Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation - Nicolas Bruffaerts; E Graf; P Matavulj; Astha Tiwari; I Pyrri; Y Zeder; S Erb; M Plaza; S Dietler; T Bendinelli; Elizabet D'hooge; B Sikoparija (Aerobiologia, 2025)
Airborne bioparticles, including fungal spores, are of major concern for human and plant health, necessitating precise monitoring systems. While a European norm exists for manual volumetric monitoring, there's a growing interest in automated real-time methods. However, these methods rely heavily on machine learning, facing challenges due to diverse particle characteristics and limited training data availability, especially for fungal spores. This study aims to address this gap by outlining best practices for collecting reference material and creating tailored datasets for training algorithms. Using 17 fungal species from the Belgian fungi collection BCCM/IHEM, including five Alternaria species, key aspects such as in vitro cultivation, dry spore harvest, and aerosolization were addressed. Simple classification models were developed, achieving varying accuracies on different monitors. The Plair Rapid-E+ demonstrated accuracies ranging from 83.4% to 95.1% (macro average F1-score 0.61), with better recognition for Cladosporium spp. and Curvularia caricae-papayae. The SwisensPoleno Jupiter, initially achieving a macro average F1-score of 0.77 with holographic images of eight genera, improved to 0.83 when combined with fluorescence data. Accuracies ranged from 55 to 95%, with notable performance for Alternaria spp. and Curvularia caricae-papayae. Species differentiation was also shown to be possible for Cladosporium, but was more difficult for some Alternaria species, while the macro average F1-score remained good (0.72). Overall, this protocol paves the way for more efficient, standard, and accurate automatic identification of airborne fungal spores.
Keywords: Airflow cytometry; Automatic identification; Culture collection; fungal spores; Machine Learning

Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation - Nicolas Bruffaerts; E Graf; Astha Tiwari; I Pyrri; S Erb; M Plaza; Elizabet D'hooge; P Matavulj; B Sikoparija (European Aerosol Congress 2024, 2024)
Airborne bioparticles, notably fungal spores, pose health risks, necessitating precise monitoring. While manual methods exist, interest is shifting towards automated systems employing machine learning. However, challenges persist due to diverse particle properties and limited training data. This study, part of SYLVA and COST Action ADOPT, addresses these gaps by establishing best practices for cultivating reference material and creating tailored datasets. Seventeen fungal species were tested on Plair RapidE+ and SwisensPoleno Jupiter. Proof-of-principle models using holography and fluorescence data were developed, achieving variable genus classification accuracy (0.43-0.95). The protocol enhances automatic identification of airborne fungal spores, promising more efficient monitoring systems.
Keywords: fungal spores

Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation - Nicolas Bruffaerts; E Graf; Astha Tiwari; I Pyrri; S Erb; M Plaza; Elizabet D'hooge; P Matavulj; B Sikoparija (World Aerobiology Conference 2024, 2024)
The presence of bioparticles in the air, including fungal spores, is a major concern for human and plant health and requires robust and precise monitoring systems. While a European norm based on the manual volumetric Hirst method exists, there's a growing interest in technologies allowing automated real-time monitoring. Most of them rely on machine learning for the identification of bioaerosols. However, the diverse nature of airborne particles in terms of size, properties and composition presents challenges, among which the availability of well-curated datasets for training algorithms. While collecting reference material for pollen is relatively straightforward, current automatic monitoring methods for fungal spores rely on limited training data, hindering broader applicability. This study, which was conducted in the frame of the SYLVA project (GA no. 101086109) and the COST Action ADOPT (CA18226), aims to address this gap by outlining best practices for collecting reference material from controlled cultivation and creating datasets specifically tailored for training algorithms to classify airborne fungal spores. Critical aspects such as access to reference fungal species, in vitro cultivation, sporulation yield, clean spore isolation, dry aerosolization, and dataset cleaning have been explored for a series of 17 fungal species from the Belgian fungi collection BCCM/IHEM, including 5 Alternaria species with contrasted morphological profiles. Simple classification models were developed as proof-of-principle to assess recognition capabilities from the holography and/or fluorescence data measured by the SwisensPoleno Jupiter (Swisens AG) and laser-induced scattering and fluorescence data measured by the Plair Rapid-E+ (Plair SA). The models were trained using 80% of reference data, while 10% was used for validation to avoid overfitting during training and the remaining 10% was left aside for testing the identification performance. For Plair Rapid-E+, classification accuracy for 7 genera was shown to vary from 0.43 to 0.75 depending on the taxon (F1 score 0.577), recognizing best Botrytis cinerea and Cladosporium (class created as a mix of 3 species). For SwisensPoleno Jupiter, the initial performance obtained for classification of 8 genera by using only holographic images (F1 score 0.77) has been significantly improved by complementing them with fluorescence measurements (F1 score 0.83). Classification accuracy varied between 0.55 and 0.95 with the best performance for Curvularia lunata and Alternaria (class created as a mix of 5 species). Differentiation of species was also shown to be possible for Cladosporium, with more difficulty for some Alternaria species, while the F1 score remained good (0.72). Overall, this protocol is paving the way for more efficient, standard and accurate automatic identification of airborne fungal spores.
Keywords: fungal spores

