Research on Infrared Spectral Quantitative Analysis of Hydrocarbon Gases Based on Adaptive Boosting Classifier and PLS

Liang, Haibo; Liu, Suqi; Li, Zhongbing; Guo, Jinhong; Jiang, Yingjun

doi:10.1109/jsen.2021.3096956

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Generally, the dimension of independent variables is reduced in advance, and it is hoped that fewer features are used to express the original data, to make the constructed model simpler and the results more accurate and precise, and PCA is a widely used method. The rapid screening and identification of contaminants in food contact materials is another important approach with the help of data mining technology, among which, PCA has been widely adopted as a favorable tool for data mining ( Liang et al, 2021 ). PCA can perform dimensionality reduction on big data so that useful information in the data can be quickly extracted and classified.…”

Section: Related Workmentioning

confidence: 99%

Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy

et al. 2022

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Surface-enhanced Raman spectroscopy (SERS) has attracted much attention because of its high sensitivity, high speed, and simple sample processing, and has great potential for application in the field of pesticide residue detection. However, SERS is susceptible to the influence of a complex detection environment in the detection of pesticide residues on the surface of fruits, facing problems such as interference from the spectral peaks of detected impurities, unclear dimension of effective correlation data, and poor linearity of sensing signals. In this work, the enhanced raw data of the pesticide thiram residues on the fruit surface using gold nanoparticle (Au-NPs) solution are formed into the raw data set of Raman signal in the IoT environment of Raman spectroscopy principal component detection. Considering the non-linear characteristics of sensing data, this work adopts kernel principal component analysis (KPCA) including radial basis function (RBF) to extract the main features for the spectra in the ranges of 653∼683 cm−1, 705∼728 cm−1, and 847∼872 cm−1, and discusses the effects of different kernel function widths (σ) to construct a qualitative analysis of pesticide residues based on SERS spectral data model, so that the SERS spectral data produce more useful dimensionality reduction with minimal loss, higher mean squared error for cross-validation in non-linear scenarios, and effectively weaken the interference features of detecting impurity spectral peaks, unclear dimensionality of effective correlation data, and poor linearity of sensing signals, reflecting better extraction effects than conventional principal component analysis (PCA) models.

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Section: Related Workmentioning

confidence: 99%

Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy

et al. 2022

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“…Liu also analyzed the application of infrared absorption spectroscopy gas quantification methods in oil and gas exploration in 2012 [2]. Song and Liang et al analyzed the recent research progress of infrared spectroscopy analysis in oil and gas exploration [3,4]. Tawalbeh and Voelz designed a tunable spectrometer for geological investigation and analyzed the superiority of infrared spectral analysis in geological investigation [5].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Alkane Gases by Infrared Absorption Spectrometry Based on Spectral Comprehensive Pretreatment Method

Gan

Wang

Tang

et al. 2022

Advances in Transdisciplinary Engineering

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Infrared absorption spectroscopy for quantitative analysis of alkane gases has been widely used in the fields of energy exploration, geological survey, coal-bed methane analysis, and atmospheric environment monitoring. With the rapid development of science and technology, various research fields have put forward higher requirements on the detection accuracy and detection limits of gas quantitative analysis. Spectral pretreatment is very important in the gas quantitative analysis of infrared absorption spectroscopy, which can effectively improve the detection accuracy, but there were few studies on the spectral pretreatment technology at this stage. In this paper, a comprehensive spectral pretreatment method based on spectral smoothing and noise reduction, spectral deconvolution and spectral baseline drift correction was proposed for the infrared absorption spectra of alkane gases. The optimal pretreatment parameters for alkane gases were investigated and the infrared absorption spectroscopy of typical alkane gases was pre-processed. The comprehensive pretreatment method of absorption spectroscopy can increase the probability of selecting variables with significant features in absorption spectral data, which can improve the accuracy of quantitative analysis. It also can improve the quality of some weak absorption signals and extend the lower detection limit downward in order to expand the detection range.

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A low-cost calibration method for the infrared sensor array for quantitative analysis of natural gas

Wang

Lei

Yang

et al. 2022

Chemometrics and Intelligent Laboratory Systems

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Research on Infrared Spectral Quantitative Analysis of Hydrocarbon Gases Based on Adaptive Boosting Classifier and PLS

Cited by 4 publications

References 24 publications

Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy

Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy

Quantitative Analysis of Alkane Gases by Infrared Absorption Spectrometry Based on Spectral Comprehensive Pretreatment Method

A low-cost calibration method for the infrared sensor array for quantitative analysis of natural gas

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