Classification of colorimetric sensor data using time series

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dc.contributor.authorFrancis, Deena P.
dc.contributor.authorLaustsen, Milan
dc.contributor.authorBabamoradi, Hamid
dc.contributor.authorMogensen, Jesper
dc.contributor.authorDossi, Eleftheria
dc.contributor.authorJakobsen, Mogens H.
dc.contributor.authorAlstrøm, Tommy S.
dc.date.accessioned2022-01-22T15:28:55Z
dc.date.available2022-01-22T15:28:55Z
dc.date.issued2021-09-12
dc.description.abstractColorimetric sensors are widely used as pH indicators, medical diagnostic devices and detection devices. The colorimetric sensor captures the color changes of a chromic chemical (dye) or array of chromic chemicals when exposed to a target substance (analyte). Sensing is typically carried out using the difference in dye color before and after exposure. This approach neglects the kinetic response, that is, the temporal evolution of the dye, which potentially contains additional information. We investigate the importance of the kinetic response by collecting a sequence of images over time. We applied end-to-end learning using three different convolution neural networks (CNN) and a recurrent network. We compared the performance to logistic regression, k-nearest-neighbor and random forest, where these methods only use the difference color from start to end as feature vector. We found that the CNNs were able to extract features from the kinetic response profiles that significantly improves the accuracy of the sensor. Thus, we conclude that the kinetic responses indeed improves the accuracy, which paves the way for new and better chemical sensors based on colorimetric responses.en_UK
dc.identifier.citationFrancis DP, Laustsen M, Babamoradi H, et al., (2021) Classification of colorimetric sensor data using time series. In: Artificial Intelligence and Machine Learning in Defense Applications III, 13-18 September 2021, Virtual Eventen_UK
dc.identifier.isbn9781510645844
dc.identifier.issn0277-786X
dc.identifier.urihttps://doi.org/10.1117/12.2600182
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/17479
dc.language.isoenen_UK
dc.publisherSociety of Photo-Optical Instrumentation Engineers (SPIE)en_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectcolorimetric sensoren_UK
dc.subjectdeep learningen_UK
dc.subjectkinetic responseen_UK
dc.subjecttime series classificationen_UK
dc.subjectconvolutional neural networken_UK
dc.titleClassification of colorimetric sensor data using time seriesen_UK
dc.typeConference paperen_UK

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