The rapid quantitative analysis of three pesticides in cherry tomatoes and red grape samples with Tchebichef image moments. (30th August 2019)

• Record Type: Journal Article
• Authors: Lu, Shao Hua; Li, Sha Sha; Yin, Bo; Mi, Jia Ying; Zhai, Hong Lin
• Is Part Of: Food chemistry. Volume 290(2019)
• Page Start: 72
• Page End: 78
• Publication Date: 2019-08-30
• Subjects: Thiabendazole (PubChem CID: 5430) -- Thiophanate-methyl (PubChem CID: 3032791) -- 2-Aminobenzimidazole (PubChem CID: 13624); Sample substrate -- Pesticides -- Quantitative analysis -- HPLC equipped with diode array detection (HPLC-DAD) -- Tchebichef image moment (TM); Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2019.03.118
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: The quantitative analysis of three pesticides in different substrates was carried out. TM method could eliminate the serious interferences without any pretreatment. Unified quantitative models were established on the different substrates. Compared with N-PLS and MCR-ALS, our method have more accuracy and reliability. The proposed approach can improve the analytical efficiency of HPLC. Abstract: Diverse interferences often affect the determination of pesticide residues in various kinds of food, and complex pretreatments are necessary. An effective approach for the simultaneous quantitative analysis of multi-target components in different substrates was proposed, and applied to the determination of ternary pesticides in cherry tomatoes and red grape samples. By means of HPLC-DAD, the spectra were obtained under isocratic elution. Utilizing the Tchebichef image moments, unified quantitative models were established for the three target components in the samples with two different substrates, respectively. The correlation coefficients of leave-one-out cross-validation ( R loo - c v 2 ) of the obtained models were more than 0.9975. The predictive correlation coefficients ( R p 2 ) were more than 0.9831. Compared with the N-PLS and MCR-ALS methods, the proposed approach is not only more accurate and reliable, but also can extract essential information and expected to be a potential tool to rapidly quantify multi-component in different substrates without complex pretreatments.