Comparison of Metabolites in Rice from Different Production Areas Using GC-MS/MS

Significance of the Topic

Comprehensive metabolite profiling of rice is essential for understanding how regional cultivation conditions affect nutritional quality and flavor profiles, supporting crop breeding and food quality control.

Objectives and Study Overview

This study investigated the metabolite composition of Koshihikari rice from six Japanese prefectures. By targeting 502 metabolites and applying multivariate statistical analysis, the work aims to identify characteristic compounds that differentiate production areas.

Methodology

Rice grains were freeze-crushed and extracted using the Bligh & Dyer method. Extracts were derivatized via methoxime-TMS, employing 2-isopropylmalate as an internal standard. A total of 65 metabolites—comprising sugars, amino acids, fatty acids, and organic acids—were consistently detected and quantified for statistical evaluation.

Used Instrumentation

• Gas chromatograph–tandem mass spectrometer: GCMS-TQ8040 NX
• Autoinjector: AOC-30i/20s U
• Capillary column: BPX-5 (30 m × 0.25 mm I.D., 0.25 µm film)
• Carrier gas: Helium at 39.0 cm/s linear velocity
• Derivatization database: Smart Metabolites Database Ver. 2
• Data processing software: LabSolutions Insight
• Multivariate analysis software: eMSTAT Solution

Main Results and Discussion

Principal component analysis (PCA) of 55 significant metabolites (p < 0.05) captured 41.4 % of total variance (PC1: 22.0 %, PC2: 19.4 %). Score plots distinguished Chiba and Kagawa samples along PC1, and Toyama versus Niigata along PC2. Loading plots revealed region-specific enrichment: amino acids (e.g., leucine, valine) in Chiba; monosaccharides (glucose, fructose) in Kagawa; medium-chain fatty acids (caproic, octanoic acids) in Niigata; and organic acids (citric acid, 2-aminopimelic acid) in Toyama. Box plots validated these characteristic differences.

Benefits and Practical Applications

• Efficient processing of large-scale chromatogram data accelerates marker discovery.
• Multivariate analysis facilitates rapid discrimination of rice origin for quality assurance.
• Insights into metabolite profiles support breeding strategies and product development in the food industry.

Future Trends and Potential Applications

Advances in high-throughput metabolomics coupled with machine learning may enable predictive models for crop quality and environmental adaptation. Integration with other omics platforms and expansion of metabolite databases will further enhance precision in food authenticity and breeding research.

Conclusion

GC-MS/MS in conjunction with intuitive statistical tools successfully differentiated Koshihikari rice by production area and identified key metabolites responsible for regional variation, offering a practical approach for food quality and authenticity assessment.