气候变化领域集成服务门户(CCPortal): Mixture analyses of air-sampled pollen extracts can accurately differentiate pollen taxa

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DOI	10.1016/j.atmosenv.2020.117746
	Mixture analyses of air-sampled pollen extracts can accurately differentiate pollen taxa
	Klimczak L.J.; Ebner von Eschenbach C.; Thompson P.M.; Buters J.T.M.; Mueller G.A.
发表日期	2020
ISSN	1352-2310
卷号	243
英文摘要	The daily pollen forecast provides crucial information for allergic patients to avoid exposure to specific pollen. Pollen counts are typically measured with air samplers and analyzed with microscopy by trained experts. In contrast, this study evaluated the effectiveness of identifying the component pollens using the metabolites extracted from an air-sampled pollen mixture. Ambient air-sampled pollen from Munich in 2016 and 2017 was visually identified from reference pollens and extracts were prepared. The extracts were lyophilized, rehydrated in optimal NMR buffers, and filtered to remove large proteins. NMR spectra were analyzed for pollen associated metabolites. Regression and decision-tree based algorithms using the concentration of metabolites calculated from the NMR spectra outperformed algorithms 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 using convolutional neural networks performed better than regression models using 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). This study demonstrates that NMR spectra of air-sampled pollen extracts can be used in an automated fashion to provide taxa and type-specific measures of the daily pollen count. © 2020
关键词	Aerobiology Exposure Metabolomics Mixtures NMR Pollen
语种	英语
scopus关键词	Biomolecules; Convolutional neural networks; Decision trees; Deep learning; Mixtures; Nuclear magnetic resonance spectroscopy; Regression analysis; Trees (mathematics); Allergic patients; Classification accuracy; Decision-tree based algorithms; Learning models; Mixture analysis; Prediction algorithms; Regression model; Relative errors; Metabolites; pollen extract; air sampling; allergy; plant extract; pollen; pollution exposure; air analysis; Article; convolutional neural network; decision tree; deep learning; grass; lyophilisate; nuclear magnetic resonance; pollen; predictive value; priority journal; regression analysis; taxonomy; Bavaria; Germany; Munich
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248897
作者单位	National Institute of Environmental Health Sciences, United States; Center of Allergy & Environment (ZAUM), Member of the German Center for Lung Research (DZL), Technische Universität München/Helmholtz Center, Munich, Germany; Molecular Education, Technology and Research Innovation Center, North Carolina State University, Raleigh, NC, United States; Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, United States
推荐引用方式 GB/T 7714	Klimczak L.J.,Ebner von Eschenbach C.,Thompson P.M.,et al. Mixture analyses of air-sampled pollen extracts can accurately differentiate pollen taxa[J],2020,243.
APA	Klimczak L.J.,Ebner von Eschenbach C.,Thompson P.M.,Buters J.T.M.,&Mueller G.A..(2020).Mixture analyses of air-sampled pollen extracts can accurately differentiate pollen taxa.ATMOSPHERIC ENVIRONMENT,243.
MLA	Klimczak L.J.,et al."Mixture analyses of air-sampled pollen extracts can accurately differentiate pollen taxa".ATMOSPHERIC ENVIRONMENT 243(2020).