scholarly journals High accuracy determination of copper in copper concentrate with double genetic algorithm and partial least square in laser-induced breakdown spectroscopy

2020 ◽  
Vol 28 (2) ◽  
pp. 2142 ◽  
Author(s):  
Haochen Li ◽  
Meizhen Huang ◽  
Huidi Xu
Keyword(s):  
Genetic Algorithm ◽  
High Accuracy ◽  
Partial Least Square ◽  
Least Square ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Accuracy Determination ◽  
Determination Of Copper

Detection of Cadmium in Navel Orange by Laser Induced Breakdown Spectroscopy Combined with Moving Window Partial Least Square

2017 ◽  
Vol 54 (8) ◽  
pp. 083002 ◽  
Author(s):  
杨晖 Yang Hui ◽  
黄林 Huang Lin ◽  
刘木华 Liu Muhua ◽  
陈添兵 Chen Tianbing ◽  
饶刚福 Rao Gangfu ◽  
...  
Keyword(s):  
Partial Least Square ◽  
Least Square ◽  
Laser Induced Breakdown Spectroscopy ◽  
Navel Orange ◽  
Moving Window ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

scholarly journals High-Sensitivity Determination of Nutrient Elements in Panax notoginseng by Laser-induced Breakdown Spectroscopy and Chemometric Methods

Molecules ◽  
2019 ◽  
Vol 24 (8) ◽  
pp. 1525 ◽  
Author(s):  
Tingting Shen ◽  
Weijiao Li ◽  
Xi Zhang ◽  
Wenwen Kong ◽  
Fei Liu ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Least Squares ◽  
Panax Notoginseng ◽  
High Accuracy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Quantitative Detection ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

High-accuracy and fast detection of nutritive elements in traditional Chinese medicine Panax notoginseng (PN) is beneficial for providing useful assessment of the healthy alimentation and pharmaceutical value of PN herbs. Laser-induced breakdown spectroscopy (LIBS) was applied for high-accuracy and fast quantitative detection of six nutritive elements in PN samples from eight producing areas. More than 20,000 LIBS spectral variables were obtained to show elemental differences in PN samples. Univariate and multivariate calibrations were used to analyze the quantitative relationship between spectral variables and elements. Multivariate calibration based on full spectra and selected variables by the least absolute shrinkage and selection operator (Lasso) weights was used to compare the prediction ability of the partial least-squares regression (PLS), least-squares support vector machines (LS-SVM), and Lasso models. More than 90 emission lines for elements in PN were found and located. Univariate analysis was negatively interfered by matrix effects. For potassium, calcium, magnesium, zinc, and boron, LS-SVM models based on the selected variables obtained the best prediction performance with Rp values of 0.9546, 0.9176, 0.9412, 0.9665, and 0.9569 and root mean squared error of prediction (RMSEP) of 0.7704 mg/g, 0.0712 mg/g, 0.1000 mg/g, 0.0012 mg/g, and 0.0008 mg/g, respectively. For iron, the Lasso model based on full spectra obtained the best result with an Rp value of 0.9348 and RMSEP of 0.0726 mg/g. The results indicated that the LIBS technique coupled with proper multivariate chemometrics could be an accurate and fast method in the determination of PN nutritive elements for traditional Chinese medicine management and pharmaceutical analysis.

Fast Determination of Copper Content in Tobacco (Nicotina tabacum L.) Leaves Using Laser-Induced Breakdown Spectroscopy with Univariate and Multivariate Analysis

2018 ◽  
Vol 61 (3) ◽  
pp. 821-829 ◽  
Author(s):  
Jiyu Peng ◽  
Lanhan Ye ◽  
Tingting Shen ◽  
Fei Liu ◽  
Kunlin Song ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Multivariate Analysis ◽  
Copper Content ◽  
Laser Induced Breakdown Spectroscopy ◽  
Fast Determination ◽  
Tobacco Leaves ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Determination Of Copper

Abstract. Fast and effective measures to determine heavy metals play an important role in ensuring food quality and safety. In this experiment, laser-induced breakdown spectroscopy (LIBS) was used to detect copper content (Cu) in tobacco ( L.) leaves. The experimental parameters for detection, including laser energy, delay time, and camera gate width, were optimized by response surface methodology (RSM). Univariate analysis and multivariate analysis, including partial least squares regression (PLSR) and extreme learning machine (ELM), were used to establish calibration models. In addition, different preprocessing methods were used to eliminate the signal variations and further improve the calibration performance, including baseline correction, background normalization, area normalization, and standard normal variate (SNV) normalization. The results showed that LIBS combined with both univariate and multivariate methods could be used to detect copper content in tobacco leaves. SNV and area normalization were efficient in dealing with signal variations and improving the calibration performance. The ELM model with SNV normalized variables in the spectral region of 324.02 to 325.98 nm achieved the best performance (R2 = 0.9552 and RMSE = 4.8416 mg kg-1 in the testing set). The results provide the first proof-of-principle data for fast determination of copper content in tobacco leaves. Keywords: Copper content, Laser-induced breakdown spectroscopy, Multivariate calibration, Response surface methodology, Tobacco leaves, Univariate calibration.

