Improved quantification of UV-B absorbing compounds in Pinus sylvestris L. pollen grains using an internal standard methodology
- Citation data:
Review of Palaeobotany and Palynology, ISSN: 0034-6667, Vol: 247, Page: 97-104
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- Agricultural and Biological Sciences; Earth and Planetary Sciences
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UV-B absorbing compounds such as para -coumaric acid are a major constituent of the sporopollenin-based exines of pollen grains. Recent research indicates that these compounds are found in higher concentrations in the pollen of plants exposed to higher levels of UV-B radiation and studies have proposed that variations of para -coumaric acid within fossil pollen could act as a proxy for changes in the amount of UV-B reaching the Earth's surface. However, the low analytical precision in the established method using Thermally Assisted Hydrolysis and Methylation with pyrolysis Gas-Chromatography Mass-Spectrometry (THM–GC/MS) means that quantification of UV-B absorbing compounds within sporopollenin remains a major challenge. Here, we test a variety of normalisation procedures combined with THM–GC/MS to find a method that can provide improved analytical precision in the quantification of UV-B absorbing compounds for Pinus sylvestris L. pollen. Normalisation of UV-B absorbing compounds against non-UV-B absorbing compounds found within sporopollenin was compared to external and internal standard-based approaches. Of the different methods tested, vanillic acid (4-hydroxy-3-methoxybenzoic acid) used as an internal standard provided the best potential for improved performance, with analytical precision improving by approximately 43% when the next-best normalisation procedures were used. Using this method, we estimate the abundance of para -coumaric acid to be 0.34 ± 0.02 ng grain −1 (95% confidence intervals, n = 20) from a sample collected from a P. sylvestris individual from Catalunya, Spain. The findings from this study provide advantages to previous THM–GC/MS procedures proposed for quantification of para -coumaric acid in pollen grains in terms of improved analytical precision and increased robustness. This will result in improved consistency for batches of samples analysed over long time periods and will enable comparisons between sample sets run in different laboratories.