Application of surface enhanced Raman scattering and competitive adaptive reweighted sampling on detecting furfural dissolved in transformer oil

Chen, Weigen; Zou, Jian; Wan, Fu; Zhang, Fan; Yang, Dingkun

doi:10.1063/1.5012685

Cited by 20 publications

(3 citation statements)

References 29 publications

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“…The main steps of UVE algorithm can be found in previous literatures (Li, Zhong, Lou, Wei, & Hou, 2017;Ye et al, 2008). CARS is an innovative algorithm for selecting optimal wavelengths (Chen, Zou, Wan, Fan, & Yang, 2018;Guo et al, 2014). The wavelengths with large absolute coefficients in a PLSR model were regarded as optimal wavelengths.…”

Section: Selection Methods Of Characteristic Wavelengthsmentioning

confidence: 99%

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Liu

Wang

Gao

et al. 2019

J Food Process Engineering

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Hyperspectral imaging (HSI) method was applied to rapidly and nondestructively predict total phenolic content in Flos Lonicerae. The least squares support vector machine (LS‐SVM) and partial least squares regression (PLSR) models were developed on the basis of full wavelengths data and characteristic wavelengths data chosen by six wavelengths selection ways. The results clarified that standard normal variable (SNV) was the optimal pretreatment method, and the nonlinear LS‐SVM model based on the characteristic wavelengths chosen by the combination (CARS‐SPA) of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) yielded the best prediction performance for the total phenolic content. The overall results demonstrated that the proposed CARS‐SPA was a good method for selecting characteristic wavelengths to enhance prediction performance of HSI, and nonlinear LS‐SVM was more appropriate than linear PLSR for the prediction of total phenolic content in Flos Lonicerae. Practical applications Flos Lonicerae is a well‐known traditional Chinese medicinal herb, and also the raw material of various medicines and herbal tea. The demand for Flos Lonicerae is increasing. Therefore, monitoring the quality of Flos Lonicerae is very important to consumers and industry of Flos Lonicerae. The phenolic compounds content in Flos Lonicerae is one of the key internal quality factors. The traditional methods (such as spectrophotometric techniques or high‐performance liquid chromatography) for detecting total phenolic content are time‐consuming, labor‐consuming, and destructive. Hyperspectral imaging (HSI) is a nondestructive and reliable method for quality inspection. The results showed that SNV was determined as the best method for pretreatment, and the nonlinear CARS‐SPA‐LS‐SVM model was developed for total phenolic content detection with high precision. It could be verified that HSI technology is effective and promising for rapid determination of total phenolic content and quality control of Flos Lonicerae as well as other agricultural products.

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Section: Selection Methods Of Characteristic Wavelengthsmentioning

confidence: 99%

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Liu

Wang

Gao

et al. 2019

J Food Process Engineering

View full text Add to dashboard Cite

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“…The regression coefficient method (RC) [41] , stepwise [42] , successive projection algorithm (SPA) [43] , competitive adaptive reweighted sampling (CARS) [44], and uninformative variable elimination (UVE) [45] were used to select the optimal wavelengths. These five methods are widely used in the selection of spectral model variables, which can achieve variable dimensionality reduction during the modeling process, thereby reducing the singularity and instability of the original spectral data.…”

Section: Optimal Wavelength Selection and Model Optimizationmentioning

confidence: 99%

Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics

Wang

Jiang

et al. 2022

International Journal of Agricultural and Biological Engineering

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This study was conducted to investigate the potential of hyperspectral imaging technique (900-1700 nm) for nondestructive determination of inosinic acid (IMP) in chicken. Hyperspectral images of chicken flesh samples were acquired, and their mean spectra within the images were extracted. The quantitative relationship between the mean spectra and reference IMP value was fitted by partial least squares (PLS) regression algorithm. A PLS model (MAS-PLS) built with moving average smoothing (MAS) spectra showed better performance in predicting IMP content, leading to correlation coefficients (R P ) of 0.951, root mean square error (RMSEP) of 0.046 mg/g, and residual predictive deviation (RPD) of 3.152. Regression coefficient (RC), successive projections algorithm (SPA), stepwise, competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) were used to select the optimal wavelengths to optimize the MAS-PLS model.

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“…In recent years, Raman spectroscopy combined with multivariate analysis algorithm has been widely used in the field of medical quantification, such as support vector machine (SVM) [12], artificial neural network (ANN) [13] and partial least squares (PLS) [14] have been used for quantitative detection of biological content. In this paper, Raman spectroscopy combined with optimized PLS prediction model is used to quantitatively analyze HBV DNA in hepatitis B serum.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of hepatitis B virus DNA based on raman spectroscopy combined with multivariate statistical methods

Du¹,

Lv²,

Li³

et al. 2020

Laser Phys. Lett.

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Quantitation of hepatitis B virus deoxyribonucleic acid (HBV DNA) is the most accurate method for determining the infection level of hepatitis B. It is a very meaningful means of detecting the different replication states of HBV infection. In this study, HBV DNA was detected and quantified in 499 patients with hepatitis B by confocal Raman spectroscopy combined with multivariate analysis. The effective information related to HBV DNA in spectral data was extracted by different pretreatments according to the vibrational modes of different functional groups. Quantitative analysis of HBV DNA was carried out by using the spectrum in the characteristic range of 807.027-1700.29 cm −1 for spectral peak assignment analysis and using partial least squares to construct a quantitative model. The results show that the regression model is established by combining normalization pretreatment and multivariate analysis methods. The correlation coefficient (R c ) and root mean square error of calibration from the calibration set of the selected feature range are 0.9751 and 0.3881, and the correlation coefficient (R p ) of the verification set and the root mean square error of prediction from the prediction set are 0.9673 and 0.4314. respectively. In clinical medical applications, Raman spectroscopy combined with diversified analysis methods has great research potential for the quantitative detection of HBV DNA in HBV serum.

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Application of surface enhanced Raman scattering and competitive adaptive reweighted sampling on detecting furfural dissolved in transformer oil

Cited by 20 publications

References 29 publications

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics

Quantitative analysis of hepatitis B virus DNA based on raman spectroscopy combined with multivariate statistical methods

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