# name
Early fire detection based on gas sensor arrays: Multivariate calibration and validation

# metadata.dc.description.abstract[].value
Smouldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks—they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smouldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m3). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community.

# metadata.dc.description.sponsorship[].value
This research was supported by ENIAC-JU-2013-1(621272), via the SAFESENS project (Sensor Technologies for Enhanced Safety and Security of Buildings and its Occupants) and Spanish project SENSIBLE. “Sensores inteligentes para edificios más seguros” (PCIN-2013-195). We would like to acknowledge, the Departament d’Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya (expedient 2017 SGR 1721 and 2017 SGR 952); the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya; and the European Social Fund (ESF). This work was partially funded by ACCIÓ (INNOTECRD18-1-0054); the Spanish MINECO program (DPI2017-89827-R); the European Research Council (H2020-780262-SHARE4RARE); and Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), initiatives of Instituto de Investigación Carlos III (ISCIII). Additional financial support has been provided by the Institut de Bioenginyeria de Catalunya (IBEC). IBEC is a member of the CERCA Programme/Generalitat de Catalunya. AS acknowledges the sponsorship of the Mexican Science and Technology Council- CONACYT. JF acknowledges the support from the Serra Húnter program. JE and BZ acknowledge The German Federal Ministry of Education and Research (BMBF, 16ES0229).

# metadata.dc.relation.ispartof[].value
Sensors and Actuators B: Chemical, 2021, vol. 352, num. 1

# metadata.dc.rights[].value
cc by-nc-nd (c) Solorzano Soria, Ana Maria et al, 2021

# metadata.dc.source[].value
Articles publicats en revistes (Institut de Bioenginyeria de Catalunya (IBEC))

# metadata.dc.title[].value
Early fire detection based on gas sensor arrays: Multivariate calibration and validation