{"id":17150,"date":"2014-11-19T15:00:46","date_gmt":"2014-11-19T14:00:46","guid":{"rendered":"http:\/\/ibecbarcelona.eu\/?page_id=17150"},"modified":"2026-01-16T14:24:28","modified_gmt":"2026-01-16T13:24:28","slug":"signal-and-information-processing-for-sensing-systems","status":"publish","type":"page","link":"https:\/\/ibecbarcelona.eu\/es\/sensingsys","title":{"rendered":"Signal and Information Processing for Sensing Systems"},"content":{"rendered":"
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About<\/h2>\n
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Current smart instrumentation using multi-sensors and\/or spectrometers provides a wealth of data that requires sophisticated signal and data processing approaches to extract the hidden information.<\/h4>\n\n\n\n
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Our group develops algorithmic solutions for the automatic processing of Gas Sensor Array, Gas Chromatography – Ion Mobility Spectrometry (IMS), Nuclear Magnetic Resonance, and Mass Spectrometry (GC\/LC-MS, MSI) data for metabolomics, food, and environmental samples.<\/p>\n<\/blockquote>\n\n\n\n

In this context, we are interested in intelligent chemical instruments for the detection of gases, volatile compounds, and smells. These systems can be based on an array of nonspecific chemical sensors with a pattern recognition engine, taking inspiration from the olfactory system. Some spectrometries, e.g. Ion Mobility Spectrometry, are capable of very fast analysis with good detection limits but poor selectivity. These technologies have been proposed for the fast determination of the volatolome (volatile fraction of the metabolome), instead of the reference technique of gas chromatography \u2013 mass spectrometry.<\/p>\n\n\n\n

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During 2023 our research has been focused on: <\/p>\n\n\n\n

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  1. Development of computational metabolomics workflows based on advanced statistics and machine learning. We have applied these methods to the discovery of metabolic biomarkers to identify patients at risk after colorectal cancer surgery.  <\/li>\n<\/ol>\n\n\n\n
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    1. Computational metabolic biomarker discovery for ventilation therapy needs in COVID patients in intensive care units.  <\/li>\n<\/ol>\n\n\n\n
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      1. Optimization of a full workflow for the analysis of Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data, and its implementation in an open source R-package made available for the community.  <\/li>\n<\/ol>\n\n\n\n
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        1. Development of a Forced Oscillation Technique apparatus and controlling software for the measurement of the respiratory impedance. The device is controlled by a mobile app, and it has full IoT capabilites (based on Microsoft Azure) for clinician remote access to measurement data. Hardware and Software are open source.  <\/li>\n<\/ol>\n\n\n\n
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          1. Development of drones with machine olfaction capabilities for gas source localization and mapping. Our results have shown that nanodrones with proper signal processing can locate sources in indoor scenarios, particularly for chemical sources located above the drone. <\/li>\n<\/ol>\n\n\n\n
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            1. Development of drones with machine olfaction capabilities for outdoor operation aiming to estimate odour concentration as for EN13725 in flight conditions over wastewater treatment plants.  <\/li>\n<\/ol>\n\n\n\n
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              1. Development of methods of urine analysis based on GC-IMS.  <\/li>\n<\/ol>\n\n\n\n
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                1. Analysis of urine GC-IMS data to develop predictive models of Colorectal Cancer.  <\/li>\n<\/ol>\n\n\n\n


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                  Fig. 1. A) Main steps of the GCIMS R package workflow; B) Image of the Regions of Interest  detected for all the samples, where each sample is represented by a different color; C) Score plot of the second and third Principal Components of the processed urine data. Red and green markers correspond to female and male individuals, respectively.   <\/em><\/figcaption><\/figure>\n\n\n\n<\/div>

                  Staff<\/h2>\n
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