Rapid detection of chlorpyrifos pesticide residue in tea using surface-enhanced Raman spectroscopy combined with chemometrics

Zhu, Xiaoyu; Li, Wenjin; Wu, Ruimei; Liu, Peng; Hu, Xiao; Xu, Lulu; Xiong, Zhengwu; Wen, Yangping; Ai, Shirong

doi:10.1016/j.saa.2020.119366

Cited by 33 publications

(23 citation statements)

References 35 publications

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“…However, a better comparison is to focus on detection methods that are also simple, easy to perform, cheap, and suited for on-site application. In that perspective, the newly developed LFIA is rather sensitive compared to an enzymatic paper-based device based on the lipase enzyme combined with smartphone readout, which showed an LOD of 65 µg/L [ 35 ]. When compared to an existing LFIA for the detection of CHLP, based on a direct format with gold reporter molecules, the newly developed indirect CHLP LFIA was slightly more sensitive (factor 10) [ 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…Even though this method may already be considered as rapid, the sample extraction procedure is still not suitable for rapid point of need approaches. Sankar et al developed a paper-based device for the rapid detection of CHLP in water samples based on the inhibition of lipase enzymatic activity, enabling on-site use [ 35 ]. This assay was combined with smartphone readout and had an incubation time of 15 min, but the limit of quantification of 200 µg/L is rather high.…”

Section: Introductionmentioning

confidence: 99%

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Biosensing Chlorpyrifos in Environmental Water Samples by a Newly Developed Carbon Nanoparticle-Based Indirect Lateral Flow Assay

Willemsen

Wichers

et al. 2022

Biosensors

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Pesticides are used in agriculture to prevent pests. Chlorpyrifos (CHLP) is an insecticide with potentially detrimental effects on humans, bees, and the aquatic environment. Its effects have led to a total ban by the European Union (EU), but outside the EU, CHLP is still produced and used. An indirect lateral flow immunoassay (LFIA) for the detection of CHLP was developed and integrated into a cassette to create a lateral flow device (LFD). Species-specific reporter antibodies were coupled to carbon nanoparticles to create a detector conjugate. Water samples were mixed with a specific CHLP monoclonal antibody and detector conjugate and applied to the LFD. Dose-response curves elicited the detection of low concentrations of CHLP (<1 µg/L). This sensitivity was recorded through a rapid handheld digital imaging device but also visually by naked eye. The CHLP LFD was applied to a range of European surface water samples, fortified with CHLP, revealing a sensitivity in these matrices of 2 µg/L, both by digital and visual analysis. To improve the simplicity of the CHLP LFIA, the assay reagents were dried in tubes, enabling to carry out the test by simply adding water samples and inserting the LFIA strips. This CHLP LFIA is thus suited for the on-site screening of surface waters.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biosensing Chlorpyrifos in Environmental Water Samples by a Newly Developed Carbon Nanoparticle-Based Indirect Lateral Flow Assay

Willemsen

Wichers

et al. 2022

Biosensors

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“…It can be seen from the figure that, due to differences in the quality and internal structure of tea, different types of tea show different fluorescence intensity. The fluorescence intensity of 500-650 nm is not high, which may be explained by the high uptake of chlorophyll and carotenoids [33]. In addition, there are two clear peaks, about 690 nm and 735 nm, respectively [34].…”

Section: Fluorescence Hyperspectral Data Extractionmentioning

confidence: 92%

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Huang

et al. 2021

Agriculture

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A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

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“…insecticides, fungicides, and herbicides) during the growth of crops to ensure high production and quality 10 . During the period of tea growth and harvest, various of pesticides are applied to prevent injurious insect to guarantee the production and cultivation 11,12 . Chlorpyrifos is a kind of broad spectrum, efficient, moderate toxicity, and long residual effect period of organophosphorus pesticides, has a good stomach toxicity and contact action, it is mainly used for the prevention and treatment of cotton, vegetables, tea, fruit and crops on the harmful insects and mites [13][14][15] .…”

mentioning

confidence: 99%

“…Currently. some classical analytical methods such as highperformance liquid chromatography 22 , liquid chromatography-mass spectrometry 23,24 , gas chromatography-mass spectrometry (GC-MS) 25,26 were used to analyze pesticide residue in tea 11 . But, the factors such as low concentration of the analytes and the presence of different interferences in the matrices of samples limit the direct application of these instruments despite of their high sensitivity 27 .…”

mentioning

confidence: 99%

Development of a new and facile method for determination of chlorpyrifos residues in green tea by dispersive liquid–liquid microextraction

Tian

Feng

Yang

et al. 2022

Sci Rep

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In this work a simple, rapid, and environmentally friendly method has been established for the determination of chlorpyrifos residue in green tea by dispersive liquid–liquid microextraction and gas chromatography-flame photometric detection. Some experimental parameters that influence extraction efficiency, such as the kind and volume of disperser solvents and extraction solvents, extraction time, addition of salt and pH, were investigated. And the optimal experimental conditions were obtained, quantitative analysis was carried out using external standard method. The correlation coefficient of the calibration curves was 0.999 with in 0.05 mg/kg to 5 mg/kg. The results showed that under the optimum conditions, the enrichment factors of the chlorpyrifos was about 554.51, the recoveries for standard addition fell in the range from 91.94 to 104.70% and the relative standard deviations was 4.61%. The limit of quantification of chlorpyrifos in green tea was 0.02 μg/mL at the signal/noise ratio of 3.

show abstract

Rapid detection of chlorpyrifos pesticide residue in tea using surface-enhanced Raman spectroscopy combined with chemometrics

Cited by 33 publications

References 35 publications

Biosensing Chlorpyrifos in Environmental Water Samples by a Newly Developed Carbon Nanoparticle-Based Indirect Lateral Flow Assay

Biosensing Chlorpyrifos in Environmental Water Samples by a Newly Developed Carbon Nanoparticle-Based Indirect Lateral Flow Assay

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Development of a new and facile method for determination of chlorpyrifos residues in green tea by dispersive liquid–liquid microextraction

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