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Statistical analysis of a database of absorption spectra of phytoplankton and pigment concentrations using self-organizing maps

机译：使用自组织图对浮游植物和色素浓度的吸收光谱数据库进行统计分析

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摘要

We present a statistical analysis of a large set of absorption spectra of phytoplankton, measured in natural samples collected from ocean water, in conjunction with detailed pigment concentrations. We processed the absorption spectra with a sophisticated neural network method suitable for classifying complex phenomena, the so-called self-organizing maps (SOM) proposed by Kohonen [Kohonen, Self Organizing Maps (Springer-Verlag, 1984)]. The aim was to compress the information embedded in the data set into a reduced number of classes characterizing the data set, which facilitates the analysis. By processing the absorption spectra, we were able to retrieve well-known relationships among pigment concentrations and to display them on maps to facilitate their interpretation. We then showed that the SOM enabled us to extract pertinent information about pigment concentrations normalized to chlorophyll a. We were able to propose new relationships between the fucoxanthin/Tchl-a ratio and the derivative of the absorption spectrum at 510 nm and between the Tchl-b/Tchl-a ratio and the derivative at 640 nm. Finally, we demonstrate the possibility of inverting the absorption spectrum to retrieve the pigment concentrations with better accuracy than a regression analysis using the Tchl-a concentration derived from the absorption at 440 nm. We also discuss the data coding used to build the self-organizing map. This methodology is very general and can be used to analyze a large class of complex data.
机译：我们对大量浮游植物的吸收光谱进行了统计分析，在从海水中采集的自然样品中测量了浮游植物的吸收光谱，并结合了详细的色素浓度。我们使用适合分类复杂现象的复杂神经网络方法（由Kohonen提出的所谓的自组织图（SOM）[Kohonen，自组织图（Springer-Verlag，1984））处理吸收光谱。目的是将嵌入在数据集中的信息压缩为表征该数据集的数量减少的类，以利于分析。通过处理吸收光谱，我们能够检索到颜料浓度之间的众所周知的关系，并将其显示在地图上以方便其解释。然后，我们证明了SOM使我们能够提取有关标准化为叶绿素a的色素浓度的相关信息。我们能够提出岩藻黄质/ Tchl-a比与510 nm处的吸收光谱的导数之间以及Tchl-b / Tchl-a比与640 nm处的衍生物之间的新关系。最后，我们证明了使用吸收在440 nm处吸收的Tchl-a浓度进行回归分析比使用Tchl-a浓度进行回归分析更准确的方法，从而证明了吸收光谱反转的可能性。我们还将讨论用于构建自组织图的数据编码。这种方法非常通用，可用于分析大量复杂数据。

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