Analysis of Furan and Its Derivatives in Food Matrices Using Solid Phase Extraction Coupled with Gas Chromatography-Tandem Mass Spectrometry.
Wen-Xuan TsaoBing Huei ChenPinpin LinShu-Han YouTsai-Hua KaoPublished in: Molecules (Basel, Switzerland) (2023)
The objective of this study was to develop a simultaneous analysis method of furan and its 10 derivatives in different food commodities. The results indicated that furan and its 10 derivatives could be separated within 9.5 min by using a HP-5MS column and gas chromatography-tandem mass spectrometry (GC-MS/MS) with multiple reaction monitoring mode for detection. Furthermore, this method could resolve several furan isomers, such as 2-methyl furan and 3-methyl furan, as well as 2,3-dimethyl furan and 2,5-dimethyl furan. The most optimal extraction conditions were: 5 g of the fruit or juice sample mixed with 5 mL of the saturated NaCl solution, separately, or 1 g of the canned oily fish sample mixed with 9 mL of the saturated NaCl solution, followed by the equilibration of each sample at 35 °C for 15 min, using a carboxen-polydimethylsiloxane SPME arrow to adsorb the analytes for 15 min at 35 °C for subsequent analysis by GC-MS/MS. For method validation of all the analytes in the different food matrices, the recovery was 76-117% and the limit of the quantitation was 0.003-0.675 ng/g, while the relative standard deviation (RSD%) of the intra-day variability range from 1-16%, and that of the inter-day variability was from 4-20%. The method validation data further demonstrated that a reliable method was established for the analysis of furan and its 10 derivatives in commercial foods.
Keyphrases
- gas chromatography
- tandem mass spectrometry
- ultra high performance liquid chromatography
- solid phase extraction
- high performance liquid chromatography
- liquid chromatography
- mass spectrometry
- simultaneous determination
- ms ms
- liquid chromatography tandem mass spectrometry
- high resolution mass spectrometry
- gas chromatography mass spectrometry
- molecularly imprinted
- high resolution
- deep learning