Staff member


Jordi Fonollosa Magrinyà

Postdoctoral Researcher
Signal and Information Processing for Sensing Systems
jfonollosa@ibecbarcelona.eu
+34 934 039 147
Staff member publications

Fonollosa, J., Fernández, L., Gutiérrez-Gálvez, A., Huerta, R., Marco, S., (2016). Calibration transfer and drift counteraction in chemical sensor arrays using Direct Standardization Sensors and Actuators B: Chemical 236, 1044-1053

Inherent variability of chemical sensors makes it necessary to calibrate chemical detection systems individually. This shortcoming has traditionally limited usability of systems based on metal oxide gas sensor arrays and prevented mass-production for some applications. Here, aiming at exploring calibration transfer between chemical sensor arrays, we exposed five twin 8-sensor detection units to different concentration levels of ethanol, ethylene, carbon monoxide, or methane. First, we built calibration models using data acquired with a master unit. Second, to explore the transferability of the calibration models, we used Direct Standardization to map the signals of a slave unit to the space of the master unit in calibration. In particular, we evaluated the transferability of the calibration models to other detection units, and within the same unit measuring days apart. Our results show that signals acquired with one unit can be successfully mapped to the space of a reference unit. Hence, calibration models trained with a master unit can be extended to slave units using a reduced number of transfer samples, diminishing thereby calibration costs. Similarly, signals of a sensing unit can be transformed to match sensor behavior in the past to mitigate drift effects. Therefore, the proposed methodology can reduce calibration costs in mass-production and delay recalibrations due to sensor aging. Acquired dataset is made publicly available.

Keywords: Calibration transfer, Chemical sensors, Direct Standardization, Electronic nose, MOX sensors, Public dataset


Huerta, R., Mosqueiro, T., Fonollosa, J., Rulkov, N.F., Rodríguez-Lujan, I., (2016). Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring Chemometrics and Intelligent Laboratory Systems 157, 169-176

A method for online decorrelation of chemical sensor signals from the effects of environmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by processing data from simultaneous readings of environmental humidity and temperature. The electronic nose setup built for this study included eight metal-oxide sensors, temperature and humidity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor the air for two years in the residence of one of the authors and it collected data continuously during 537 days with a sampling rate of 1 sample per second. To estimate the effects of variations in air humidity and temperature on the chemical sensors' signals, we used a standard energy band model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the model is that variations in sensor conductivity can be expressed as a nonlinear function of changes in the semiconductor energy bands in the presence of external humidity and temperature variations. Fitting this model to the collected data, we confirmed that the most statistically significant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coefficient of determination R2 close to 1. To show how the humidity–temperature correction model works for gas discrimination, we constructed a model for online discrimination among banana, wine and baseline response. This shows that pattern recognition algorithms improve performance and reliability by including the filtered signal of the chemical sensors.

Keywords: Electronic nose, Chemical sensors, Humidity, Temperature, Decorrelation, Wireless e-nose, MOX sensors, Energy band model, Home monitoring


Ziyatdinov, Andrey, Fonollosa, Jordi, Fernández, Luis, Gutiérrez-Gálvez, Agustín, Marco, Santiago, Perera, Alexandre, (2015). Data set from gas sensor array under flow modulation Data in Brief 3, 131-136

Abstract Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation. The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air. The acquired time series show two dominant frequency bands: the low-frequency signal corresponds to a conventional response curve of a sensor in response to a gas pulse, and the high-frequency signal has a clear principal harmonic at the respiration frequency. The data are related to the study in [1], and the data analysis results reported there should be considered as a reference point.

Keywords: Gas sensor array, MOX sensor, Flow modulation, Early detection, Biomimetics, Respiration, Sniffing


Ziyatdinov, Andrey, Fonollosa, Jordi, Fernánndez, Luis, Gutierrez-Gálvez, Agustín, Marco, Santiago, Perera, Alexandre, (2015). Bioinspired early detection through gas flow modulation in chemo-sensory systems Sensors and Actuators B: Chemical 206, 538-547

