Analysis of multiple pesticide residues in rice by LC–MS/MS

Tauseef, Muhammad; Rafique, Nazia; Ahmad, Ijaz; Ishtiaq, Muhammad; Samad, Abdul; Saba, Sonia; Ahad, Karam; Mehboob, Farrakh

doi:10.1007/s11696-021-01533-x

Cited by 11 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, many methods have been developed for the detection of pesticide/fungicide residues in food, such as liquid chromatography-mass spectrometry (LC-MS/MS) (Tauseef et al, 2021;Zheng et al, 2023), gas chromatography-mass spectrometry (GC-MS) (Tankiewicz & Berg, 2022), etc. Although some of these methods have become national standard methods in examination of pesticides/fungicides, they also show limitation in the application due to their complex operation, special working environment and special sample storage conditions (Wang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface- enhanced Raman spectroscopy integrated with deep learning approach

Xuan

Liu

Dong

et al. 2023

Preprint

View full text Add to dashboard Cite

Chlorpyrifos and pyrimethanil are widely used insecticides/fungicides in agriculture. The residual pesticides/fungicides remaining in fruits and vegetables may do harm to human health if they are taken without notice by the customers. Therefore, it is important to develop methods and tools for the rapid detection of pesticides/fungicides in fruits and vegetables, which are highly demanded in the current markets. Surface-enhanced Raman spectroscopy (SERS) can achieve trace chemical detection, while it is still a challenge to apply SERS for the detection and identification of mixed pesticides/fungicides. In this work, we tried to combine SERS technique and deep learning spectral analysis for the determination of mixed chlorpyrifos and pyrimethanil on the surface of fruits including apples and strawberries. Especially, the multi-channel convolutional neural networks-gate recurrent unit (MC-CNN-GRU) classification model was used to extract sequence and spatial information in the spectra, so that the accuracy of the optimized classification model could reach 99% even when the mixture ratio of pesticide/fungicide varied considerably. This work therefore demonstrates an effective application of using SERS combined deep learning approach to rapid detection and identification of different mixed pesticides in agricultural products.

show abstract

Section: Introductionmentioning

confidence: 99%

Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface- enhanced Raman spectroscopy integrated with deep learning approach

Xuan

Liu

Dong

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Pesticide residue analyses are broadly classified into individual methods and multiclass multiresidue methods (MRMs) based on the purpose of the analysis [ 8 , 9 ]. Individual methods are highly reliable because the analysis is optimized for individual compounds; however, these methods are less frequently applied for pesticide residue analysis, owing to the time and cost required for detecting multiple pesticides [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Screening of 322 Residual Pesticides in Fruits and Vegetables Using GC-MS/MS and Deterministic Health Risk Assessments

Choi,

Lee,

Kim

et al. 2023

Foods

View full text Add to dashboard Cite

The development of efficient methods for evaluating pesticide residues is essential in order to ensure the safety and quality of agricultural products since the Republic of Korea implemented the Positive List System (PLS). The objective of this research was to establish a method for the simultaneous analysis of 322 pesticide residues in fruits and vegetables (such as coffee, potato, corn, and chili pepper), using the quick, easy, cheap, effective, rugged, and safe (QuEChERS) approach in combination with gas chromatography-tandem mass spectrometry (GC-MS/MS). This study introduces a robust, high-throughput GC-MS/MS method for screening the target pesticide residues in agricultural products, achieving the PLS criterion of 0.01 mg/kg LOQ. Despite some compounds not aligning with the CODEX recovery guideline, sufficient reproducibility was confirmed, attesting to the method’s applicability in qualitative analyses. A health risk assessment conducted using estimated daily intake/acceptable daily intake ratios indicated low risks associated with product consumption (<0.035391%), thereby confirming their safety. This efficient method holds significant implications for the safe distribution of agricultural products, including during import inspections.

show abstract

Assessment of residues from common pesticides and associated risks in Pakistan

Abbas,

Hussain

et al. 2024

Environ Monit Assess

View full text Add to dashboard Cite

Analysis of multiple pesticide residues in rice by LC–MS/MS

Cited by 11 publications

References 9 publications

Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface- enhanced Raman spectroscopy integrated with deep learning approach

Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface- enhanced Raman spectroscopy integrated with deep learning approach

Simultaneous Screening of 322 Residual Pesticides in Fruits and Vegetables Using GC-MS/MS and Deterministic Health Risk Assessments

Assessment of residues from common pesticides and associated risks in Pakistan

Contact Info

Product

Resources

About