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浮游植物的多变量光学计算分类,第三部分:演示。

Taxonomic classification of phytoplankton with multivariate optical computing, part III: demonstration.

机构信息

Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA.

出版信息

Appl Spectrosc. 2013 Jun;67(6):640-7. doi: 10.1366/12-06785.

Abstract

We describe the automatic analysis of fluorescence tracks of phytoplankton recorded with a fluorescence imaging photometer. The optical components and construction of the photometer were described in Part I and Part II of this series in this issue. An algorithm first isolates tracks corresponding to a single phytoplankter transit in the nominal focal plane of a flow cell. Then, the fluorescence streaks in the track that correspond to individual optical elements on the filter wheel are identified. The fluorescence intensity of each streak is integrated and used to calculate ratios. This approach was tested using 853 fluorescence measurements of the coccolithophore Emiliania huxleyi and the diatom Thalassiosira pseudonana. Average intensity ratios for the two classes closely follow those predicted in Part I of this series, with a distribution of ratios in each class that is consistent with the signal-to-noise ratio calculations in Part II for single cells. No overlap of the two class ratios was observed, yielding perfect classification.

摘要

我们描述了使用荧光成像光度计记录的浮游植物荧光轨迹的自动分析。光度计的光学组件和结构在本系列的第 I 部分和第 II 部分中进行了描述。该算法首先分离出在流动池的标称焦平面上单个浮游植物通过的轨迹。然后,识别轨迹中与滤光轮上的单个光学元件相对应的荧光条纹。对每个条纹的荧光强度进行积分并用于计算比值。该方法使用共球藻和拟菱形藻的 853 次荧光测量进行了测试。两类的平均强度比与本系列第 I 部分中预测的结果非常接近,每类的比值分布与第 II 部分中单个细胞的信噪比计算一致。未观察到两类比值的重叠,实现了完美分类。

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