Signal and information processing for sensing systems


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.

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 – mass spectrometry.

Odor intensity over a wastewater treatment plant in standardized European odor units according EN13725 estimated with a flying drone with olfaction capabilities.

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.

During 2019 our research has been focused on:

Peal alignment in NMR data using clustering methods using the AlpsNMR package.

  1. Development of drones and terrestrial robots with bioinspired machine olfaction capabilities for gas source localization and mapping. Our results have shown that nanodrones with proper signal processing are able to locate sources in indoor scenarios particularly for chemical sources located above the drone.
  2. Development of signal processing approaches to improve the time dynamics of chemical sensors and extract bioinspired chemical features from turbulent plumes. Proper deconvolution methods based on inverse filters are able to improve the sensor bandwidth an order of magnitude, reaching time dynamics able to detect with subsecond events.
  3. Development of data processing methods to resolve molecular heterogeneity in mass spectrometry images of colorectal cancer tissues. The developed methods can discriminate resistant and sensitive areas of tissue to chemotherapy after proper training with homogeneous tissue images.
  4. Development of full workflows including signal processing and machine learning tools for the analysis of untargeted nuclear magnetic resonance data. We have developed and released to the public a package developed in R for the analysis of NMR data: AlpsNMR.
  5. Development of methods for the analysis of flatus and their relationship with food intake.
  6. Development of signal processing methods for the analysis of Ham and Olive Oil flavour data using GC-IMS and their potential use in fraud detection.
  7. Development of methods for the analysis of urine using GC-IMS
  8. Development of techniques to reduce the power consumption of chemical sensor based on metal oxides.



Santiago Marco Colás | Group Leader
Agustín Gutiérrez Gálvez | Senior Researcher
Yinsheng Chen | Postdoctoral Researcher
Lluís Fernández Romero | Postdoctoral Researcher
Jordi Fonollosa Magrinyà | Postdoctoral Researcher
Celia Mallafré Muro | PhD Student
Héctor Gracia Cabrera | Research Assistant
Edmond Géraud Aguilar | Masters Student
Francisco Javier Madrid Gambín | Visiting Researcher




EU-funded projects
SNIFFDRONE · Drone-based Environmental Odor Monitoring (2019-2020) EU Commission · Attract Projects Santiago Marco
National projects
TENSOMICS · Development of tensorial signal processing and machine learning tools tailored to the analysis of urine metabolomics (2019-2021) Ministerio de Ciencia, Innovación y Universidades Santiago Marco
Finished projects
Analisis de tapones de corcho por espectroscopia de movilidad de iones (2015-2016) M3C INDUSTRIAL AUTOMATION & VISION, S.L. Santiago Marco
Sensor test for indoor air quality and safety applications (2015-2016) BSH Electrodomesticos España S.A. Santiago Marco
Preparació i realització d’un curs de processat de senyal per sensors químics de dos dies a BSH Zaragoza (2016-2017) BSH Electrodomesticos España S.A. Santiago Marco
SMART-IMS Procesado de Señal para Espectroscopia de Movilidad de Iones: Análisis de Fluidos Biomédicos y Detección de Sustancias Tóxicas (2012-2015) MINECO, I+D-Investigación fundamental no orientada Santiago Marco
Transducción biomimética para olfacción artificial MINECO, EUROPA EXCELENCIA Agustín Gutiérrez
BIOENCODE Estudio comparativo de la capacidad de codificación de información química de sistemas biológicos y artificiales MINECO, I+D-Investigación fundamental no orientada Agustín Gutiérrez
SENSIBLE Sensores inteligentes para edificios más seguros (2014-2016) MINECO, Acciones de Programación Conjunta Internacional Santiago Marco
SAFESENS Sensor Technologies for Enhanced Safety and Security of Buildings and its Occupants (2014-2017) ENIAC Joint Undertaking Santiago Marco
SIGVOL Mejora de la señal para instrumentación química: aplicaciones en metabolómica de volátiles y en olfacción (2015-2017) MINECO, Retos investigación: Proyectos I+D Santiago Marco
Computational Metabolomics (2017-2019) Industrial Project with Nestlé Institute of Health Sciences, Switzerland Santiago Marco
Development of Data Processing Algorithms for Temperature Modulated Sensors Industrial Project with BSH Electrodomesticos, Spain Santiago Marco


Madrid-Gambin, Francisco, Oller-Moreno, Sergio, Fernandez, Luis, Bartova, Simona, Giner, Maria Pilar, Joyce, Christopher, Ferraro, Francesco, Montoliu, Ivan, Moco, Sofia, Marco, Santiago, (2020). AlpsNMR: an R package for signal processing of fully untargeted NMR-based metabolomics Bioinformatics revisar (no pdf) no en web general, revisar (no pdf) no en web general

NMR-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration, and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines.The AlpsNMR R package and tutorial is freely available to download from under the MIT license.

