Dataset on the solvent screening and extraction optimization of volatile compounds from Sequoia sempervirens leaves

This dataset reports the comprehensive results of a solvent screening and extraction opti-mization experiment targeting volatile compounds obtained from Sequoia sempervirens (coastredwood) leaves. Leaf samples were collected from a single tree and branch under rigorous andidentical conditions to minimize inter-individual variability. Extractions were systematicallyperformed in duplicate using various pure and binary solvent systems. Parameters suchas solvent composition, solid-to-liquid ratio, and maceration duration were varied to mapextraction efficiency. Each extract was analyzed by Gas Chromatography–Mass Spectrometry(GC–MS) with an optimized oven temperature program, and the signal intensities (TotalIon Current peak areas) of the identified compounds were normalized and averaged acrossindependent replicates. This dataset provides a robust quantitative basis for optimizingsolvent choice and extraction conditions within the framework of green chemistry, bio-sourcedapplications, and forest biorefinery development.