Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations
| Autor: | Predrag Matavulj, Antonella Cristofori, Fabiana Cristofolini, Elena Gottardini, Sanja Brdar, Branko Sikoparija |
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| Rok vydání: | 2022 |
| Předmět: |
Domain adaptation
Environmental Engineering Airborne pollen Atmosphere Aerobiology Settore BIO/03 - BOTANICA AMBIENTALE E APPLICATA Reproducibility of Results Allergens Classification Poaceae Pollution Fluorescence Machine learning Environmental Chemistry Pollen Neural Networks Computer Waste Management and Disposal |
| Zdroj: | The Science of the total environment. 851(Pt 2) |
| ISSN: | 1879-1026 |
| Popis: | Pollen is the most common cause of seasonal allergies, affecting over 33 % of the European population, even when considering only grasses. Informing the population and clinicians in real-time about the actual presence of pollen in the atmosphere is essential to reduce its harmful health and economic impact. Thus, there is a growing network of automatic particle analysers, and the reproducibility and transferability of implemented models are recommended since a reference dataset for local pollen of interest needs to be collected for each device to classify pollen, which is complex and time-consuming. Therefore, it would be beneficial to incorporate the reference dataset collected from other devices in different locations. However, it must be considered that laser-induced data are prone to device-specific noise due to laser and detector sensibility. This study collected data from two Rapid-E bioaerosol identifiers in Serbia and Italy and implemented a multi-modal convolutional neural network for pollen classification. We showed that models lost their performance when trained on data from one and tested on another device, not only in terms of the recognition ability but also in comparison with the manual measurements from Hirst-type traps. To enable pollen classification with just one model in both study locations, we first included the missing pollen classes in the dataset from the other study location, but it showed poor results, implying that data of one pollen class from different devices are more different than data of different pollen classes from one device. Combining all available reference data in a single model enabled the classification of a higher number of pollen classes in both study locations. Finally, we implemented a domain adaptation method, which improved the recognition ability and the correlations of transferred models only for several pollen classes. |
| Databáze: | OpenAIRE |
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