New Approach to Food Smell Analysis Using Combination of GCMS™ and GC-SCD (1)
Applications | 2020 | ShimadzuInstrumentation
Food odor analysis is essential in food quality control and flavor profiling. Sulfur-containing volatiles, although often present in trace amounts, significantly influence aroma. Traditional GC–MS excels at qualitative profiling but can struggle with low-abundance sulfur compounds. Combining GC–MS with sulfur chemiluminescence detection (GC-SCD) offers both broad compound coverage and targeted sensitivity for sulfur species.
This work evaluates a hybrid analytical workflow that integrates GC–MS and GC-SCD to characterize odor compounds released from packaged kimchi. The primary goals are to achieve comprehensive volatile profiling and selective, high-sensitivity detection of sulfur compounds.
This dual-technique approach improves odor profiling by combining comprehensive compound identification with targeted sulfur analysis. It is valuable for food quality assurance, flavor optimization, and off-odor investigation in the food industry.
The combination of GC–MS and GC-SCD presents a powerful platform for in-depth aroma analysis. It enables both comprehensive volatile profiling and sensitive detection of trace sulfur compounds, advancing food odor research and quality control.
GC, GC/MSD, GC/SQ
IndustriesFood & Agriculture
ManufacturerShimadzu
Summary
Importance of the Topic
Food odor analysis is essential in food quality control and flavor profiling. Sulfur-containing volatiles, although often present in trace amounts, significantly influence aroma. Traditional GC–MS excels at qualitative profiling but can struggle with low-abundance sulfur compounds. Combining GC–MS with sulfur chemiluminescence detection (GC-SCD) offers both broad compound coverage and targeted sensitivity for sulfur species.
Objectives and Overview of the Study
This work evaluates a hybrid analytical workflow that integrates GC–MS and GC-SCD to characterize odor compounds released from packaged kimchi. The primary goals are to achieve comprehensive volatile profiling and selective, high-sensitivity detection of sulfur compounds.
Methodology and Sample Preparation
- Sample preparation: 5 g of commercial kimchi sealed in a polyethylene bag.
- Volatile capture: MonoTrap adsorbent placed alongside the sample for 2 h at room temperature.
- Desorption: MonoTrap extracted with 1 mL of diethyl ether/n-pentane (1:1).
- Chromatographic conditions: Split injection (1 µL, split ratio 1:5) under constant He pressure; InertCap 5MS/Sil column; temperature program 50 °C (5 min) → 250 °C at 10 °C/min.
Used Instrumentation
- GC-SCD: Nexis GC-2030 coupled to SCD-2030 chemiluminescence detector.
- GC–MS: Shimadzu GCMS-QP2020NX with electron ionization source.
- Column: InertCap 5MS/Sil (30 m × 0.32 mm I.D., 0.50 µm film).
Main Results and Discussion
- GC-SCD selectively detected eight sulfur compounds, including allyl methyl sulfide and dimethyl trisulfide, at trace levels.
- GC–MS total ion chromatogram identified four sulfur species alongside other volatiles such as alcohols and ketones.
- GC–MS provided a broad aroma profile, while GC-SCD offered enhanced sensitivity and selectivity for sulfur volatiles.
Benefits and Practical Applications
This dual-technique approach improves odor profiling by combining comprehensive compound identification with targeted sulfur analysis. It is valuable for food quality assurance, flavor optimization, and off-odor investigation in the food industry.
Future Trends and Potential Applications
- Real-time or dynamic sampling strategies for continuous odor monitoring.
- Coupling with multidimensional GC or high-resolution MS to resolve complex matrices.
- Extension of the method to diverse food and beverage products for aroma fingerprinting.
Conclusion
The combination of GC–MS and GC-SCD presents a powerful platform for in-depth aroma analysis. It enables both comprehensive volatile profiling and sensitive detection of trace sulfur compounds, advancing food odor research and quality control.
References
- Takemori Y., Kawamura K., Ishii T. Shimadzu Application Note No. M288, First Edition, February 2020.
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