Abstract The design of bioinspired systems for chemical sensing is an engaging line of research in machine olfaction. Developments in this line could increase the lifetime and sensitivity of artificial chemo-sensory systems. Such approach is based on the sensory systems known in live organisms, and the resulting developed artificial systems are targeted to reproduce the biological mechanisms to some extent. Sniffing behaviour, sampling odours actively, has been studied recently in neuroscience, and it has been suggested that the respiration frequency is an important parameter of the olfactory system, since the odour perception, especially in complex scenarios such as novel odourants exploration, depends on both the stimulus identity and the sampling method. In this work we propose a chemical sensing system based on an array of 16 metal-oxide gas sensors that we combined with an external mechanical ventilator to simulate the biological respiration cycle. The tested gas classes formed a relatively broad combination of two analytes, acetone and ethanol, in binary mixtures. Two sets of low-frequency and high-frequency features were extracted from the acquired signals to show that the high-frequency features contain information related to the gas class. In addition, such information is available at early stages of the measurement, which could make the technique suitable in early detection scenarios. The full data set is made publicly available to the community.11 http://archive.ics.uci.edu/ml/datasets/Gas+sensor+array+under+flow+modulation.

Keywords: Gas sensor array, MOX sensor, Flow modulation, Early detection, Biomimetics, Sniffing


Fonollosa, J., Sheik, S., Huerta, R., Marco, S., (2015). Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring Sensors and Actuators B: Chemical 215, 618-629

Metal oxide (MOX) gas sensors arrays are a predominant technological choice to perform fundamental tasks of chemical detection. Yet, their use has been mainly limited to relatively controlled instrument configurations where the sensor array is placed within a closed measurement chamber. Usually, the experimental protocol is defined beforehand and it includes three stages: the array is first exposed to a gas reference, then to the gas sample, and finally to the reference again to recover the initial state. Such sampling procedure requires signal acquisition during the complete experimental protocol and usually delays the output prediction until the predefined measurement duration is complete. Due to the slow time response of chemical sensors, the completion of the measurement typically requires minutes. In this paper we propose the use of reservoir computing (RC) algorithms to overcome the slow temporal dynamics of chemical sensor arrays, allowing identification and quantification of chemicals of interest continuously and reducing measurement delays. We generated two datasets to test the ability of RC algorithms to provide accurate and continuous prediction to fast varying gas concentrations in real time. Both datasets - one generated with synthetic data and the other acquired from actual gas sensors - provide time series of MOX sensors exposed to binary gas mixtures where concentration levels change randomly over time. Our results show that our approach improves the time response of the sensory system and provides accurate predictions in real time, making the system specifically suitable for online monitoring applications. Finally, the collected dataset and developed code are made publicly available to the research community for further studies.

Keywords: Chemical sensors, Continuous gas prediction, Electronic nose, Real-time detection, Reservoir computing


Fonollosa, Jordi, Neftci, Emre, Rabinovich, Mikhail, (2015). Learning of chunking sequences in cognition and behavior Plos Computational Biology PLoS Computational Biology , 11, (11), e1004592

We often learn and recall long sequences in smaller segments, such as a phone number 858 534 22 30 memorized as four segments. Behavioral experiments suggest that humans and some animals employ this strategy of breaking down cognitive or behavioral sequences into chunks in a wide variety of tasks, but the dynamical principles of how this is achieved remains unknown. Here, we study the temporal dynamics of chunking for learning cognitive sequences in a chunking representation using a dynamical model of competing modes arranged to evoke hierarchical Winnerless Competition (WLC) dynamics. Sequential memory is represented as trajectories along a chain of metastable fixed points at each level of the hierarchy, and bistable Hebbian dynamics enables the learning of such trajectories in an unsupervised fashion. Using computer simulations, we demonstrate the learning of a chunking representation of sequences and their robust recall. During learning, the dynamics associates a set of modes to each information-carrying item in the sequence and encodes their relative order. During recall, hierarchical WLC guarantees the robustness of the sequence order when the sequence is not too long. The resulting patterns of activities share several features observed in behavioral experiments, such as the pauses between boundaries of chunks, their size and their duration. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson’s disease and Schizophrenia.


