Assessing Miniaturized Sensor Performance Using Supervised Learning, with Application to Drug and Explosive Detection

Downloaded from orbit.dtu.dk on: Sep 28, 2021 Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection Alstrøm, Tommy Sonne Publication date: 2013 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Alstrøm, T. S. (2013). Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection. Technical University of Denmark. IMM-PHD-2012 No. 292 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. 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Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection Tommy Sonne Alstrøm Kongens Lyngby 2012 IMM-PhD-2012-292 Technical University of Denmark Informatics and Mathematical Modelling Building 321, DK-2800 Kongens Lyngby, Denmark Phone +45 45253351, Fax +45 45882673 [email protected] www.imm.dtu.dk IMM-PhD-2012-292 Summary (English) This Ph.D. thesis titled “Assessing Miniaturized Sensor Performance using Su- pervised Learning, with Application to Drug and Explosive Detection” is a part of the strategic research project “Miniaturized sensors for explosives detection in air” funded by the Danish Agency for Science and Technology’s, Program Commission on Nanoscience Biotechnology and IT (NABIIT), case number: 2106-07-0031. The project, baptized “Xsense” was led by professor Anja Boisen, DTU Nanotech. DTU Informatics participate in the project as data analysis partner. This thesis presents advances in the area of detection of vapor emanated by explosives and drugs, similar to an electronic nose. To evaluate sensor responses a data processing and evaluation pipeline is required. The work presented herein focuses on the feature extraction, feature representation and sensor accuracy. Thus the primary aim of this thesis is twofold; firstly, present methods suit- able for assessing sensor accuracy, and secondly improve sensor performance by enhancing the preprocessing and feature extraction. Five different miniaturized sensors are presented. Naturally, each sensor require its own special preprocessing and feature extraction techniques before the sensor responses can be applied to supervised learning algorithms. The technologies used for sensing consist of Calorimetry, Cantilevers, Chemoselective compounds, Quartz Crystal Microbalance and Surface Enhanced Raman Scattering. Each of the sensors have their own strength and weaknesses. The reasoning for using multiple sensors was the desire to investigate the feasibility for an integrated multisensor solution. A unique setup of multiple independent detectors is able to vastly enhance accuracy compared to what a single sensor can deliver. ii As we are detecting hazardous compounds this enables the need for sensors to deliver not only decisions but also certainty about decisions. This requirement is handled by introducing classifiers that offer posterior probabilities and not only decisions. The three probabilistic classification models utilized are Artificial Neural Networks, Logistic Regression and Gaussian Processes. Often, there is no tradition for using these methods in the communities of the prescribed sensors. Here, a method of too much complexity is often undesired so it is a balance when to utilize more sophisticated methods. For this reason, an array of methods that only discriminate between samples are used as baseline. The methods used vary from sensor to sensor, as these methods serve as baseline performance when introducing new methods. The most widely used baseline method in this thesis is the k-nearest-neighbor algorithm. This method is of particular interest in the application of sensors, as the sensors are designed to provide robust and reliable measurements. That means, the sensors are designed to have repeated measurement clusters. Sensor fusion is presented for the sensor based on chemoselective compounds. An array of color changing compounds are handled and in unity they make up an colorimetric sensor array. In this setting it is valuable to qualify which compounds in the colorimetric sensor array are important. That knowledge enables the ability to either reduce the size of the sensor or replace less sensitive and unimportant compounds with more selective and responsive compounds. A framework based on forward selection Gaussian Process classification is demon- strated to successfully identify a set of important compounds. Summary (Danish) Ph.D.-projektet “Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection” er et led i det strategiske forskningsprojekt “Miniaturized sensors for explosives detection in air”, som er af Det Strategiske Forskningsr˚ads Programkomite for Nanovidenskab og -teknologi, Bioteknologi og IT (NABIIT), bevilling 2106-07-0031. Projektet er blevet døbt ”Xsense” og blev ledet af professor Anja Boisen, DTU Nanotech. DTU Informatik deltager i projektet som data analyse partner. Form˚alet med projektet har været at udvikle nano-sensorer med henblik p˚aat skabe grundlaget for en elektronisk næse, som kan detektere farlige stoffer f.eks. sprængstoffer. En s˚adan næse ville kunne bruges i lufthavne, hos anti-terror korps, i afsøgningen af vejsidebomber, m.v. og dermed kraftigt reducere den menneskelige risiko. Forskningsarbejdet har haft to primære m˚al. For det første at præsentere metoder, som var i stand til at vurdere sensorernes nøjagtighed. For det andet at forbedre sensorernes ydeevne gennem optimeret signalbehandling og datamodel- lering. Ved identifikation af farlige stoffer, er der behov for en sensor som kan detektere med høj nøjagtighed, men ogs˚a behov for vurdering af hvor p˚alidelighed denne er. Arbejdet har involveret fem forskellige kemiske nano-sensorer og fokuseret p˚a udvikling, optimering og evaluering af metoder og modeller til databehand- ling. Sensorne viser sig i stand til at detektere sprængstoffer som ofte bliver benyttet af terrorister s˚avel som stoffer i forbindelse narkotika bekæmpelse. Nøjagtigheden kan forbedres hvis systemet indeholder flere sensorer. Fordele iv ved en integreret enhed er undersøgt og de er indikationer at nøjagtigheden kan forbedres i et multisensor system. Preface This thesis was prepared at the Department of Informatics and Mathematical Modeling, Technical University of Denmark, in partial fulfillment of the requirements for acquiring the Ph.D. degree in engineering. The Ph.D. project was a part of the strategic research project named “Xsense - Miniaturized sensors for explosives detection in air”, led by Professor Anja Boisen, DTU Nanotech, and funded by the Danish Agency for Science and Technology’s, Program Commis- sion on Nanoscience Biotechnology and IT (NABIIT). The Xsense project worked towards the development of four individual sensor technologies for detection of explosives. All of the sensor technologies can po- tentially be incorporated into a single miniaturized device. The main hypothesis of the project is that sufficient reliability can only be ensured by merging several independent measuring principles. The main idea behind this Ph.D. project was to develop the signal processing pipeline for each sensor and then perform sensor fusion. This objective however turned out to be a little too optimistic. A complete device was never built and the work herein is exclusively about the processing of data obtained using the individual sensors and the application of machine learning methods to assess the performance of the sensors. Fortunately the work conducted in Xsense has spurred a new project named MUSE, which in many ways is a continuation of Xsense. The application area is different, but the sensor technologies are similar and there is a greater focus on sensor integration. There is also a continuity of personnel. Several of the researchers in MUSE participated in Xsense, and now these people have more experience and know-how. In Xsense, each sensor technology was developed and refined by people at DTU vi Preface Nanotech and Department of Chemistry, Syddansk University respectively. All data was collected by researchers at DTU Nanotech though. Due to the data flow in Xsense, my project turned out to be provider of data analysis knowledge for the researchers developing the sensors. My research has therefore been very much about handling the data produced by these sensors, assessing the sensor performance and then handing the results to the scientists at DTU Nanotech. Thus, whenever data has been handled, the overall goal was always to assess and possibly improve the sensor performance. The starting point was to use existing techniques, and if the sensor was able to deliver flawless performance using these techniques, no refinement in the signal processing was made. For this very reason, the research conducted

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