Burgués, Javier, Marco, Santiago, (2020). Environmental chemical sensing using small drones: A review Science of The Total Environment In Press, 141172

Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aim of this paper is to assemble all of these pieces of information into a comprehensive, structured and updated review of the field of chemical sensing using small drones. We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research.

Keywords: Unmanned aircraft systems, Remotely piloted aircraft systems, Chemical sensors, Gas sensors, Environmental monitoring, Industrial emission monitoring

Burgués, Javier, Marco, Santiago, (2020). Feature extraction for transient chemical sensor signals in response to turbulent plumes: Application to chemical source distance prediction Sensors and Actuators B: Chemical In press, 128235

This paper describes the design of a linear phase low-pass differentiator filter with a finite impulse response (FIR) for extracting transient features of gas sensor signals (the so-called “bouts”). The detection of these bouts is relevant for estimating the distance of a gas source in a turbulent plume. Our current proposal addresses the shortcomings of previous ‘bout’ estimation methods, namely: (i) they were based in non-causal digital filters precluding real time operation, (ii) they used non-linear phase filters leading to waveform distortions and (iii) the smoothing action was achieved by two filters in cascade, precluding an easy tuning of filter performance. The presented method is based on a low-pass FIR differentiator, plus proper post-processing, allowing easy algorithmic implementation for real-time robotic exploration. Linear phase filters preserve signal waveform in the bandpass region for maximum reliability concerning both ‘bout’ detection and amplitude estimation. As a case study, we apply the proposed filter to predict the source distance from recordings obtained with metal oxide (MOX) gas sensors in a wind tunnel. We first perform a joint optimization of the cut-off frequency of the filter and the bout amplitude threshold, for different wind speeds, uncovering interesting relationships between these two parameters. We demonstrate that certain combinations of parameters can reduce the prediction error to 8 cm (in a distance range of 1.45 m) improving previously reported performances in the same dataset by a factor of 2.5. These results are benchmarked against traditional source distance estimators such as the mean, variance and maximum of the response. We also study how the length of the measurement window affects the performance of different signal features, and how to select the filter parameters to make the predictive models more robust to changes in wind speed. Finally, we provide a MATLAB implementation of the bout detection algorithm and all analysis code used in this study.

Keywords: Gas sensors, Differentiator, Low pass filter, Metal oxide semiconductor, MOX sensors, Signal processing, Feature extraction, Gas source localization, Robotics

Burgués, Javier, Hernández, Victor, Lilienthal, Achim J., Marco, Santiago, (2020). Gas distribution mapping and source localization using a 3D grid of metal oxide semiconductor sensors Sensors and Actuators B: Chemical 304, 127309

The difficulty to obtain ground truth (i.e. empirical evidence) about how a gas disperses in an environment is one of the major hurdles in the field of mobile robotic olfaction (MRO), impairing our ability to develop efficient gas source localization strategies and to validate gas distribution maps produced by autonomous mobile robots. Previous ground truth measurements of gas dispersion have been mostly based on expensive tracer optical methods or 2D chemical sensor grids deployed only at ground level. With the ever-increasing trend towards gas-sensitive aerial robots, 3D measurements of gas dispersion become necessary to characterize the environment these platforms can explore. This paper presents ten different experiments performed with a 3D grid of 27 metal oxide semiconductor (MOX) sensors to visualize the temporal evolution of gas distribution produced by an evaporating ethanol source placed at different locations in an office room, including variations in height, release rate and air flow. We also studied which features of the MOX sensor signals are optimal for predicting the source location, considering different lengths of the measurement window. We found strongly time-varying and counter-intuitive gas distribution patterns that disprove some assumptions commonly held in the MRO field, such as that heavy gases disperse along ground level. Correspondingly, ground-level gas distributions were rarely useful for localizing the gas source and elevated measurements were much more informative. We make the dataset and the code publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

Keywords: Mobile robotic olfaction, Metal oxide gas sensors, Signal processing, Sensor networks, Gas source localization, Gas distribution mapping

Mas, S., Torro, A., Fernández, L., Bec, N., Gongora, C., Larroque, C., Martineau, P., de Juan, A., Marco, S., (2020). MALDI imaging mass spectrometry and chemometric tools to discriminate highly similar colorectal cancer tissues Talanta 208, 120455

