Study on Molecularly Imprinted Polymers Obtained Sonochemically for the Determination of Aflatoxins in Food.
Sara PalmieriDounia ElfadilFederico FantiFlavio Della PelleManuel SergiAziz AmineDario CompagnonePublished in: Molecules (Basel, Switzerland) (2023)
Aflatoxins (AFs) are fungi secondary metabolites produced by the Aspergillus family. These compounds can enter the food chain through food contamination, representing a risk to human health. Commercial immunoaffinity columns are widely used for the extraction and cleanup of AFs from food samples; however, their high cost and large solvent consumption create a need for alternative strategies. In this work, an alternative strategy for producing molecularly imprinted polymers (MIPs) was proposed to extract aflatoxins AFB1, AFB2, AFG1, and AFG2 from complex food samples, using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The MIPs were synthesized via a low-cost and rapid (5 min) sonochemical free-radical polymerization, using 1-hydroxy-2-naphthoic acid as a dummy template. MIPs-based solid phase extraction performance was tested on 17 dietary supplements (vegetables, fruits, and cereals), obtaining appreciable recovery rates (65-90%) and good reproducibility (RSD ≤ 6%, n = 3); the selectivity towards other mycotoxins was proved and the data obtained compared with commercial immunoaffinity columns. The proposed strategy can be considered an alternative affordable approach to the classical immunoaffinity columns, since it is more selective and better performing.
Keyphrases
- solid phase extraction
- molecularly imprinted
- liquid chromatography
- human health
- tandem mass spectrometry
- high performance liquid chromatography
- ultra high performance liquid chromatography
- risk assessment
- mass spectrometry
- high resolution mass spectrometry
- liquid chromatography tandem mass spectrometry
- simultaneous determination
- gas chromatography
- gas chromatography mass spectrometry
- climate change
- low cost
- ms ms
- ionic liquid
- quantum dots
- machine learning