Browsing by Author "Francis, Deena P."

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Classification of colorimetric sensor data using time series
    (Society of Photo-Optical Instrumentation Engineers (SPIE), 2021-09-12) Francis, Deena P.; Laustsen, Milan; Babamoradi, Hamid; Mogensen, Jesper; Dossi, Eleftheria; Jakobsen, Mogens H.; Alstrøm, Tommy S.
    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.
  • Thumbnail Image
    ItemOpen Access
    Machine learning methods for the detection of explosives, drugs and precursor chemicals gathered using a colorimetric sniffer sensor
    (Royal Society of Chemistry, 2023-04-18) Francis, Deena P.; Laustsen, Milan; Dossi, Eleftheria; Treiberg, Tuule; Hardy, Iona; Shiv, Shai Hvid; Hansen, Bo Svarrer; Mogensen, Jesper; Jakobsen, Mogens H.; Alstrøm, Tommy S.
    Colorimetric sensing technology for the detection of explosives, drugs, and their precursor chemicals is an important and effective approach. In this work, we use various machine learning models to detect these substances from colorimetric sensing experiments conducted in controlled environments. The detection experiments based on the response of a colorimetric chip containing 26 chemo-responsive dyes indicate that homemade explosives (HMEs) such as hexamethylene triperoxide diamine (HMTD), triacetone triperoxide (TATP), and methyl ethyl ketone peroxide (MEKP) used in improvised explosives devices are detected with true positive rate (TPR) of 70–75%, 73–90% and 60–82% respectively. Time series classifiers such as Convolutional Neural Networks (CNN) are explored, and the results indicate that improvements can be achieved with the use of kinetics of the chemical responses. The use of CNNs is limited, however, to scenarios where a large number of measurements, typically in the range of a few hundred, of each analyte are available. Feature selection of important dyes using the Group Lasso (GPLASSO) algorithm indicated that certain dyes are more important in discrimination of an analyte from ambient air. This information could be used for optimizing the colorimetric sensor and extend the detection to more analytes.