Classification of colorimetric sensor data using time series

Deena P. Francis, Milan Laustsen, Hamid Babamoradi, Jesper Mogensen, Eleftheria Dossi, Mogens H. Jakobsen, Tommy S. Alstrøm

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Colorimetric 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.
Original languageEnglish
Title of host publicationArtificial Intelligence and Machine Learning in Defense Applications III
EditorsJudith Dijk
Number of pages9
Place of PublicationWashington
PublisherSPIE - International Society for Optical Engineering
Publication date2021
Article number118700L
ISBN (Print)9781510645844
ISBN (Electronic)9781510645851
Publication statusPublished - 2021
EventSPIE Security + Defence, 2021 - Online event, Madrid, Spain
Duration: 13 Sept 202116 Sept 2021


ConferenceSPIE Security + Defence, 2021
LocationOnline event
SeriesProceedings of SPIE - The International Society for Optical Engineering


  • Colorimetric sensor
  • Deep learning
  • Kinetic response
  • Time series classification
  • Convolutional neural network


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