Hair analysis for diagnosis of addiction by Laser Induced Breakdown Spectroscopy (LIBS) combined with Partial Least Square Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) models

2017 ◽  
Author(s):  
Zahra Derakhshan Zadeh ◽  
Seyyed Jabbar Mousavi ◽  
Hassan Ranjbar Askari ◽  
Seyyed Mohammad Reza Darbani
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Hair Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

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.

Quantitative and classification analysis of slag samples by laser induced breakdown spectroscopy (LIBS) coupled with support vector machine (SVM) and partial least square (PLS) methods

2015 ◽  
Vol 30 (2) ◽  
pp. 368-374 ◽  
Author(s):  
Tianlong Zhang ◽  
Shan Wu ◽  
Juan Dong ◽  
Jiao Wei ◽  
Kang Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Partial Least Square ◽  
Least Square ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Classification Analysis ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

A laser induced breakdown spectroscopy (LIBS) technique coupled with SVM and PLS was proposed to perform quantitative and classification analysis of 20 slag samples.

scholarly journals Discrimination of Melanoma Using Laser-Induced Breakdown Spectroscopy Conducted on Human Tissue Samples

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Muhammad Nouman Khan ◽  
Qianqian Wang ◽  
Bushra Sana Idrees ◽  
Geer Teng ◽  
Xutai Cui ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Laser Induced Breakdown Spectroscopy ◽  
Chemometric Methods ◽  
Tissue Samples ◽  
Linear Discriminant ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Signal Intensities

Discrimination and identification of melanoma (a kind of skin cancer) by using laser-induced breakdown spectroscopy (LIBS) combined with chemometrics methods are reported. The human melanoma and normal tissues are used in the form of formalin-fixed paraffin-embedded (FFPE) blocks as samples. The results demonstrated higher LIBS signal intensities of phosphorus (P), potassium (K), sodium (Na), magnesium (Mg), and calcium (Ca) in melanoma FFPE samples while lower signal intensities in normal FFPE tissue samples. Chemometric methods, artificial neural network (ANN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least square discriminant analysis (PLS-DA) are used to build the classification models. Different preprocessing methods, standard normal variate (SNV), mean-centering, normalization by total area, and autoscaling, were compared. A good performance of the model (sensitivity, specificity, and accuracy) for melanoma and normal FFPE tissues has been achieved by the ANN and PLS-DA models (all were 100%). The results revealed that LIBS combined with chemometric methods for detection and discrimination of human malignancies is a reliable, accurate, and precise technique.

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 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.

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