Klimczak Leszek J, von Eschenbach Cordula Ebner, Thompson Peter M, Buters Jeroen T M, Mueller Geoffrey A
National Institute of Environmental Health Sciences.
Center of Allergy & Environment (ZAUM), Member of the German Center for Lung Research (DZL), Technische Universität München/Helmholtz Center, Munich, Germany.
Atmos Environ (1994). 2020 Dec 15;243. doi: 10.1016/j.atmosenv.2020.117746. Epub 2020 Jul 6.
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.
每日花粉预报为过敏患者提供了避免接触特定花粉的关键信息。花粉计数通常通过空气采样器进行测量，并由训练有素的专家用显微镜进行分析。相比之下，本研究评估了使用从空气采样的花粉混合物中提取的代谢物来识别成分花粉的有效性。从2016年和2017年慕尼黑的环境空气采样花粉中，通过参考花粉进行目视识别并制备提取物。提取物经冻干处理，在最佳核磁共振缓冲液中复水，并过滤以去除大蛋白质。对核磁共振光谱进行分析以寻找与花粉相关的代谢物。使用从核磁共振光谱计算出的代谢物浓度的基于回归和决策树的算法，在花粉识别方面比使用核磁共振光谱本身作为输入数据的算法表现更好。针对低、中、高和非常高花粉计数组训练的分类预测算法，对树木花粉计数的准确率为74%，对草本花粉计数的准确率为82%，对杂草花粉计数的准确率为93%。使用卷积神经网络的深度学习模型比使用核磁共振光谱输入的回归模型表现更好，并且在相对误差和分类准确率方面是总体最佳方法（树木花粉计数为86%，草本花粉计数为89%，杂草花粉计数为93%）。本研究表明，空气采样花粉提取物的核磁共振光谱可用于以自动化方式提供每日花粉计数的分类群和类型特异性测量。
