Chemical Profiles of Atractylodes Macrocephala Rhizoma Grades

A study on Atractylodes macrocephala Rhizoma (Baizhu) analyzed using HS-GC–MS, LC–MS, electronic nose, and electronic tongue. The research identified four volatile and six non-volatile differential compounds across grades and established a multi-technology fusion model achieving 98.33% classification accuracy. It was authored by Ruiqi Yang and published in 2026.

Use Cases

• Identify grade-specific volatile compounds like terpenoids from HS-GC–MS data for aroma profiling.
• Analyze non-volatile taste compounds such as esters and phenolic acids from LC–MS data for quality assessment.
• Train a Random Forest classifier on fused electronic nose and electronic tongue sensor data for rapid grade classification.
• Perform Spearman correlation analysis on differential compounds to link chemical profiles to commercial grading standards.

Strengths

• Model achieved 98.33% classification accuracy using a multi-technology fusion strategy.
• Identified four volatile and six non-volatile differential compounds common to all grades.
• Data generated from four analytical technologies: HS-GC–MS, LC–MS, electronic nose, and electronic tongue.

Limitations

• Dataset scope is limited to a single medicinal herb species, Atractylodes macrocephala Rhizoma.
• Data is presented in a DOCX document (180.5 KB), requiring extraction for computational analysis.
• Sample size and specific row/column counts for the underlying measurements are not provided.

Provenance

Source

Ruiqi Yang via figshare.

Collection Method

Chemical analysis using HS-GC–MS and LC–MS, combined with electronic nose and electronic tongue sensor measurements.

Time Range

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Freshness

Last updated in March 2026.

Geography

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