Abstract Pollen is routinely monitored and forecasted with respect to public health and allergies, but monitoring networks generally utilize a manual process of collection, analysis, and modeling that leads to poor sampling density and high measurement cost. Here, we discuss application of a single-particle fluorescence sensor recently developed for the purpose of real-time detection and recognition of pollen and spores. The sensor operates by collecting fluorescence emission spectra from many individual pollen grains sampled onto a microscope slide for each of four excitation wavelengths (280, 350, 405, and 450 nm) associated with pollen fluorophores. The sensor also records major and minor diameters of each particle. Approximately 25–30 particles for each of eight commercially purchased pollen species were interrogated. Data were analyzed using four classification methods: hierarchical agglomerative and k-means clustering (unsupervised) and random forest and gradient boosting algorithms (supervised). The purpose of the manuscript is to show development of a computational strategy to analyze spectral input data of this kind in order to support further efforts to automate sensor data collection and analysis. Both unsupervised methods showed insufficient accuracy for separating pollen species (76% k-means, 9% HAC) whereas supervised methods performed similarly well (94–95%). The random forest algorithm was then utilized to further optimize operational parameters, based on its higher computational speed. Analyzing the relative importance of each optical source for sensor performance highlighted ways that may be useful to lower sensor cost with minimal reduction to analysis quality. The results provide a framework for the application of this and similar sensors to ambient pollen detection and classification. Copyright © 2020 American Association for Aerosol Research

中文翻译：

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使用新开发的单粒子荧光光谱仪的花粉聚类策略

摘要 花粉在公共卫生和过敏方面受到常规监测和预测，但监测网络通常采用手动收集、分析和建模过程，导致采样密度低和测量成本高。在这里，我们讨论了最近为实时检测和识别花粉和孢子而开发的单粒子荧光传感器的应用。该传感器的工作原理是从显微镜载玻片上采样的许多单个花粉粒收集荧光发射光谱，其中每个波长与花粉荧光团相关联的四个激发波长（280、350、405 和 450 nm）。传感器还记录每个颗粒的大直径和小直径。询问了八种商业购买的花粉种类中的每一种的大约 25-30 个颗粒。使用四种分类方法分析数据：分层凝聚和 k 均值聚类（无监督）以及随机森林和梯度提升算法（有监督）。手稿的目的是展示分析此类光谱输入数据的计算策略的发展，以支持进一步努力实现传感器数据收集和分析的自动化。两种无监督方法在分离花粉种类（76% k-means，9% HAC）方面表现出不足的准确性，而监督方法表现相似（94-95%）。然后基于其更高的计算速度，利用随机森林算法进一步优化操作参数。分析每个光源对传感器性能的相对重要性突出了可能有助于降低传感器成本的方法，同时最大限度地降低分析质量。结果为将此传感器和类似传感器应用于环境花粉检测和分类提供了框架。版权所有 © 2020 美国气溶胶研究协会