Application of a conductive polymer electronic-nose device to identify aged woody samples
The identification of aged woody samples is often a difficult task as a result of weathering and physical deterioration over time which removes or obscures distinguishing anatomical features and characteristics required for visual taxonomic determinations. Fortunately, the chemical characteristics of aged woods usually are preserved better than physical characteristics if the wood remains dry in storage. All wood types, determined by the particular plant species from which woody samples are derived, produce and release a unique complex of volatile organic compounds that distinguish individual wood types when headspace volatiles (containing these unique chemical mixtures) are collectively analyzed using an electronic gas-sensing device such as an electronic nose. The advantage of electronic-nose devices over conventional analytical-chemistry instruments, typically used in laboratory chemical analyses, is that the woody source (plant species) from which headspace volatiles are derived may be identified without having to identify individual chemical compounds present in the headspace analyte mixture. Methods were developed for a conductive polymer type electronic nose gas-sensing device, the Aromascan model A32S, to accurately identify aged woody samples derived from wood pieces held in dry storage for long periods of time. An aroma library was developed using diagnostic aroma profile databases (electronic aroma signature patterns) from known woods of numerous tree species. The A32S electronic nose was capable of distinguishing between 44 wood types, providing correct identification determinations at frequencies ranging from 92-99%. The distribution of aroma class components, defined by wood type for each sample analyzed, also could be determined to indicate the relatedness of volatile aroma components that each sample analyte had in common with individual wood aroma classes. This information was useful for determining the taxonomic relatedness of wood types (plant species) based on the headspace volatiles that were produced. Furthermore, principal component analysis provided precise statistical numerical values (quality factors of significance) that indicated the chemical relatedness between wood volatiles based on pairwise comparisons of organic chemical mixtures from individual wood types.