Quantitative Determination of Cd in Soil Using Laser-Induced Breakdown Spectroscopy in Air and Ar Conditions

Liu, Xiaodan; Liu, Fei; Huang, Wei‐Hao; Peng, Jiyu; Shen, Tingting; He, Yong

doi:10.3390/molecules23102492

Cited by 25 publications

(12 citation statements)

References 64 publications

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“…The CARS-PLS algorithm was applied to select characteristics variables from the spectra. 25 Running times were 50 and 8 times those used by the interactive validation model. The best model was chosen according to the lowest RMSEP value.…”

Section: Results Obtained With the Cars-pls Modelmentioning

confidence: 99%

“…23 It is based on Darwin's evolution theory and used for the selection of N subsets of variables by conducting N sampling runs iteratively. 24,25 RF is an efficient method that borrows the framework from the reversible-jump Markov chain Monte Carlo method. 26 The RF algorithm served as a selection of variables based on normal distribution, and the variables were added or deleted, making a foundation for model building.…”

Section: Data Analysis Using Chemometric Algorithmsmentioning

confidence: 99%

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Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms

Wang

Zareef

et al. 2019

J Sci Food Agric

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BACKGROUND The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality. RESULTS Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first‐order derivative (1stD) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF‐PLS), synergy interval partial least square (Si‐PLS), genetic algorithm‐partial least square (GA‐PLS) and competitive adaptive reweighted sampling partial least square (CARS‐PLS). RF‐PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (RP), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51–14.58% and 2.10–3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade. CONCLUSION The proposed method can be used as a rapid, accurate and non‐destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry

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Section: Results Obtained With the Cars-pls Modelmentioning

confidence: 99%

Section: Data Analysis Using Chemometric Algorithmsmentioning

confidence: 99%

Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms

Wang

Zareef

et al. 2019

J Sci Food Agric

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“…The results showed that the LS-SVR model under an Ar atmosphere obtained the best performance. The root-mean-square error for calibration (RMSEC) and the root-mean square error for prediction (RMSEP) were only 0.026 and 0.034, respectively, which demonstrated the ability of LIBS for the accurate quantitative detection of Cd in soil [21]. To the best of our knowledge, the simultaneous quantitative detection of N, P, and K elements based on LIBS coupled with SVR models by different parameter optimization methods has not been investigated.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Elements in Fertilizer Using Laser-Induced Breakdown Spectroscopy Coupled with Support Vector Regression Model

Xiao

Ling

et al. 2019

Sensors

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The rapid detection of the elements nitrogen (N), phosphorus (P), and potassium (K) is beneficial to the control of the compound fertilizer production process, and it is of great significance in the fertilizer industry. The aim of this work was to compare the detection ability of laser-induced breakdown spectroscopy (LIBS) coupled with support vector regression (SVR) and obtain an accurate and reliable method for the rapid detection of all three elements. A total of 58 fertilizer samples were provided by Anhui Huilong Group. The collection of samples was divided into a calibration set (43 samples) and a prediction set (15 samples) by the Kennard–Stone (KS) method. Four different parameter optimization methods were used to construct the SVR calibration models by element concentration and the intensity of characteristic line variables, namely the traditional grid search method (GSM), genetic algorithm (GA), particle swarm optimization (PSO), and least squares (LS). The training time, determination coefficient, and the root-mean-square error for all parameter optimization methods were analyzed. The results indicated that the LIBS technique coupled with the least squares–support vector regression (LS-SVR) method could be a reliable and accurate method in the quantitative determination of N, P, and K elements in complex matrix like compound fertilizers.

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“…Results showed that the LS-SVM model for an Ar environment provided the best performance of prediction with R 2 of 0.98, and root mean square error of prediction (RMSEP) of 0.034 mg kg -1 . The authors concluded that LIBS combined with LS-SVM for anAr environment condition could be a useful tool for the accurate prediction of Cd for environmental monitoring(94).Table 1summarizes some of the available studies on the application LIBS for predicting PTEs in the soils. Compared with XRFS, few papers are available reporting results obtained by LIBS.…”

mentioning

confidence: 99%

“…Compared with XRFS, few papers are available reporting results obtained by LIBS. The use of multivariate statistical analyses is still limited to the commonly used PLSR, with a very limited application of machine learning methods(94). Spectral data preprocessing is not regularly applied, although standard normal variate (SNV) and wavelet methods are used to correct the spectral information, and reduce noise in order to improve the prediction performance(89).…”

mentioning

confidence: 99%

The applicability of spectroscopy methods for estimating potentially toxic elements in soils: state-of-the-art and future trends

Nawar

Cipullo

Douglas

et al. 2019

Applied Spectroscopy Reviews

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Potentially toxic elements (PTEs) in soils pose severe threats to the environment and human health. It is therefore imperative to have access to simple, rapid, portable and accurate methods for their detection in soils. In this regards, the review introduces recent progresses made in the development and applications of spectroscopic methods for in-situ semi-quantitative and quantitative detection of PTEs in soil and critically compares them to standard analytical methods. The advantages and limitations of these methods are discussed together with recent advances in chemometrics and data mining techniques allowing to extract useful information based on spectral data. Furthermore, the factors influencing soil spectra and data analysis are discussed and recommendations on how to reduce or eliminate their influences are provided. Future research and development needs for spectroscopy techniques are emphasized, and an analytical framework based on technology integration and data fusion is proposed to improve the measurement accuracy of PTEs in soil.

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Quantitative Determination of Cd in Soil Using Laser-Induced Breakdown Spectroscopy in Air and Ar Conditions

Cited by 25 publications

References 64 publications

Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms

Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms

Quantitative Analysis of Elements in Fertilizer Using Laser-Induced Breakdown Spectroscopy Coupled with Support Vector Regression Model

The applicability of spectroscopy methods for estimating potentially toxic elements in soils: state-of-the-art and future trends

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