Quantitative analysis of thymine with surface-enhanced Raman spectroscopy and partial least squares (PLS) regression

Zhang, Lei; Li, Qingqing; Wei, Tao; Yu, Bohao; Du, Yukou

doi:10.1007/s00216-010-4074-0

Cited by 55 publications

(35 citation statements)

References 27 publications

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“…This inherent unstable background is mainly because of the emission of fluorescence [9]. Furthermore, some instrumental factors, like variations in laser power or wavelength, optical train variations or irreproducible sample placement, or the change of position and angel of Ag or Au sol attached on analyte molecules [31], may also contribute to unstable signals. Traditional PLSR only considers the intensity information of Raman signals without separating the Raman peaks from the unstable background, which affects the quantitative prediction accuracy.…”

Section: Limitations Of Plsrmentioning

confidence: 99%

“…Quantitative analysis of spectrum is also called spectroscopic calibration, which is mainly to determine the chemical or physical properties of an analyte (e.g., concentrations of pure components in the compound) from its measured spectrum. The state-of-the-art analysis method is partial least square regression (PLSR) [3], [6], [17], [24], [31], which is developed from partial least squares (PLS) method. A recent overview of PLS can be found in [22].…”

Section: Introductionmentioning

confidence: 99%

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Continuous Wavelet Transform Based Partial Least Squares Regression for Quantitative Analysis of Raman Spectrum

Nyagilo

Davé

et al. 2013

IEEE Trans.on Nanobioscience

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Quantitative analysis of Raman spectra using surface-enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in in vivo molecular imaging. Partial least square regression (PLSR) methods have been reported as state-of-the-art methods. However, the approaches fully rely on the intensities of Raman spectra and can not avoid the influences of the unstable background. In this paper we design a new continuous wavelet transform based PLSR (CWT-PLSR) algorithm that uses mixing concentrations and the average CWT coefficients of Raman spectra to carry out PLSR. We elaborate and prove how the average CWT coefficients with a Mexican hat mother wavelet are robust representations of Raman peaks, and the method can reduce the influences of unstable baseline and random noises during the prediction process. The algorithm was tested using three Raman spectra data sets with three cross-validation methods in comparison with current leading methods, and the results show its robustness and effectiveness.

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Section: Limitations Of Plsrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continuous Wavelet Transform Based Partial Least Squares Regression for Quantitative Analysis of Raman Spectrum

Nyagilo

Davé

et al. 2013

IEEE Trans.on Nanobioscience

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“…This supervised quantification method is effective for our application because the AcAm is the only analyte in our case. More sophisticated and unsupervised methods such as principal component analysis (PCA) and partial least squares (PLS) can be applied to multi-analyte situations [30,31], but we do not attempt those here. The noise level highlighted in cyan is the intensity of the blank sample.…”

Section: Instrumentationmentioning

confidence: 99%

Trace cancer biomarker quantification using polystyrene-functionalized gold nanorods

Hajisalem

et al. 2014

Biomed. Opt. Express

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Abstract:We demonstrate the application of polystyrene-functionalized gold nanorods (AuNRs) as a platform for surface enhanced Raman scattering (SERS) quantification of the exogenous cancer biomarker Acetyl Amantadine (AcAm). We utilize the hydrophobicity of the polystyrene attached to the AuNR surface to capture the hydrophobic AcAm from solution, followed by drying and detection using SERS. We achieve a detection limit of 16 ng/mL using this platform. This result shows clinical potential for low-cost early cancer detection.

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“…Raman data usually contain a large amount of variables, thus requiring the analysis by robust mathematical models. For spectral analysis, principal components analysis (PCA), partial least squares (PLS), support vector machine (SVM), and artificial neural network (ANN) have been extensively applied . Among them, PCA is a well‐established method for extraction and dimensionality reduction .…”

Section: Introductionmentioning

confidence: 99%

Detection of glioma by surface‐enhanced Raman scattering spectra with optimized mathematical methods

Sun

Fang

Zhang

et al. 2019

J Raman Spectroscopy

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This work aimed to establish a fast and accurate method to detect glioma by combining surface‐enhanced Raman scattering (SERS) and mathematical analysis. At first, 785‐nm laser was selected as the optimum laser to acquire Raman spectra of human brain tissue. Second, it was verified that Raman data in the range of 1,200–1,600 cm−1 could improve the performance of classifier. Based on the analytical results of 1,200–1,600 cm−1 data, the sensitivity and specificity of partial least square (PLS) analysis and back‐propagation neural network (BPNN) were as high as 100%, whereas the sensitivity and specificity of support vector machine (SVM) were 96% and 100%, respectively. Among them, PLS was more potential in the detection of glioma, because of its lower computational cost compared with SVM and BPNN. Moreover, the correlation between observed Raman peaks and 2‐hydroxyglutarate (2HG; 512, 790, 1,204, 1,302, and 1,463 cm−1) was observed, suggesting 2HG as a potential marker of glioma using its Raman spectroscopic signatures. After all, SERS combining with mathematical analysis could be a promising tool for the accurate detection of glioma.

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Quantitative analysis of thymine with surface-enhanced Raman spectroscopy and partial least squares (PLS) regression

Cited by 55 publications

References 27 publications

Continuous Wavelet Transform Based Partial Least Squares Regression for Quantitative Analysis of Raman Spectrum

Continuous Wavelet Transform Based Partial Least Squares Regression for Quantitative Analysis of Raman Spectrum

Trace cancer biomarker quantification using polystyrene-functionalized gold nanorods

Detection of glioma by surface‐enhanced Raman scattering spectra with optimized mathematical methods

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