Fonollosa, J., Neftci, E., Huerta, R., Marco, S., (2015). Evaluation of calibration transfer strategies between Metal Oxide gas sensor arrays Procedia Engineering EUROSENSORS 2015 , Elsevier (Freiburg, Germany) 120, 261-264

Abstract Inherent variability of chemical sensors makes necessary individual calibration of chemical detection systems. This shortcoming has traditionally limited usability of systems based on Metal Oxide (MOX) sensor arrays and prevented mass-production for some applications. Here, aiming at exploring transfer calibration between electronic nose systems, we exposed five identical 8-sensor detection units to controlled gas conditions. Our results show that a calibration model provides more accurate predictions when the tested board is included in the calibration dataset. However, we show that previously built calibration models can be extended to other units using a reduced number of measurements. While baseline correction seems imperative for successful baseline correction, among the different tested strategies, piecewise direct standardization provides more accurate predictions.

Keywords: Electronic nose, Calibration, MOX sensor, Machine Olfaction


Fonollosa, Jordi, Vergara, Alexander, Huerta, R., Marco, Santiago, (2014). Estimation of the limit of detection using information theory measures Analytica Chimica Acta 810, 1-9

Abstract Definitions of the limit of detection (LOD) based on the probability of false positive and/or false negative errors have been proposed over the past years. Although such definitions are straightforward and valid for any kind of analytical system, proposed methodologies to estimate the LOD are usually simplified to signals with Gaussian noise. Additionally, there is a general misconception that two systems with the same LOD provide the same amount of information on the source regardless of the prior probability of presenting a blank/analyte sample. Based upon an analogy between an analytical system and a binary communication channel, in this paper we show that the amount of information that can be extracted from an analytical system depends on the probability of presenting the two different possible states. We propose a new definition of LOD utilizing information theory tools that deals with noise of any kind and allows the introduction of prior knowledge easily. Unlike most traditional LOD estimation approaches, the proposed definition is based on the amount of information that the chemical instrumentation system provides on the chemical information source. Our findings indicate that the benchmark of analytical systems based on the ability to provide information about the presence/absence of the analyte (our proposed approach) is a more general and proper framework, while converging to the usual values when dealing with Gaussian noise.

Keywords: Limit of detection, Information theory, Mutual information, Heteroscedasticity, False positive/negative errors, Gas discrimination and quantification


Sheik, S., Marco, S., Huerta, R., Fonollosa, J., (2014). Continuous prediction in chemoresisitive gas sensors using reservoir computing Procedia Engineering 28th European Conference on Solid-State Transducers (EUROSENSORS 2014) , Eurosensors (Brescia, Italy) 87, 843-846

Although Metal Oxide (MOX) sensors are predominant choices to perform fundamental tasks of chemical detection, their use has been mainly limited to relatively controlled scenarios where a gas sensor array is first exposed to a reference, then to the gas sample, and finally to the reference again to recover the initial state. In this paper we propose the use of MOX sensors along with Reservoir Computing algorithms to identify chemicals of interest. Our approach allows continuous gas monitoring in simple experimental setups without the requirement of acquiring recovery transient of the sensors, thereby making the system specifically suitable for online monitoring applications.

Keywords: Chemical sensing, Reservoir computing, Gas sensors, Dynamic gas mixtures, Electronic nose


Fonollosa, Jordi, Fernérndez, Luis, Huerta, Ramón, Gutiérrez-Gálvez, Agustín, Marco, Santiago, (2013). Temperature optimization of metal oxide sensor arrays using Mutual Information Sensors and Actuators B: Chemical Elsevier 187, (0), 331-339

The sensitivity and selectivity of metal oxide (MOX) gas sensors change significantly when the sensors operate at different temperatures. While previous investigations have presented systematic approaches to optimize the operating temperature of a single MOX sensor, in this paper we present a methodology to select the optimal operating temperature of all the MOX sensors constituent of a gas sensor array based on the multivariate response of all the sensing elements. Our approach estimates a widely used Information Theory measure, the so-called Mutual Information (MI), which quantifies the amount of information that the state of one random variable (response of the gas sensor array) can provide from the state of another random variable representing the gas quality. More specifically, our methodology builds sensor models from experimental data to solve the technical problem of populating the joint probability distribution for the MI estimation. We demonstrate the relevance of our approach by maximizing the MI and selecting the best operating temperatures of a four-sensor array sampled at 94 different temperatures to optimize the discrimination task of ethanol, acetic acid, 2-butanone, and acetone. In addition to being applicable in principle to sensor arrays of any size, our approach gives precise information on the ability of the system to discriminate odors according to the temperature of the MOX sensors, for either the optimal set of temperatures or the temperatures that may render inefficient operation of the system itself.

