Improved PLS regression based on SVM classification for rapid analysis of coal properties by near-infrared reflectance spectroscopy

Wang, Yasheng; Yang, Meng; Gao, Wei; Hu, Ruifen; Luo, Zhiyuan; Li, Guang

doi:10.1016/j.snb.2013.12.028

Cited by 60 publications

(29 citation statements)

References 30 publications

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“…The PLS-SVM is the combination of the PLS and the SVM. (23)(24)(25) First, PLS is used for data analysis and processing; then, the training samples are disposed of with the SVM to obtain the optimal hyperplane and classify them.…”

Section: Pls-svmmentioning

confidence: 99%

Partial Least Square-Support Vector Machine for Rapid Detection of Egg Storage Life by Chemometric Processing of Voltammetric Signals

2016

Sensors and Materials

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The rapid detection of the grade and storage life of eggs is important for consumers and producers. In this study, an electrochemical system based on voltammetry was used for the rapid detection of egg yolk and egg white mixtures for classification and prediction of storage days. Measuring the Haugh units (H.U.) index of Lindian eggs is aimed at identifying the grades of these eggs and the corresponding storage days. Discriminant function analysis was used to identify accurately the grades of eggs on the basic of storage life. The Gaussian kernel function and a suitable penalty factor were selected to establish a support vector machine for predicting the storage days of eggs. By avoiding deviation in the result due to the correlation of data, which was acquired by square-wave voltammetry technology, the partial least squares and support vector machine were combined, and the predictive accuracy of egg storage days was improved by up to 97.14%. A tenfold cross-validation has been used to evaluate the classification performance of the model; the average accuracy of prediction of storage life was 88.57%.

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Section: Pls-svmmentioning

confidence: 99%

Partial Least Square-Support Vector Machine for Rapid Detection of Egg Storage Life by Chemometric Processing of Voltammetric Signals

2016

Sensors and Materials

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“…According to the properties of coal, they chose the optimal wave band (1680, 1942, 2100, 2180, 2300nm) for modeling and the on-line detection of coal analysis was realized. Yasheng Wang [9] analyzed the quality of coal by NIRS and built Near-infrared spectroscopy regression model of PLS, The results showed that the accuracy of Fixed carbon and volatile was high, but the accuracy of Moisture, ash, sulfur was not high. Zhongsheng Li and Peter M. Fredericks [10] of Australia achieved a chemical efficacy testing of Low-sulfur coal by use of NIRS.…”

Section: The Research Progress Of Spectral Imaging In the Detectiomentioning

confidence: 99%

Application of Infrared Spectroscopy in the Detection of Coal Quality of Power Plant

Liu¹,

Zhao²,

Zhang³

et al. 2015

Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering

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Spectrum technology in coal quality detection has become a research hotspot with the maturing of spectral technology and the continuous development of spectral imager. This article mainly analyzed and summarized the progress and research status of scholars at home and abroad based on the coal quality index (moisture, ash content, volatile matter, fixed carbon and sulfur content).According to the coal quality index, we demonstrated spectrum technology has considerable prospects in coal quality testing by analysis the application of spectrum technology in detection of other material's corresponding index.

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“…Recently, near infrared reflectance spectra (NIRS) has become an effective method for rapid and real-time analysis of fuel properties [3][4][5][6][7][8][9][10][11][12][13]. Organic functional groups that contain hydrogen can form broad absorption bands within near infrared wavelength range.…”

Section: Introductionmentioning

confidence: 99%

“…For quantitative analysis, the primary method is partial least square (PLS) regression [10,[14][15][16]. Support vector machine (SVM) has been gradually applied to near-infrared spectra in qualitative analysis in recent years [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid analysis of diesel fuel properties by near infrared reflectance spectra

Feng

Zeng

2015

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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Improved PLS regression based on SVM classification for rapid analysis of coal properties by near-infrared reflectance spectroscopy

Cited by 60 publications

References 30 publications

Partial Least Square-Support Vector Machine for Rapid Detection of Egg Storage Life by Chemometric Processing of Voltammetric Signals

Partial Least Square-Support Vector Machine for Rapid Detection of Egg Storage Life by Chemometric Processing of Voltammetric Signals

Application of Infrared Spectroscopy in the Detection of Coal Quality of Power Plant

Rapid analysis of diesel fuel properties by near infrared reflectance spectra

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