Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning
Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning
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DOI：
10.1007/s00216-009-2794-9
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发表时间：
2009-07-01
影响因子：
4.3
通讯作者：
Bersani, M.
中科院分区：
文献类型：
--
作者：
Dell'Anna, R.;Lazzeri, P.;Bersani, M.
首次采用中红外傅里叶变换红外（FT-IR）显微光谱技术，结合无监督和有监督的多元统计方法，研究了过敏相关花粉的鉴别和分类。收集了11种不同类群的花粉样品，其开花期间的室外空气浓度通常由空气生物学监测网络测量。无监督的层次聚类分析提供了有价值的信息的再现性的FT-IR光谱的同一分类群获得的花粉粒在25 × 25 μ m（2）的区域内或从一组颗粒内100 × 100 μ m（2）的区域。至于监督学习方法，实现了最好的结果，使用K近邻分类器和留一交叉验证程序的数据集组成的单花粉粒光谱（总体精度84%）。因此，傅立叶变换红外显微光谱是一种可靠的方法，过敏性花粉的歧视和分类。还讨论了其在大气生物监测站实际应用的局限性。
The discrimination and classification of allergy-relevant pollen was studied for the first time by mid-infrared Fourier transform infrared (FT-IR) microspectroscopy together with unsupervised and supervised multivariate statistical methods. Pollen samples of 11 different taxa were collected, whose outdoor air concentration during the flowering time is typically measured by aerobiological monitoring networks. Unsupervised hierarchical cluster analysis provided valuable information about the reproducibility of FT-IR spectra of the same taxon acquired either from one pollen grain in a 25 x 25 mu m(2) area or from a group of grains inside a 100 x 100 mu m(2) area. As regards the supervised learning method, best results were achieved using a K nearest neighbors classifier and the leave-one-out cross-validation procedure on the dataset composed of single pollen grain spectra (overall accuracy 84%). FT-IR microspectroscopy is therefore a reliable method for discrimination and classification of allergenic pollen. The limits of its practical application to the monitoring performed in the aerobiological stations were also discussed.