Multiple headspace solid-phase microextraction after matrix modification for avoiding matrix effect in the
Chang-Wen Ye1, Xue-Na Zhang, Yuan-Li Gao
1College of Food Science & Technology, Huazhong Agricultural University, Wuhan, China.
Analytica Chimica Acta
|November 30, 2011
Summary
Multiple headspace solid-phase microextraction (HS-SPME) offers complete analyte recovery for solid samples. This method accurately quantifies ethyl carbamate in bread, overcoming water adsorption issues for reliable results.
Area of Science:
- Analytical Chemistry
- Food Safety Analysis
Background:
- Solid-phase microextraction (SPME) is widely used for analyte quantification.
- Traditional SPME methods can suffer from matrix effects and incomplete analyte recovery.
- Quantifying contaminants in solid food matrices presents unique challenges.
Purpose of the Study:
- To develop and validate a multiple headspace solid-phase microextraction (HS-SPME) method for ethyl carbamate (EC) determination in bread.
- To address and overcome the challenge of water adsorption in fresh bread samples affecting EC quantification.
- To introduce a novel, high-performance extraction fiber for improved analytical performance.
Main Methods:
- Utilized multiple HS-SPME coupled with gas chromatography-flame ionization detection (GC-FID).
- Developed and tested a novel polyethylene glycol/hydroxy-terminated silicone oil fiber.
- Incorporated anhydrous sodium sulphate to mitigate water adsorption interference.
Main Results:
- The developed method demonstrated excellent linearity (0.15-1500 μg g⁻¹), precision (1.6%), and a low limit of detection (0.041 μg g⁻¹).
- Achieved high recovery rates (92.5–103.4%) across different spiking levels.
- Successfully applied the method to analyze 14 real bread samples.
Conclusions:
- Multiple HS-SPME provides complete analyte recovery, effectively eliminating matrix effects.
- The novel fiber and anhydrous sodium sulphate addition successfully resolved water adsorption issues in bread analysis.
- This validated method offers a cost-effective, time-efficient, and reliable approach for analyzing multiple solid samples.
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