Holography-based aerobiological monitoring: a 2-year intercomparison campaign versus the standard Hirst method in Brussels, Belgium - Astha Tiwari; Nicolas Bruffaerts (Challenges of Modern Aerobiology – 82nd International scientific conference, 2024)
Around 10% of the Belgian population is allergic to the pollen emitted by trees of the Betulaceae family (birch, alder, hazel and hornbeam) and an estimated 18% to grass pollen. To prevent and treat respiratory symptoms and reduce the allergy burden, pollen grain concentrations have been continuously monitored using the standard Hirst method with a volumetric spore trap. However, this offline method can only provide results retrospectively, from 1 day to 1 week, and previous evaluation studies have shown certain limitations in terms of sampling efficiency and measurement accuracy. Recently, several automatic real-time instruments have been developed to allow the identification and quantification of airborne pollen. After a 2-year campaign performed in Brussels (Belgium), daily concentrations of 13 pollen taxa measured by the Swisens Poleno Jupiter were compared to the measurements of the Hirst method. The highest intraseasonal correlation values between both methods were found for Betula, Fraxinus and Poaceae. While some pollen taxa showed similar seasonal kinetics but with scaling discrepancies, other pollen taxa frequently presented out-of-season false positive peaks as results from the automatic monitor. In all, the holography-based monitoring in real conditions appeared to be relatively reliable within the seasons of most allergenic pollen taxa. Further improvements are expected by training the identification algorithm with reference datasets generated from pollen collected in the local environment.
Keywords: pollen

Holography-based aerobiological monitoring: a 2-year intercomparison campaign versus the standard Hirst method in Brussels, Belgium - Astha Tiwari; Nicolas Bruffaerts (World Aerobiology Conference 2024, 2024)
Around 10% of the Belgian population is allergic to the pollen emitted by trees of the Betulaceae family (birch, alder, hazel and hornbeam) and an estimated 18% to grass pollen. To prevent and treat respiratory symptoms and reduce the allergy burden, pollen grain concentrations have been continuously monitored using the standard Hirst method with a volumetric spore trap. However, this offline method can only provide results retrospectively, from 1 day to 1 week, and previous evaluation studies have shown certain limitations in terms of sampling efficiency and measurement accuracy. Recently, several automatic real-time instruments have been developed to allow the identification and quantification of airborne pollen. After a 2-year campaign performed in Brussels (Belgium), daily concentrations of 13 pollen taxa measured by the Swisens Poleno Jupiter were compared to the measurements of the Hirst method. The highest intraseasonal correlation values between both methods were found for Betula, Fraxinus and Poaceae. While some pollen taxa showed similar seasonal kinetics but with scaling discrepancies, other pollen taxa frequently presented out-of-season false positive peaks as results from the automatic monitor. In all, the holography-based monitoring in real conditions appeared to be relatively reliable within the seasons of most allergenic pollen taxa. Further improvements are expected by training the identification algorithm with reference datasets generated from pollen collected in the local environment.
Keywords: pollen