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    <responseDate>2026-10-14T06:15:01Z</responseDate>
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            <header>
                <identifier>oai:data.mendeley.com/zchkwzh3kk.1</identifier>
                <datestamp>2023-01-23T14:56:38Z</datestamp>
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            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>zieger, Silvia</dc:creator>
    <dc:title>Hyperspetral fluorescence images of an pH+O2 dual-analyte optical chemical sensor</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains examples of the original hyperspectral images used for the calibration of pH/O2 dual-analyte optical chemical sensors (so-called optodes). In addition a spreadsheet summarises the spectral response of the dual-analyte sensor upon data preparation. Those data are published together with the paper &quot;Machine Learning for Optical Chemical Multi-Analyte Imaging | Why we should dare and why it’s not without risks&quot;

For data extraction from the hyperspectral images, data preparation and machine learning model development, please have a look at the programming code written in Python which can be downloaded from GitHub (https://github.com/silviaelisabeth/ML_for_pHandO2).</dc:description>
    <dc:subject>Optical Imaging Technique</dc:subject>
    <dc:subject>Machine Learning</dc:subject>
    <dc:subject>Adaptive Sensing</dc:subject>
    <dc:subject>Analytical Chemistry Data Analysis</dc:subject>
    <dc:contributor>Koren, Klaus</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/zchkwzh3kk.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/zchkwzh3kk.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
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    <dc:date>2023-01-23T14:56:38Z</dc:date>
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