Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classifcation task. Within this study we developed an explainable framework to unveil a deep learning model for pollen classifcation. Model works on data coming from single particle detector (Rapid-E) that records for each particle optical fngerprint with scattered light and laser induced fuorescence. Morphological properties of a particle are sensed with the light scattering process, while chemical properties are encoded with fuorescence spectrum and fuorescence lifetime induced by high-resolution laser. By utilizing these three data modalities, scattering, spectrum, and lifetime, deep learning-based models with millions of parameters are learned to distinguish diferent pollen classes, but a proper understanding of such a black-box model decisions demands additional methods to employ. Our study provides the frst results of applied explainable artifcial intelligence (xAI) methodology on the pollen classifcation model. Extracted knowledge on the important features that attribute to the predicting particular pollen classes is further examined from the perspective of domain knowledge and compared to available reference data on pollen sizes, shape, and laboratory spectrofuorometer measurements

Using Front-Face Fluorescence Spectroscopy and Biochemical Analysis of Honey to Assess a Marker for the Level of Varroa destructor Infestation of Honey Bee (Apis mellifera) Colonies

Varroa destructor is a parasitic mite responsible for the loss of honey bee (Apis mellifera) colonies. This study aimed to find a promising marker in honey for the bee colony infestation level using fluorescence spectroscopy and biochemical analyses. We examined whether the parameters of the honey samples’ fluorescence spectra and biochemical parameters, both related to proteins and phenolics, may be connected with the level of honey bee colonies’ infestation. The infestation level was highly positively correlated with the catalase activity in honey (r = 0.936). Additionally, the infestation level was positively correlated with the phenolic spectral component (r = 0.656), which was tentatively related to the phenolics in honey. No correlation was found between the diastase activity in honey and the colonies’ infestation level. The results indicate that the catalase activity in honey and the PFC1 spectral component may be reliable markers for the V. destructor infestation level of the colonies. The obtained data may be related to the honey yield obtained from the apiaries

Comparative Analysis of some Vernal Pollen Concentrations in Timisoara (Romania), Szeged (Hungary), Novi Sad (Serbia) and Ljubljana (Slovenia)

he aim of the study was to compare the airborne concentrations of pollen produced by vernal flowering trees taxa (Alnus, Betula, Carpinus, Corylus, Fraxinus, Platanus, Populus, Quercus, Taxaceae/Cupressaceae) in the cities of Timisoara (Romania), Szeged (Hungary), Novi Sad (Serbia) and Ljubljana (Slovenia) during the years 20062008. Annual variations in the concentration of pollen in the atmosphere were analysed by the volumetric method. In these cities, the period with the greatest diversity of pollen types is spring. These trees are found in mixed forests and are used in urban landscaping and home gardens. Inter-annual differences can be seen in the seasonal behaviour of the pollen in Novi Sad, 2008 being the year in which the highest levels of airborne pollen were reached. During the 3-year period, pollen of the representatives of the family Betulaceae accounted for a significant proportion of total pollen, predominated by Betula pollen and a considerably lower proportion of Alnus, Carpinus and Corylus airpollen. Taxaceae/Cupressaceae pollen appears in the atmospheric pollen spectra of all localities in high concentrations. These pollen grains are the main source of allergens in springtime. Results of the study reveal important differences between the cities

Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations

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

Physicochemical composition and techno-functional properties of bee pollen collected in Serbia

Physicochemical composition and techno-functional properties of bee pollens collected in Serbia were assessed. Analysed bee pollen contained 14.81-27.25% proteins, 1.31-6.78% lipids, 64.42-81.84% carbohydrates and 1.18-3.21% ash, with mean energy value of 375 kcal. Bee pollen showed low protein solubility (2.79-25.90 g/100 g), high carbohydrate solubility (31.2-75 g/100 g), good emulsifying properties (emulsion stability index ranged from 19.6 to 49.3 min and emulsion activity index ranged from 10.40 to 24.52 m(2)/g), non-foaming properties, poor water absorption capacity (0.92-2.25 g/g) and excellent oil absorption capacity (1-3.53 g/g). Protein solubility was positively correlated with carbohydrate content (r = 0.73, p lt 0.05), but negatively with ash and lipid content (r = -0.39, r = -0.46, p lt 0.05, respectively). The total protein content and lipid content were shown positive relationship with carbohydrate solubility (r = 039, r = 0.45, p lt 0.05, respectively). Emulsion stability was positively correlated with protein solubility (r = 0.47, p lt 0.05), whereas emulsion activity was negatively correlated with this parameter (r = -0.39, p lt 0.05). Water and oil absorption capacity were not shown significant correlations with other investigated parameters. The obtained data indicated that bee pollen could find useful application as food ingredient in variety of food products

Assessment of real-time bioaerosol particle counters using reference chamber experiments

