Time series hyperspectral chemical imaging data: challenges, solutions and applications

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Imaging Science

Background:

• Hyperspectral chemical imaging (HCI) generates 3D data (hypercubes) with spatial and spectral information.
• Time series HCI enables comprehensive understanding of dynamic multi-component systems.
• Large, multivariate datasets from time series HCI pose significant analytical challenges.

Purpose of the Study:

• To present modeling strategies for analyzing time series hyperspectral chemical imaging data.
• To address challenges including dimensionality reduction, temporal variation, and instrumental drift.
• To illustrate solutions with real-world time series HCI examples.

Main Methods:

• Multiway analysis for high-dimensional data.
• Object tracking for monitoring sample changes.
• Multivariate curve resolution and non-linear regression for spectral data analysis.

Main Results:

• Demonstrated applicability of presented methods to time series HCI data.
• Successfully addressed challenges of dimensionality and temporal dynamics.
• Provided a framework for comprehensive analysis of complex chemical imaging experiments.

Conclusions:

• Time series HCI offers powerful insights into dynamic chemical processes.
• Proposed modeling strategies effectively handle the complexity of HCI data.
• This work facilitates advanced analysis and interpretation of time series HCI experiments.