Pollen Count Standardization for Burkard & Rotorod Samplers

Abstract

The daily pollen forecast provides crucial information for allergic patients to avoid exposure to specific pollens. Pollen counts are typically measured with air samplers and analyzed with microscopy by trained experts. Automated analyses of pollen extracts are being explored as an alternative to traditional pollen counting. METHODS: Extracts of ambient air-sampled pollen from Munich in 2016 and 2017 were lyophilized, rehydrated in optimal NMR buffers, and filtered to remove proteins. NMR spectra were analyzed for pollen associated metabolites. Regression and classification models, using traditional machine learning and deep learning algorithms, were trained to recognize patterns in the metabolites or NMR spectra, based on expertidentified pollen counts. RESULTS: Regression and decision-tree based algorithms using the concentration of metabolites, measured from the NMR spectra, outperformed using the NMR spectra themselves as input data for pollen identification. Categorical prediction algorithms trained for low, medium, high, and very high pollen count groups had accuracies of 74% for the tree, 82% for the grass, and 93% for the weed pollen count. Deep learning models performed better than regression models for NMR spectral input, and were the overall best method in terms of relative error and classification accuracy (86% for tree, 89% for grass, and 93% for weed pollen count). CONCLUSIONS: This study demonstrates that NMR spectra of airsampled pollen extracts could be used in an automated fashion to provide genus and type-specific measures of the pollen count. The classification algorithms can accurately differentiate the low/medium/high category standards of the National Allergy Board.

Airborne Fungal Spore Review, New Advances and Automatisation

“…This has made this sampler useful for studies in remote areas. While there are several studies related to fungal spores using this sampler [61], the standardisation of the results is still being undertaken [62].…”