Optical Sensors Group, Institute of Analytical Chemistry and Food Chemistry , Graz University of Technology , Graz , Austria.
Plant biology and Ecology Department, Faculty of Science and Technology , University of the Basque Country (UPV/EHU) , Leioa 48940 , Spain.
Environ Sci Technol. 2018 Dec 18;52(24):14266-14274. doi: 10.1021/acs.est.8b04528. Epub 2018 Nov 30.
Early stage identification of harmful algal blooms (HABs) has gained significance for marine monitoring systems over the years. Various approaches for in situ classification have been developed. Among them, pigment-based taxonomic classification is one promising technique for in situ characterization of bloom compositions, although it is yet underutilized in marine monitoring programs. To demonstrate the applicability and importance of this powerful approach for monitoring programs, we combined an ultra low-cost and miniaturized multichannel fluorometer with Fisher's linear discriminant analysis (LDA). This enables the real-time characterization of algal blooms at order level based on their spectral properties. The classification capability of the algorithm was examined with a leave-one-out cross validation of 53 different unialgal cultures conducted in terms of standard statistical measures and independent figures of merit. The separation capability of the linear discriminant analysis was further successfully examined in mixed algal suspensions. Besides this, the impact of the growing status on the classification capability was assessed. Further, we provide a comprehensive study of spectral features of eight different phytoplankton phyla including an extensive study of fluorescence excitation spectra and marker pigments analyzed via HPLC. The analyzed phytoplankton species belong to the phyla of Cyanobacteria, Dinophyta (Dinoflagellates), Bacillariophyta (Diatoms), Haptophyta, Chlorophyta, Ochrophyta, Cryptophyta, and Euglenophyta.
多年来,有害藻华(HAB)的早期识别对于海洋监测系统变得越来越重要。已经开发了各种原位分类方法。其中,基于色素的分类方法是一种很有前途的原位描述浮游生物组成的技术,尽管它在海洋监测计划中尚未得到充分利用。为了展示这种强大的监测方法的适用性和重要性,我们将超低成本和微型多通道荧光计与 Fisher 的线性判别分析(LDA)相结合。这使得能够根据藻类的光谱特性实时对藻类进行分类。通过对 53 种不同的单藻培养物进行逐一交叉验证,使用标准统计指标和独立的优良指标对算法的分类能力进行了检验。线性判别分析的分离能力也在混合藻液中得到了成功的检验。除此之外,还评估了生长状态对分类能力的影响。此外,我们对 8 个不同浮游植物门的光谱特征进行了全面研究,包括通过 HPLC 分析荧光激发光谱和标记色素的广泛研究。分析的浮游植物物种属于蓝藻门、甲藻门(甲藻)、硅藻门(硅藻)、甲藻门、绿藻门、黄藻门、隐藻门和眼虫门。