A suspect screening strategy with automated data processing tools for the comprehensive detection of emerging chemical hazards in food

In a rapidly evolving agri-food landscape fraught with unforeseen chemical risks, the establishment of proactive analytical strategies become paramount to safeguard food safety and public health. Leveraging ultra-high-performance liquid chromatography high resolution tandem mass spectrometry (UHPLC-...

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Vydáno v:Food control Ročník 163; s. 110538
Hlavní autoři: Lim, Hui Yi, Yu, Dingyi, Chan, Sheot Harn, Li, Angela
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2024
Témata:
Automated data processing
automation
Comprehensive
Emerging contaminants
food contamination
Food safety
landscapes
Non-targeted analysis
prioritization
public health
Suspect screening
tandem mass spectrometry
ultra-performance liquid chromatography
user interface
Automated data processing
Food safety
Suspect screening
Comprehensive
Non-targeted analysis
Emerging contaminants
ISSN:0956-7135, 1873-7129
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Shrnutí:In a rapidly evolving agri-food landscape fraught with unforeseen chemical risks, the establishment of proactive analytical strategies become paramount to safeguard food safety and public health. Leveraging ultra-high-performance liquid chromatography high resolution tandem mass spectrometry (UHPLC-HRMS/MS), this study presents a comprehensive suspect screening strategy which was designed for the efficient detection of emerging contaminants in food. Employing a generic extraction protocol and an extensive suspect list, the strategy was implemented on representative food samples fortified at parts per billion (ppb) level. A notable range of the observed True Positive Rates (TPR) spanning 84%–100% underscores the strategy's robustness and versatility in accurately identifying a wide array of compounds in complex food matrices. Moreover, the integration of a python-powered graphical user interface (GUI) plays a pivotal role in automating data processing steps, streamlining data and facilitating data prioritisation through the application of a metabolomics-inspired approach. This enhancement significantly elevates the strategy's efficiency for high throughput analyses, thus reflecting a more agile approach in addressing emerging hazards. •A comprehensive suspect screening strategy was developed for the efficient detection of emerging chemical hazards in food.•A python-powered GUI that is capable of automated data processing steps was incorporated for high throughput non-targeted screening.•The strategy's robustness and versatility in accurately identifying a wide array of compounds in complex food matrices were evaluated in this study.
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ISSN:0956-7135
1873-7129
DOI:10.1016/j.foodcont.2024.110538