Publications

Smoldering 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 smoldering 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 m(3)). 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 (doi: 10.5281/zenodo.5643074).

JTD Keywords: Calibration, Chemical sensors, Co2, Early fire, Early fire detection, En-54, Fire alarm, Fire detection, Fire room, Fires, Gas detectors, Gas emissions, Gas sensors, Pattern recognition, Public dataset, Sensor arrays, Sensors array, Signatures, Smoke, Smoke detector, Smoke detectors, Standard fire, Standard fire room, Support vector machines, Temperature, Toxicity, Volatile organic compounds

Quantification of odor emissions in wastewater treatment plants (WWTPs) is key to minimize odor impact to surrounding communities. Odor measurements in WWTPs are usually performed via either expensive and discontinuous olfactometry hydrogen sulfide detectors or via fixed electronic noses. We propose a portable lightweight electronic nose specially designed for real-time odor monitoring in WWTPs using small drones. The so-called RHINOS e-nose allows odor measurements with high spatial resolution, and its accuracy is only slightly worse than that of dynamic olfactometry. The device has been calibrated using odor samples collected in a WWTP in Spain over a period of six months and validated in the same WWTP three weeks after calibration. The promising results obtained support the suitability of the proposed instrument to identify the odor sources having the highest emissions, which may give a useful indication to the plant managers as regards odor control and abatement.© 2021 The Author(s).

JTD Keywords: biofiltration, calibration transfer, chemical sensor arrays, chemistry, drift compensation, engineering, environmental chemical engineering, h2s, model, oxide gas sensors, removal, sensor, system, Chemistry, Engineering, Environmental chemical engineering, Sensor, Sensor system, Variable selection methods

The human olfactory system remains one of the most challenging biological systems to replicate. Humans use it without thinking, where it can equally offer protection from harm and bring enjoyment in equal measure. It is the system’s ability to detect and analyze complex odors, without the need for specialized infra-structure, that is the envy of many scientists. The field of artificial olfaction has recruited and stimulated interdisciplinary research and commercial development for several applications that include malodor measurement, medical diagnostics, food and beverage quality, environment and security. Over the last century, innovative engineers and scientists have been focused on solving a range of problems associated with measurement and control of odor. The IEEE Sensors Journal has published Special Issues on olfaction in 2002 and 2012. Here we continue that coverage. In this article, we summarize early work in the 20th Century that served as the foundation upon which we have been building our odor-monitoring instrumental and measurement systems. We then examine the current state of the art that has been achieved over the last two decades as we have transitioned into the 21st Century. Much has been accomplished, but great progress is needed in sensor technology, system design, product manufacture and performance standards. In the final section, we predict levels of performance and ubiquitous applications that will be realized during in the mid to late 21st Century.

JTD Keywords: air-quality, breath analysis, calibration transfer, chemical sensor arrays, chemosensor arrays, drift compensation, electronic nose, gas sensors, headspace sampling, machine learning, machine olfaction, odor detection, plume structure, voc analysis, Artificial olfaction, Electrodes, Electronic nose, Electronic nose technology, Headspace sampling, Instruments, Machine learning, Machine olfaction, Monitoring, Odor detection, Olfactory, Sensor phenomena and characterization, Sensors, Temperature sensors, Voc analysis

Abstract In this paper we study the role of redundant sensory information to prevent the performance degradation of a chemical sensor array for different distributions of sensor failures across sensor types. The large amount of sensing conditions with two different types of redundancy provided by our sensor array makes possible a comprehensive experimental study. Particularly, our sensor array is composed of 8 different types of commercial MOX sensors modulated in temperature with two redundancy levels: (1) 12 replicates of each sensor type for a total of 96 sensors and (2) measurements using 16 load resistors per sensors for a total of 1536 redundant measures per second. We perform two experiments to determine the performance degradation of the array with increasing number of damaged sensors in two different scenarios of sensor faults distributions across sensor types. In the first experiment, we characterize the diversity and redundancy of the array for increasing number of damaged sensors. To measure diversity and redundancy, we proposed a functional definition based on clustering of sensor features. The second experiment is devoted to determine the performance degradation of the array for the effect of faulty sensors. To this end, the system is trained to separate ethanol, acetone and butanone at different concentrations using a PCA–LDA model. Test set samples are corrupted by means of three different simulated types of faults. To evaluate the performance of the array we used the Fisher score as a measure of odour separability. Our results show that to exploit to the utmost the redundancy of the sensor array faulty sensory units have to be distributed uniformly across the different sensor types.

JTD Keywords: Gas sensor arrays, Sensor redundancy, Sensor diversity, Sensor faults aging, Sensor damage, MOX sensors, Large sensor arrays

Over the last two decades, electronic nose research has produced thousands of research works. Many of them were describing the ability of the e-nose technology to solve diverse applications in domains ranging from food technology to safety, security, or health. It is, in fact, in the biomedical field where e-nose technology is finding a research niche in the last years. Although few success stories exist, most described applications never found the road to industrial or clinical exploitation. Most described methodologies were not reliable and were plagued by numerous problems that prevented practical application beyond the lab. This work emphasizes the need of external validation in machine olfaction. I describe some statistical and methodological pitfalls of the e-nose practice and I give some best practice recommendations for researchers in the field.

JTD Keywords: Chemical sensor arrays, Pattern recognition, Chemometrics, Electronic noses, Robustness, Signal and data processing

Abstract In this paper we study the role of redundant sensory information to prevent the performance degradation of a chemical sensor array as the number of faulty sensors increases. The large amount of sensing conditions with two different types of redundancy provided by our sensor array makes possible a comprehensive experimental study. Particularly, our sensor array is composed of 8 different types of commercial MOX sensors modulated in temperature with two redundancy levels: 1) 12 replicates of each sensor type for a total of 96 sensors, and 2) measurements using 16 load resistors per sensors for a total of 1536 redundant measures per second. The system is trained to identify ethanol, acetone and butanone using a PCA-LDA model. Test set samples are corrupted by means of three different simulated types of faults. To evaluate the tolerance of the array against sensor failure, the Fisher Score is used as a figure of merit for the corrupted test set samples projected on the PCA-LDA model.

JTD Keywords: Gas ensor arrays, sensor redundancy, MOX sensors, large sensor arrays.

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing.

JTD Keywords: Chemical sensors, Electronic nose, Intelligent sensors, Measurement techniques, Sensor arrays, Sensor systems