Institute of Chemical Technologies and Analytics, TU Wien, Getreidemarkt 9, 1060, Vienna, Austria.
Institute of Solide State Physics, TU Wien, Wiedner Hauptstrasse 8, 1040, Vienna, Austria.
Sci Rep. 2017 Jul 28;7(1):6832. doi: 10.1038/s41598-017-07041-x.
Chemical imaging is a powerful tool for understanding the chemical composition and nature of heterogeneous samples. Recent developments in elemental, vibrational, and mass-spectrometric chemical imaging with high spatial resolution (50-200 nm) and reasonable timescale (a few hours) are capable of providing complementary chemical information about various samples. However, a single technique is insufficient to provide a comprehensive understanding of chemically complex materials. For bulk samples, the combination of different analytical methods and the application of statistical methods for extracting correlated information across different techniques is a well-established and powerful concept. However, combined multivariate analytics of chemical images obtained via different imaging techniques is still in its infancy, hampered by a lack of analytical methodologies for data fusion and analysis. This study demonstrates the application of multivariate statistics to chemical images taken from the same sample via various methods to assist in chemical structure determination.
化学成像是理解不均匀样品化学组成和性质的有力工具。近年来，元素、振动和质谱化学成像技术取得了进展，具有高空间分辨率（50-200nm）和合理的时间尺度（数小时），能够提供各种样品的补充化学信息。然而，单一技术不足以全面了解化学复杂材料。对于块状样品，结合不同的分析方法并应用统计方法从不同技术中提取相关信息是一个成熟且强大的概念。然而，通过不同成像技术获得的化学图像的组合多元分析仍然处于起步阶段，受到缺乏数据分析融合和分析的分析方法学的限制。本研究通过各种方法对同一样品的化学图像进行多元统计分析，以协助确定化学结构。
