by total area, standard normal variable, and normalization by the maximum, on the performance of the PLSR model. Principal component analysis and least absolute shrinkage and selection operator (LASSO) were utilized for feature extraction and feature selection, and the performance of the PLSR model was determined based on its quantitative analysis. Subsequently, the optimal number of features was determined. The residuals were corrected using support vector regression (SVR). The mean absolute error and root mean square error of prediction obtained from the quantitative analysis results of the combined LASSO-PLSR-SVR model for the test set data were 5.0396% and 7.2491%, respectively, and the coefficient of determination R2 was 0.9983. The results showed that the LIBS technique can be adopted to test adulteration in the sample of Fritillaria cirrhosa powder and has potential applications in drug quality control.

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Section: Introductionmentioning

confidence: 99%

Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy

Wei

Teng

Wang

et al. 2023

Foods

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“…Many assistances and pioneering strategies have been acquainted with meeting the challenge of improving the estimation accuracy. Some of the major methods which were being used for determining the sulfur content of coal are as follows: Pyrolysis-Gas Chromatography [34,35], X-ray absorption near-edge structure spectroscopy (XANES), X-ray photoelectron spectroscopy (XPS) [36], X-ray diffraction (XRD) [37], energydispersive X-ray fluorescence (ED-XRF) [38], visible Spectrophotometry (VIS) [39], inductively coupled plasma-atomic emission spectroscopy (ICP-AES) [40], laser-induced breakdown spectroscopy (LIBS) [41][42][43][44], thermogravimetric-Fourier-transform infrared spectroscopy (TG-FTIR) [45], and Fourier-transform infrared spectroscopy (FTIR) [46][47][48][49][50]. All these methods have their own advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data

et al. 2023

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High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm−1) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R2) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (SCHNS, wt.%) using CHNS data and the model predicted sulfur (SFTIR, wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide.

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“…Correlations were also developed using maximum likelihood estimation (MLE), efficiently predicting the elemental compositions for ultimate analysis [12]. Some of the major estimation methods developed by researchers are as follows: X-ray fluorescence spectrometry (XRF) [13,14]; optical emission spectrometry with inductively coupled plasma (ICP-OES) [15,16]; mass spectrometry with inductively coupled plasma (ICP-MS) [17]; laser-induced breakdown spectroscopy (LIBS) [18][19][20][21][22][23]; thermogravimetric Fourier-transform infrared spectroscopy (TG-FTIR) [24], and Fourier-transform infrared spectroscopy (FTIR) [25][26][27]. All of these methods have advantages and constraints, which were preferred for analyses based on requirements and equipment availability.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy

et al. 2023

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The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as CCHNS (in wt. %), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as CFTIR, in wt %). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm−1) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (CCHNS) and modeled carbon from FTIR data (CFTIR) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R2) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed t-test and F-test showed that no significant difference occurred between the pair of observed CCHNS and the model’s estimated CFTIR. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples.

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Sulfur Detection in Soil by Laser Induced Breakdown Spectroscopy Assisted by Multivariate Analysis

Cited by 15 publications

References 26 publications

Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy

Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy

Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data

Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy

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