This study presents the first reference calibrations of three commercially available bioaerosol detectors. The Droplet Measurement Technologies WIBS-NEO (new version of the wideband integrated bioaerosol spectrometer), Plair Rapid-E, and Swisens Poleno were compared with a primary standard for particle number concentrations at the Federal Institute for Metrology (METAS). Polystyrene (PSL) spheres were used to assess absolute particle counts for diameters from 0.5 to 10 µm. For the three devices, counting efficiency was found to be strongly dependent on particle size. The results confirm the expected detection range for which the instruments were designed. While the WIBS-NEO achieves its highest efficiency with smaller particles, e.g. 90 % for 0.9 µm diameter, the Plair Rapid-E performs best for larger particles, with an efficiency of 58 % for particles with a diameter of 10 µm. The Swisens Poleno is also designed for larger particles but operates well from 2 µm. However, the exact counting efficiency of the Poleno could not be evaluated as the cut-off diameter range of the integrated concentrator unit was not completely covered. In further experiments, three different types of fluorescent particles were tested to investigate the fluorescent detection capabilities of the Plair Rapid-E and the Swisens Poleno. Both instruments showed good agreement with the reference data. While the challenge to produce known concentrations of larger particles above 10 µm or even fresh pollen particles remains, the approach presented in this paper provides a potential standardised validation method that can be used to assess counting efficiency and fluorescence measurements of automatic bioaerosol monitoring devices

Bioaerosol field measurements: Challenges and perspectives in outdoor studies

Outdoor field measurements of bioaerosols are performed within a wide range of basic and applied scientific disciplines, each with its own goals, assumptions, and terminology. This paper contains brief reviews of outdoor field bioaerosol research from these diverse interests, with emphasis on perspectives from the atmospheric sciences. The focus is on a high level discussion of pressing scientific questions, grand challenges, and needs for cross-disciplinary collaboration. The research topics, in which bioaerosol field measurement are important, include (i) atmospheric physics, clouds, climate, and hydrological cycle; (ii) atmospheric chemistry; (iii) airborne allergen-containing particles; (iv) airborne human pathogens and national security; (v) airborne livestock and crop pathogens; and (vi) biogeography and biodiversity. We concisely review bioaerosol impacts and discuss properties that distinguish bioaerosols from abiological aerosols. We give extra focus to regions of specific interest, i.e. forests, polar regions, marine and coastal environments, deserts, urban and rural areas, and summarize key considerations related to bioaerosol measurements, such as of fluxes, long-range transport, and from both stationary and vessel-driven platforms. Keeping in mind a series of key scientific questions posed within the diverse communities, we suggest that pressing scientific questions include: (i) emission sources and flux estimates; (ii) spatial distribution; (iii) changes in distribution; (iv) atmospheric aging; (v) metabolic activity; (vi) urbanization of allergies; (vii) transport of human pathogens; and (viii) climate-relevant properties

Towards standardisation of automatic pollen and fungal spore monitoring : best practises and guidelines

Standards for manual pollen and fungal spore monitoring have been established based on several decades of experience, tests, and research. New technological and methodological advancements have led to the development of a range of different automatic instruments for which no standard yet exist. This paper aims to provide an overview of aspects that need to be considered for automatic pollen and fungal spore monitoring, including a set of guidelines and recommendations. It covers issues relevant to developing an automatic monitoring network, from the instrument design and calibration through algorithm development to site selection criteria. Despite no official standard yet existing, it is essential that all aspects of the measurement chain are carried out in a manner that is as standardised as possible to ensure high-quality data and information can be provided to end-users

An operational model for forecasting ragweed pollen release and dispersion in Europe

The paper considers the possibilities of modelling the release and dispersion of the pollen of common ragweed (Ambrosia artemisiifolia L.), a highly allergenic invasive weed, which is spreading through southern and central Europe. In order to provide timely warnings for the allergy sufferers, a model was developed for forecasting ragweed pollen concentrations in the air. The development was based on the system for integrated modelling of atmospheric composition (SILAM) and concentrated on spatio-temporal modelling of ragweed flowering season and pollen release, which constitutes the emission term. Evaluation of the new model against multi-annual ragweed pollen observations demonstrated that the model reproduces well the main ragweed pollen season in the areas with major plant presence, such as the Pannonian Plain, the Lyon area in France, the Milan region in Italy, Ukraine and southern Russia. The predicted start of the season is mostly within 3 days of the observed for the majority of stations in these areas. The temporal correlation between modelled and observed concentrations exceeds 0.6 for the bulk of the stations. Model application to the seasons of 2005–2011 indicated the regions with high ragweed pollen concentrations, in particular the areas where allergenic thresholds are exceeded. It is demonstrated that, due to long-range transport of pollen, high-concentration areas are substantially more extensive than the heavily infested territories