Time series hyperspectral chemical imaging data: Challenges, solutions and applications
| Autor: | Gerard Downey, Carlos Esquerre, Colm P. O'Donnell, Aoife Gowen, James Burger, Federico Marini |
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| Rok vydání: | 2011 |
| Předmět: |
Chemical imaging
computer.software_genre Biochemistry Analytical Chemistry Imaging Three-Dimensional Statistics Cluster Analysis Environmental Chemistry Spectroscopy Spectroscopy Near-Infrared Series (mathematics) Pixel Chemistry Dimensionality reduction Hyperspectral imaging Data structure chemical hyperspectral hyperspectral chemical imaging imaging series time time series Kinetics Spectrophotometry Video tracking Multivariate Analysis Hypercube Data mining Agaricales computer Algorithms |
| Zdroj: | Analytica Chimica Acta. 705:272-282 |
| ISSN: | 0003-2670 |
| DOI: | 10.1016/j.aca.2011.06.031 |
| Popis: | Hyperspectral chemical imaging (HCI) integrates imaging and spectroscopy resulting in three-dimensional data structures, hypercubes, with two spatial and one wavelength dimension. Each spatial image pixel in a hypercube contains a spectrum with >100 datapoints. While HCI facilitates enhanced monitoring of multi-component systems; time series HCI offers the possibility of a more comprehensive understanding of the dynamics of such systems and processes. This implies a need for modeling strategies that can cope with the large multivariate data structures generated in time series HCI experiments. The challenges posed by such data include dimensionality reduction, temporal morphological variation of samples and instrumental drift. This article presents potential solutions to these challenges, including multiway analysis, object tracking, multivariate curve resolution and non-linear regression. Several real world examples of time series HCI data are presented to illustrate the proposed solutions. |
| Databáze: | OpenAIRE |
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