论文标题
主要组件的基本数量和几乎无训练的模型用于光谱分析
Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis
论文作者
论文摘要
通过对多气体混合物数据集的研究，我们表明，在多组分光谱分析中，保留基本信息所需的功能性或非功能性主成分的数量与混合组中的独立成分的数量相同。由于不同气体分子之间的相互依赖性，可以建立从主要成分到混合物成分的一对一投影，从而导致光谱定量的显着简化。此外，借助每个组成部分的摩尔灭绝系数，可以直接从系数中提取完整的主组件集，并且学习模型几乎不需要训练样本。与其他方法相比，所提出的方法提供了所需的记忆尺寸较小的快速准确的光谱定量解决方案。
Through a study of multi-gas mixture datasets, we show that in multi-component spectral analysis, the number of functional or non-functional principal components required to retain the essential information is the same as the number of independent constituents in the mixture set. Due to the mutual in-dependency among different gas molecules, near one-to-one projection from the principal component to the mixture constituent can be established, leading to a significant simplification of spectral quantification. Further, with the knowledge of the molar extinction coefficients of each constituent, a complete principal component set can be extracted from the coefficients directly, and few to none training samples are required for the learning model. Compared to other approaches, the proposed methods provide fast and accurate spectral quantification solutions with a small memory size needed.
