Interval combination iterative optimization approach coupled with SIMPLS (ICIOA-SIMPLS) for quantitative analysis of surface-enhanced Raman scattering (SERS) spectra

Zhu, Junbo; Ahmad, Waqas; Jiao, Tianhui; Wang, Jingjing; Jiang, Hui; Li, Huanhuan; Chen, Quansheng

doi:10.1016/j.aca.2020.01.018

Cited by 13 publications

(4 citation statements)

References 32 publications

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“…Variable combination population analysis (VCPA) is a recent wavelength‐choosing algorithm, developed by a Chinese research team 24 . The basic theory of the method is that optimized spectral characteristic wavelengths are selected by running exponentially decreasing function (EDF) 25 and binary matrix sampling (BMS) 26 in multiple iterations. The best variable subset is acquired in terms of the minimum RMSECV value 20 .…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Dianhong black tea quality using near‐infrared hyperspectral imaging technology

et al. 2020

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BACKGROUND Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS Two matrix statistical algorithms encompassing a gray‐level co‐occurrence matrix (GLCM) and a gradient co‐occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near‐infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry

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Section: Methodsmentioning

confidence: 99%

Evaluation of Dianhong black tea quality using near‐infrared hyperspectral imaging technology

et al. 2020

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“…The application of chemometrics to spectroscopic techniques in particular Raman and SERS has been extensively surveyed (Chisanga et al., 2020; Clément et al., 2019; Hassan et al., 2019; Liu et al., 2020; Villa et al., 2020; Zhu et al., 2020) not only in laboratory but is also exploited in industry to examine and monitor the quality standards for various analytes particularly in food matrix. Multivariate calibration methods might be classified based on certain factors and criteria, such as linear and nonlinear methods and algorithms, rigid or flexible methods, classical or inverse methods, and direct or indirect methods.…”

Section: Chemometrics Used For Food Samples In Sers and Raman Spectroscopymentioning

confidence: 99%

Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy

Wang

Chen

Belwal

et al. 2021

Comp Rev Food Sci Food Safe

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Raman spectroscopy and surface‐enhanced Raman spectroscopy (SERS) have been extensively explored in the design of accurate, transparent, and conclusive food safety and quality control assays. Its hyphenation with chemometric algorithms is instrumental in securing safe food campaigns. To provide valuable recommendations and meet the growing demands for food screening, the current study begins with a brief description of the Raman spectroscopy and SERS theory followed by a comprehensive overview of spectral preprocessing, qualitative algorithms, variable selection methods, and quantitative algorithms. The review emphasizes on the importance of food monitoring practices using multivariate regression models. The applicability of the distinct chemometrics modes toward monitoring pesticide, food and illicit additives, heavy metals, pathogens, and its metabolites in Raman spectroscopy and SERS is covered in dairy, poultry, oil, honey, beverages, and other selected food matrices. Its pertinence toward classification and/or discrimination in food quality and safety monitoring and authentication is examined. Finally, it also complies with the limitations, key challenges, and prospects. The chemometrics processing spectra implemented with simpler or no complicated sample pretreatment step make Raman spectroscopy/SERS technique a potential approach that is expected to achieve simultaneous and fast detection of multiple analytes in food matrices.

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“…Hence, variable selection methods such as genetic algorithm (GA), competitive adaptive reweighted sampling algorithm, random frog algorithm (RF), and so on are the critical steps in multivariate calibration to minimize the effect on prediction efficiency. 33 In this paper, a dual-functional SERS chip was prepared using novel metal/semiconductor composite-Cu 2 O mesoporous spheres decorated with Ag NPs (MCu 2 O@Ag NPs) arrayed on a paper chip as a supporting substrate for the detection and photocatalytic degradation of pymetrozine and thiram in tea samples as displayed in Scheme 1. PLS, GA-PLS, and random frog-partial least squares (RF-PLS) were employed to reduce the dimensionality of SERS spectra data sets and build a cost-effective calibration model.…”

Section: Introductionmentioning

confidence: 99%

“…It is based on full spectra collected from samples that can influence the prediction ability of a system because of the presence of irrelevant information. Hence, variable selection methods such as genetic algorithm (GA), competitive adaptive reweighted sampling algorithm, random frog algorithm (RF), and so on are the critical steps in multivariate calibration to minimize the effect on prediction efficiency …”

Section: Introductionmentioning

confidence: 99%

Self-Cleaning-Mediated SERS Chip Coupled Chemometric Algorithms for Detection and Photocatalytic Degradation of Pesticides in Food

Hassan

Ali

et al. 2021

J. Agric. Food Chem.

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Pesticide residues in food have been a grave concern to consumers. Herein, we have developed a dual-mode SERS chip using Cu 2 O mesoporous spheres decorated with Ag nanoparticles (MCu 2 O@Ag NPs) as both sensing and degradation/ clearing unit for rapid detection of pymetrozine and thiram pesticides in tea samples. Three kinds of chemometric algorithms were comparatively applied to analyze the collected SERS spectra of pesticides. In comparison, random frog-partial least squares achieved the best performance with root mean square error of prediction and residual predictive deviation values of 0.9871, 6.17, and 0.9873, 6.64 for pymetrozine and thiram, respectively. Additionally, the prepared SERS chip showed great photocatalytic activity to degrade pesticides under visible light irradiation. Through a facile method, this work presented a novel dual-functional SERS chip for the rapid detection and degradation of low-concentration pesticides in both environmental and food samples.

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Interval combination iterative optimization approach coupled with SIMPLS (ICIOA-SIMPLS) for quantitative analysis of surface-enhanced Raman scattering (SERS) spectra

Cited by 13 publications

References 32 publications

Evaluation of Dianhong black tea quality using near‐infrared hyperspectral imaging technology

Evaluation of Dianhong black tea quality using near‐infrared hyperspectral imaging technology

Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy

Self-Cleaning-Mediated SERS Chip Coupled Chemometric Algorithms for Detection and Photocatalytic Degradation of Pesticides in Food

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