Keywords: MOX gas sensor, Temperature optimization, Limit of detection, Mutual Information, E-nose, Sensor array, Information Theory, Chemical sensing


Fonollosa, Jordi, Gutierrez-Galvez, Agustin, Marco, Santiago, (2012). Quality coding by neural populations in the early olfactory pathway: Analysis using information theory and lessons for artificial olfactory systems PLoS ONE 7, (6), e37809

In this article, we analyze the ability of the early olfactory system to detect and discriminate different odors by means of information theory measurements applied to olfactory bulb activity images. We have studied the role that the diversity and number of receptor neuron types play in encoding chemical information. Our results show that the olfactory receptors of the biological system are low correlated and present good coverage of the input space. The coding capacity of ensembles of olfactory receptors with the same receptive range is maximized when the receptors cover half of the odor input space - a configuration that corresponds to receptors that are not particularly selective. However, the ensemble’s performance slightly increases when mixing uncorrelated receptors of different receptive ranges. Our results confirm that the low correlation between sensors could be more significant than the sensor selectivity for general purpose chemo-sensory systems, whether these are biological or biomimetic.


Fonollosa, J., Halford, B., Fonseca, L., Santander, J., Udina, S., Moreno, M., Hildenbrand, J., Wöllenstein, J., Marco, S., (2009). Ethylene optical spectrometer for apple ripening monitoring in controlled atmosphere store-houses Sensors and Actuators B: Chemical 136, (2), 546-554

In today's store-houses the ripening of fruit is controlled by managing the ethylene concentration in the ambient atmosphere. Precise and continuous ethylene monitoring is very advantageous since low ethylene concentrations are produced by the fruit itself and are indicative of its ripeness, and on other occasions, ethylene is externally added when ripeness or degreening of the product must be promoted. In this work, a multichannel mid-infrared spectrometer for ethylene measurement is built and characterized. The instrument contains additional channels to reject potential cross-interferences like ammonia and ethanol. Additionally, these channels are useful for monitoring a potential malfunction of the cooling system and possible fouling of the fruit, respectively. The complete spectrometer contains a silicon-based macroporous infrared (IR) emitter, a miniaturized long path cell (white cell), a four-channel detector module, low-noise analog amplification and filtering, and a microcontroller-based lock-in amplifier. The new inner architecture of the detector module features a fourfold thermopile array with narrow band optical filters attached by flip-chip technology, and a Fresnel lens array attached on the lid of the package. Laboratory tests show that the system is able to distinguish between ammonia and ethylene, featuring a detection limit of 30 ppm and 160 ppm (95% confidence) for ethylene and ammonia, respectively. Field tests show that the spectrometer is suitable as an ethylene alarm to detect fruit ripening and prevent fruit to decline into senescence. Simulation results show that system selectivity could be improved by setting ammonia channel to another absorption wavelength.

Keywords: IR spectrometer, Ethylene, Fruit storage, Fresnel lens, White cell, Lock-in amplifier


Fonollosa, J., Carmona, M., Santander, J., Fonseca, L., Moreno, M., Marco, S., (2009). Limits to the integration of filters and lenses on thermoelectric IR detectors by flip-chip techniques Sensors and Actuators A: Physical 149, (1), 65-73

In the trend towards miniaturization, a detector module containing multiple IR sensor channels is being built and characterized. In its final form it contains thermopiles, narrow band filters and Fresnel lenses. An important feature of such module is the assembly by flip-chip of the IR filters on top of the thermopiles. The performance of the filter-thermopile ensemble has been assessed by physical simulation and experiments and it has been optimized by the use of an empirically validated model. It has been found that integration of filters (or lenses) too close to the IR detector may lead to degraded performance due to thermal coupling. The impact and extent of this degradation has been thoroughly explored, being the main parameter the distance between the IR sensor and the filter. To avoid such detrimental effects a possibility is to set the device in vacuum conditions, obtaining an improved output response and avoiding the influence of the filters. Another way is to increase the solder joint height. Beyond a certain height, the filter is considered to be isolated from the thermopile.

Keywords: Assembly, Infrared sensor, Infrared filter, Fresnel lenses, FEM simulation, Optimization


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