Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, USA.
Appl Spectrosc. 2013 Jun;67(6):620-9. doi: 10.1366/12-06783.
Phytoplankton are single-celled, photosynthetic algae and cyanobacteria found in all aquatic environments. Differential pigmentation between phytoplankton taxa allows use of fluorescence excitation spectroscopy for discrimination and classification. For this work, we applied multivariate optical computing (MOC) to emulate linear discriminant vectors of phytoplankton fluorescence excitation spectra by using a simple filter-fluorometer arrangement. We grew nutrient-replete cultures of three differently pigmented species: the coccolithophore Emiliania huxleyi, the diatom Thalassiosira pseudonana, and the cyanobacterium Synechococcus sp. Linear discriminant analysis (LDA) was used to determine a suitable set of linear discriminant functions for classification of these species over an optimal wavelength range. Multivariate optical elements (MOEs) were then designed to predict the linear discriminant scores for the same calibration spectra. The theoretical performance specifications of these MOEs are described.
浮游植物是单细胞、进行光合作用的藻类和蓝细菌，存在于所有水生环境中。浮游植物类群之间的色素差异允许使用荧光激发光谱进行区分和分类。在这项工作中，我们应用多元光学计算 (MOC) 通过使用简单的滤波器荧光计装置来模拟浮游植物荧光激发光谱的线性判别向量。我们培养了三种不同色素的营养充足的培养物：颗石藻 Emiliania huxleyi、硅藻 Thalassiosira pseudonana 和蓝细菌 Synechococcus sp. 线性判别分析 (LDA) 用于确定一组合适的线性判别函数，以便在最佳波长范围内对这些物种进行分类。然后设计了多元光学元件 (MOE) 来预测相同校准光谱的线性判别分数。本文描述了这些 MOE 的理论性能规格。
