Au@Ag nanostructure based SERS substrate for simultaneous determination of pesticides residue in tea via solid phase extraction coupled multivariate calibration

Hassan, Md. Mehedi; Li, Huanhuan; Ahmad, Waqas; Zareef, Muhammad; Wang, Jingjing; Xie, Shicai; Wang, Pingyue; Ouyang, Qin; Wang, Shaoyun; Chen, Quansheng

doi:10.1016/j.lwt.2019.02.016

Cited by 88 publications

(30 citation statements)

References 26 publications

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“…The R C and R P values were used for the performance assessment of the final models based on the computation and comparing correlation coefficients. Likewise, RMSEP and RMSECV as well as the ratio of standard deviations residual predictive deviation (RPD) was also used to assess the robustness of the final build model (Hassan et al, ; Khulal, Zhao, Hu, & Chen, ). The performance and better prediction ability correlates to higher R P and R C values of the prediction models and lower values of RMSEP and RMSECV.

RMSEC / RMSEP = \sqrt{\frac{\sum_{i = 1}^{n} {false({\overset{false^}{y}}_{i} - y_{i} false)}^{2}}{n}}

…”

Section: Resultsmentioning

confidence: 99%

Rapid screening of phenolic compounds in congou black tea ( Camellia sinensis ) during in vitro fermentation process using portable spectral analytical system coupled chemometrics

Zareef

Chen

Ouyang

et al. 2019

J Food Process Preserv

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Black tea composite contains a wide range of simple and complex phenolic compounds known for their antioxidant capabilities. Herein, a portable near‐infrared (NIR) spectroscopy (899–1724 nm) was used to predict the concentrations of cianidanol, ferulic acid, gallic acid, L‐epicatechin, phloridzin and rutin in congou black tea. High‐performance liquid chromatography‐diode array detector (HPLC‐DAD) was also utilized as a reference method. Models were predicted and constructed based on the data acquisition from portable NIR and HPLC‐DAD using partial least squares (PLS) followed by variables selection algorithms such as competitive adaptive reweighted sampling‐PLS and ant colony optimization‐PLS. The correlation coefficient of calibration (RC) and correlation coefficient of prediction (Rp) was found in ranged from 0.785 to 0.979 and 0.751 to 0.969, respectively. The overall results indicates portable NIR combined successfully with multivariate chemometrics offers a non‐destructive technique for the rapid screening of the phenolic compounds of congou black tea. Practical applications Phenolic compounds play a significant role or as a key indicator of black tea quality. To the best of our knowledge, this is the first study for monitoring these compounds during black tea fermentation. Current study reported a first time non‐invasive and non‐destructive method for determining the quality index of black tea with reasonable accuracy and that could be endorsed a model approach as it addresses more advantages over conventional methods. This study successfully predicted phenolic compounds in black tea during fermentation after using a portable near‐infrared spectroscopy coupled multivariate chemometrics. Furthermore, current work encourages fast, nondestructive quantification of various analyte in food and biological engineering as a quality and safety indicator.

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RMSEC / RMSEP = \sqrt{\frac{\sum_{i = 1}^{n} {false({\overset{false^}{y}}_{i} - y_{i} false)}^{2}}{n}}

…”

Section: Resultsmentioning

confidence: 99%

Rapid screening of phenolic compounds in congou black tea ( Camellia sinensis ) during in vitro fermentation process using portable spectral analytical system coupled chemometrics

Zareef

Chen

Ouyang

et al. 2019

J Food Process Preserv

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“…Thus, the Si‐PLS model is expected to be more robust, as compared to the PLS model . The GA was based on the mechanics of natural genetics and natural selection of variables . In GA‐PLS, first‐generation high‐fitness variables can be selected for the next generation.…”

Section: Methodsmentioning

confidence: 99%

“…19,20 The GA was based on the mechanics of natural genetics and natural selection of variables. 21 In GA-PLS, first-generation high-fitness variables can be selected for the next generation. The value 1 is added to its corresponding gene or zero is added, as determined by a variable included in the subset.…”

Section: Data Analysis Using Chemometric Algorithmsmentioning

confidence: 99%

Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms

Wang

Zareef

et al. 2019

J Sci Food Agric

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BACKGROUND The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality. RESULTS Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first‐order derivative (1stD) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF‐PLS), synergy interval partial least square (Si‐PLS), genetic algorithm‐partial least square (GA‐PLS) and competitive adaptive reweighted sampling partial least square (CARS‐PLS). RF‐PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (RP), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51–14.58% and 2.10–3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade. CONCLUSION The proposed method can be used as a rapid, accurate and non‐destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry

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“…Moreover, higher Rp and Rc values and lower RMSECV and RMSEP might be required to build an excellent prediction models, whereas for the prediction, minimal difference between each of the two pairs is required. The GA‐PLS is applied in a study (Hassan et al., 2019) for the SERS‐based determination of 2,4‐dichlorophenoxyacetic acid (2,4‐D) and acetamiprid (AC) pesticides in tea. The GA‐PLS model results were RPD of 6.23 and 6.53 and Rp of 0.9923 and 0.99243, respectively, indicating excellent performance for 2,4‐D and AC detection.…”

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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Au@Ag nanostructure based SERS substrate for simultaneous determination of pesticides residue in tea via solid phase extraction coupled multivariate calibration

Cited by 88 publications

References 26 publications

Rapid screening of phenolic compounds in congou black tea ( Camellia sinensis ) during in vitro fermentation process using portable spectral analytical system coupled chemometrics

Rapid screening of phenolic compounds in congou black tea ( Camellia sinensis ) during in vitro fermentation process using portable spectral analytical system coupled chemometrics

Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms

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

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