假阳性悖论
色谱法
农药残留
杀虫剂
液相色谱-质谱法
基质(化学分析)
质谱法
化学
样品制备
探索者
污染
复矩阵
样品(材料)
分析物
计算机科学
人工智能
生物
农学
生态学
作者
Rossana Scarpone,Roberta Rosato,Francesco Chiumiento,Chiara Cipolletti,Manuel Sergi,Darío Compagnone
标识
DOI:10.1007/s12161-020-01727-1
摘要
Abstract This preliminary study describes the use of high resolution and accuracy mass spectrometry techniques combined with new generation chemical software products for detecting and identifying contaminants in food commodities. As a first step, the extracts of routine target analysis samples (obtained in our official laboratory responsible for food residues control) were acquired and processed with this method in order to search unknown and non-targeted contaminants in food. In order to verify the feasibility of the presented method, the research has been firstly addressed to untargeted pesticides and their metabolites in stone fruits commodities and tomatoes. The differential analysis carried with Compound Discoverer 2.0 between the investigated unknown sample and the blank matrix sample allowed to remove all the matrix molecular components; Aggregated Computational Toxicology Resource (ACToR) helped to understand and predict chemical interpretation of substances. The acquisition in FullScan-AIF and FullScan-ddMS2 allowed the clear detection and identification of isobaric compounds such as quinalphos and phoxim. In order to verify that the proposed method is suitable to the scope of application, the main points of SANTE/11813/2017 Document have been followed. The results demonstrate that no false positives and no false negatives have been detected from the analysis of samples spiked with 55 pesticides at 0.010 and 0.10 mg kg −1 . This preliminary study has been also tested with a Proficiency Test (EUPT-FV-SM08) and, according to EUPT-FV-SM08 Final Report, our laboratory has been included in the 67% (56) that clearly detected over 70% pesticides. Finally, this method has been extended to other matrices and contaminants.
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