预处理光谱数据,突出材料的光谱特征

IF 3 Q2 CHEMISTRY, ANALYTICAL Analytical science advances Pub Date : 2024-10-10 DOI:10.1002/ansa.202400018
Francisco Javier Esquivel, José Luis Romero-Béjar, José Antonio Esquivel
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引用次数: 0

摘要

光谱仪产生的大量数据集通常被称为大数据,对化学、地质学、考古学、药学和人类学等各个领域提取矿物成分的宝贵信息起着至关重要的作用。对这些光谱数据的分析属于大数据范畴,需要应用主成分分析和聚类分析等先进的统计方法。然而,光谱仪记录的大量数据(大数据)使得从原始数据中获得可靠结果变得非常困难。通常的方法是对原始数据进行不同的数学变换。在此,我们建议使用仿射变换来突出每个样本的基本特征。最后,我们将对美国国家航空航天局喷气推进实验室记录的矿物或岩石光谱数据进行应用。举例来说,我们分析了三种矿物样本,它们具有不同的成因和副成因,属于不同的矿物学组。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Preprocessing of spectroscopic data to highlight spectral features of materials

The study of the extensive data sets generated by spectrometers, which are of the type commonly referred to as big data, plays a crucial role in extracting valuable information on mineral composition in various fields, such as chemistry, geology, archaeology, pharmacy and anthropology. The analysis of these spectroscopic data falls into the category of big data, which requires the application of advanced statistical methods such as principal component analysis and cluster analysis. However, the large amount of data (big data) recorded by spectrometers makes it very difficult to obtain reliable results from raw data. The usual method is to carry out different mathematical transformations of the raw data. Here, we propose to use the affine transformation for highlight the underlying features for each sample. Finally, an application to spectroscopic data collected from minerals or rocks recorded by NASA's Jet Propulsion Laboratory is performed. An illustrative example has been included by analysing three mineral samples, which have different diageneses and parageneses and belong to different mineralogical groups.

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