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<p>Grass pollen is the major outdoor allergen source, inducing allergic rhinitis in susceptible people. In Australia, the considerable prevalence of allergic rhinitis (23.8%) and asthma (11%) is creating a major health and economic burden. Subtropical grass species exhibit distinct ecological and phylogenetic differences compared with temperate grasses, including variations in pollen allergen composition and immune response. Traditional light microscopy, however, cannot differentiate between airborne grass pollen of various subfamilies. This study hypothesizes that automated holography and fluorometry sensing can discern grass pollen beyond family level (Poaceae). This project applied automatic sensing to five aerosolized dried grass pollen samples from Panicoideae; <i>Paspalum notatum</i> (Bahia grass) and <i>Sorghum halepense</i> (Johnson grass), Chloridoideae; <i>Cynodon dactylon</i> (Bermuda grass), and Pooideae; <i>Lolium perenne</i> (Ryegrass) and <i>Phleum pratense</i> (Timothy grass). Datasets were examined for differences in distributions of holography-derived particle features and emitted fluorescence spectra. In three repeated measurement campaigns of the same dry <i>L. perenne</i> pollen sample, distributions of particle features, and relative fluorescence intensity values suggested that reliability and reproducibility of automatic sensors remain a challenge. Data from five grass pollen types, even within the same subfamily, showed distinct morphological and fluorescence profiles, particularly at excitation 280&#xa0;nm with 357&#xa0;nm emission. The automated monitoring was also capable of distinguishing dried and freshly collected local <i>P. notatum</i> pollen. To translate these new insights for improved respiratory disease management, further research with broader data collection and validation with key locally relevant pollen taxa will be required for more precise real-time monitoring of grass pollen exposure.</p>

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The ability of automated fluorometry and holography to discern airborne grass pollen beyond family level

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Izhar Ullah,
  • Andelija Milic,
  • Beth Addison-Smith,
  • Saeideh Hajighasemi,
  • Darren Wraith,
  • Janet M. Davies

摘要

Grass pollen is the major outdoor allergen source, inducing allergic rhinitis in susceptible people. In Australia, the considerable prevalence of allergic rhinitis (23.8%) and asthma (11%) is creating a major health and economic burden. Subtropical grass species exhibit distinct ecological and phylogenetic differences compared with temperate grasses, including variations in pollen allergen composition and immune response. Traditional light microscopy, however, cannot differentiate between airborne grass pollen of various subfamilies. This study hypothesizes that automated holography and fluorometry sensing can discern grass pollen beyond family level (Poaceae). This project applied automatic sensing to five aerosolized dried grass pollen samples from Panicoideae; Paspalum notatum (Bahia grass) and Sorghum halepense (Johnson grass), Chloridoideae; Cynodon dactylon (Bermuda grass), and Pooideae; Lolium perenne (Ryegrass) and Phleum pratense (Timothy grass). Datasets were examined for differences in distributions of holography-derived particle features and emitted fluorescence spectra. In three repeated measurement campaigns of the same dry L. perenne pollen sample, distributions of particle features, and relative fluorescence intensity values suggested that reliability and reproducibility of automatic sensors remain a challenge. Data from five grass pollen types, even within the same subfamily, showed distinct morphological and fluorescence profiles, particularly at excitation 280 nm with 357 nm emission. The automated monitoring was also capable of distinguishing dried and freshly collected local P. notatum pollen. To translate these new insights for improved respiratory disease management, further research with broader data collection and validation with key locally relevant pollen taxa will be required for more precise real-time monitoring of grass pollen exposure.