LPC2E-CNRS, 3A Avenue de la Recherche Scientifique, CEDEX 2, 45071 Orléans, France.
LIFY-AIR, Le LAB'O, 1 Avenue du Champ de Mars, 45100 Orléans, France.
Sensors (Basel). 2023 Mar 9;23(6):2964. doi: 10.3390/s23062964.
The monitoring of airborne pollen has received much attention over the last decade, as the prevalence of pollen-induced allergies is constantly increasing. Today, the most common technique to identify airborne pollen species and to monitor their concentrations is based on manual analysis. Here, we present a new, low-cost, real-time optical pollen sensor, called Beenose, that automatically counts and identifies pollen grains by performing measurements at multiple scattering angles. We describe the data pre-processing steps and discuss the various statistical and machine learning methods that have been implemented to distinguish different pollen species. The analysis is based on a set of 12 pollen species, several of which were selected for their allergic potency. Our results show that Beenose can provide a consistent clustering of the pollen species based on their size properties, and that pollen particles can be separated from non-pollen ones. More importantly, 9 out of 12 pollen species were correctly identified with a prediction score exceeding 78%. Classification errors occur for species with similar optical behaviour, suggesting that other parameters should be considered to provide even more robust pollen identification.
在过去的十年中，空气中花粉的监测受到了广泛关注，因为花粉引起的过敏症的患病率一直在不断增加。如今，识别空气传播花粉种类并监测其浓度的最常见技术是基于手动分析。在这里，我们提出了一种新的、低成本、实时的光学花粉传感器，称为 Beenose，它通过在多个散射角进行测量来自动计数和识别花粉粒。我们描述了数据预处理步骤，并讨论了为区分不同花粉种类而实现的各种统计和机器学习方法。该分析基于一组 12 种花粉，其中一些花粉因其致敏能力而被选中。我们的结果表明，Beenose 可以根据花粉的大小特性提供一致的花粉种类聚类，并且可以将花粉颗粒与非花粉颗粒分离。更重要的是，12 种花粉中有 9 种被正确识别，预测分数超过 78%。对于具有相似光学特性的物种，会出现分类错误，这表明应该考虑其他参数以提供更可靠的花粉识别。