scholarly journals Analysis of Salt Mixture Contamination on Insulators via Laser-Induced Breakdown Spectroscopy

2020 ◽  
Vol 10 (7) ◽  
pp. 2617 ◽  
Author(s):  
Shan Lu ◽  
Xinwei Wang ◽  
Tianzheng Wang ◽  
Xinran Qin ◽  
Xilin Wang ◽  
...  
Keyword(s):  
Spectral Characteristics ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Laser Induced Breakdown Spectroscopy ◽  
Mixture Ratio ◽  
Breakdown Spectroscopy ◽  
Mixing Ratios ◽  
Calibration Curves ◽  
Laser Induced Breakdown

The composition of contamination deposited on transmission line insulators can affect their surface flashover voltage. Currently, there is no rapid on-line method to detect this contamination composition in power grids. In this paper, we applied laser-induced breakdown spectroscopy (LIBS) to analyze contamination on insulator surfaces. Usually, Na and Ca salts are found in contamination along with various sulfate, carbonate, and chloride compounds. As an element’s detection method, LIBS can only measure a certain element content, for example, Ca. The mixture of various compounds with the same cations can influence the LIBS signal. The influence of mixing ratios on the calibration curves and relative spectral intensity was studied via LIBS. Na2CO3, NaHCO3, CaSO4, and CaCO3 samples containing different proportions of Na and Ca were prepared. The linear correlation coefficients (R2) for the Na and Ca calibration curves generated using various mixing ratios were analyzed. The results showed that the mixture ratio did not dramatically affect the linear calibration curves for mixtures containing the same cations. This finding may significantly reduce the difficulty of applying LIBS analysis for complex contamination on insulators. The laser energy density had effects on the spectral characteristics of the measured elements. The partial least-square regression (PLSR) model can improve the accuracy of Na and Ca prediction.

scholarly journals Edible Gelatin Diagnosis Using Laser-Induced Breakdown Spectroscopy and Partial Least Square Assisted Support Vector Machine

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4225 ◽  
Author(s):  
Hao Zhang ◽  
Shun Wang ◽  
Dongxian Li ◽  
Yanyan Zhang ◽  
Jiandong Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Partial Least Square ◽  
Least Square ◽  
Computational Time ◽  
Laser Induced Breakdown Spectroscopy ◽  
Accurate Estimation ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Svm Model ◽  
Laser Induced Breakdown

Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square–support vector machine (PLS-SVM) method for the fast and accurate estimation of edible gelatin adulteration. Gelatin samples with 11 different adulteration ratios were prepared by mixing pure edible gelatin with industrial gelatin, and the LIBS spectra were recorded to analyze their elemental composition differences. The PLS, SVM, and PLS-SVM models were separately built for the prediction of gelatin adulteration ratios, and the hybrid PLS-SVM model yielded a better performance than only the PLS and SVM models. Besides, four different variable selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), random frog (RF), and principal component analysis (PCA), were adopted to combine with the SVM model for comparative study; the results further demonstrated that the PLS-SVM model was superior to the other SVM models. This study reveals that the hybrid PLS-SVM model, with the advantages of low computational time and high prediction accuracy, can be employed as a preferred method for the accurate estimation of edible gelatin adulteration.

scholarly journals Comparison of different calibration techniques of laser induced breakdown spectroscopy in bakery products: on NaCl measurement

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Gonca Bilge ◽  
Kemal Efe Eseller ◽  
Halil Berberoglu ◽  
Banu Sezer ◽  
Ugur Tamer ◽  
...  
Keyword(s):  
Atomic Emission ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Laser Induced Breakdown Spectroscopy ◽  
Bakery Products ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Artificial Neural Network Ann ◽  
Calibration Techniques

AbstractLaser induced breakdown spectroscopy (LIBS) is a rapid optical spectroscopy technique for elemental determination, which has been used for quantitative analysis in many fields. However, the calibration involving atomic emission intensity and sample concentration, is still a challenge due to physical-chemical matrix effect of samples and fluctuations of experimental parameters. To overcome these problems, various chemometric data analysis techniques have been combined with LIBS technique. In this study, LIBS was used to show its potential as a routine analysis for Na measurements in bakery products. A series of standard bread samples containing various concentrations of NaCl (0.025%–3.5%) was prepared to compare different calibration techniques. Standard calibration curve (SCC), artificial neural network (ANN) and partial least square (PLS) techniques were used as calibration strategies. Among them, PLS was found to be more efficient for predicting the Na concentrations in bakery products with an increase in coefficient of determination value from 0.961 to 0.999 for standard bread samples and from 0.788 to 0.943 for commercial products.

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