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Issue 7, 2013
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Correlated imaging – a grand challenge in chemical analysis

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Abstract

Correlated chemical imaging is an emerging strategy for acquisition of images by combining information from multiplexed measurement platforms to track, visualize, and interpret in situ changes in the structure, organization, and activities of interesting chemical systems, frequently spanning multiple decades in space and time. Acquiring and correlating information from complementary imaging experiments has the potential to expose complex chemical behavior in ways that are simply not available from single methods applied in isolation, thereby greatly amplifying the information gathering power of imaging experiments. However, in order to correlate image information across platforms, a number of issues must be addressed. First, signals are obtained from disparate experiments with fundamentally different figures of merit, including pixel size, spatial resolution, dynamic range, and acquisition rates. In addition, images are often acquired on different instruments in different locations, so the sample must be registered spatially so that the same area of the sample landscape is addressed. The signals acquired must be correlated in both spatial and temporal domains, and the resulting information has to be presented in a way that is readily understood. These requirements pose special challenges for image cross-correlation that go well beyond those posed in single technique imaging approaches. The special opportunities and challenges that attend correlated imaging are explored by specific reference to correlated mass spectrometric and Raman imaging, a topic of substantial and growing interest.

Graphical abstract: Correlated imaging – a grand challenge in chemical analysis

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Publication details

The article was received on 01 Oct 2012, accepted on 24 Jan 2013 and first published on 29 Jan 2013


Article type: Critical Review
DOI: 10.1039/C3AN36416J
Citation: Analyst, 2013,138, 1924-1939
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    Correlated imaging – a grand challenge in chemical analysis

    R. Masyuko, E. J. Lanni, J. V. Sweedler and P. W. Bohn, Analyst, 2013, 138, 1924
    DOI: 10.1039/C3AN36416J

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