Over the past few years, a diverse range of automatic real-time instruments has been developed to respond to the needs of end users in terms of information about atmospheric bioaerosols. One of them, the SwisensPoleno Jupiter, is an airflow cytometer used for operational automatic bioaerosol monitoring. The instrument records holographic images and fluorescence information for single aerosol particles, which can be used for identification of several aerosol types, in particular different pollen taxa. To improve the pollen identification algorithm applied to the SwisensPoleno Jupiter and currently based only on the holography data, we explore the impact of merging fluorescence spectra measurements with holographic images. We demonstrate, using measurements of aerosolised pollen, that combining information from these two sources results in a considerable improvement in the classification performance compared to using only a single source (balanced accuracy of 0.992 vs. 0.968 and 0.878). This increase in performance can be ascribed to the fact that often classes which are difficult to resolve using holography alone can be well identified using fluorescence and vice versa. We also present a detailed statistical analysis of the features of the pollen grains that are measured and provide a robust, physically based insight into the algorithm's identification process. The results are expected to have a direct impact on operational pollen identification models, particularly improving the recognition of taxa responsible for respiratory allergies.