Intratumour heterogeneity due to cancer cell clonal evolution and microenvironment composition and tumor differences due to genetic variations between patients suffering of the same cancer pathology play a crucial role in patient response to therapies. This study is oriented to show that matrix-assisted laser-desorption ionization-Mass spectrometry imaging (MALDI-MSI), combined with an advanced multivariate data processing pipeline can be used to discriminate subtle variations between highly similar colorectal tumors. To this aim, experimental tumors reproducing the emergence of drug-resistant clones were generated in athymic mice using subcutaneous injection of different mixes of two isogenic cell lines, the irinotecan-resistant HCT116-SN50 (R) and its sibling human colon adenocarcinoma sensitive cell line HCT116 (S). Because irinotecan-resistant and irinotecan-sensitive are derived from the same original parental HCT116 cell line, their genetic characteristics and molecular compositions are closely related. The multivariate data processing pipeline proposed relies on three steps: (a) multiset multivariate curve resolution (MCR) to separate biological contributions from background; (b) multiset K-means segmentation using MCR scores of the biological contributions to separate between tumor and necrotic parts of the tissues; and (c) partial-least squares discriminant analysis (PLS-DA) applied to tumor pixel spectra to discriminate between R and S tumor populations. High levels of correct classification rates (0.85), sensitivity (0.92) and specificity (0.77) for the PLS-DA classification model were obtained. If previously labelled tissue is available, the multistep modeling strategy proposed constitutes a good approach for the identification and characterization of highly similar phenotypic tumor subpopulations that could be potentially applicable to any kind of cancer tissue that exhibits substantial heterogeneity. © 2019 Elsevier B.V.

Keywords: Chemometrics, Colorectal cancer, MALDI imaging, Multivariate analysis, Tumor heterogeneity

Palacio, F., Fonollosa, J., burgués, J., Gomez, J. M., Marco, S., (2020). Pulsed-temperature metal oxide gas sensors for microwatt power consumption IEEE Access 8, 70938-70946

Metal Oxide (MOX) gas sensors rely on chemical reactions that occur efficiently at high temperatures, resulting in too-demanding power requirements for certain applications. Operating the sensor under a Pulsed-Temperature Operation (PTO), by which the sensor heater is switched ON and OFF periodically, is a common practice to reduce the power consumption. However, the sensor performance is degraded as the OFF periods become larger. Other research works studied, generally, PTO schemes applying waveforms to the heater with time periods of seconds and duty cycles above 20%. Here, instead, we explore the behaviour of PTO sensors working under aggressive schemes, reaching power savings of 99% and beyond with respect to continuous heater stimulation. Using sensor sensitivity and the limit of detection, we evaluated four Ultra Low Power (ULP) sensors under different PTO schemes exposed to ammonia, ethylene, and acetaldehyde. Results show that it is possible to operate the sensors with total power consumption in the range of microwatts. Despite the aggressive power reduction, sensor sensitivity suffers only a moderate decline and the limit of detection may degrade up to a factor five. This is, however, gas-dependent and should be explored on a case-by-case basis since, for example, the same degradation has not been observed for ammonia. Finally, the run-in time, i.e., the time required to get a stable response immediately after switching on the sensor, increases when reducing the power consumption, from 10 minutes to values in the range of 10–20 hours for power consumptions smaller than 200 microwatts.

Keywords: Robot sensing systems, Temperature sensors, Heating systems, Gas detectors, Power demand, Sensitivity, Electronic nose, gas sensors, low-power operation, machine olfaction, pulsed-temperature operation, temperature modulation

Wang, S., Hu, Y., Burgués, J., Marco, S., Liu, S.-L., (2020). Prediction of gas concentration using gated recurrent neural networks 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) , IEEE (Genova, Italy) , 178-182

Low-cost gas sensors allow for large-scale spatial monitoring of air quality in the environment. However they require calibration before deployment. Methods such as multivariate regression techniques have been applied towards sensor calibration. In this work, we propose instead, the use of deep learning methods, particularly, recurrent neural networks for predicting the gas concentrations based on the outputs of these sensors. This paper presents a first study of using Gated Recurrent Unit (GRU) neural network models for gas concentration prediction. The GRU networks achieve on average, a 44.69% and a 25.17% RMSE improvement in concentration prediction on a gas dataset when compared with Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models respectively. With the current advances in deep network hardware accelerators, these networks can be combined with the sensors for a compact embedded system suitable for edge applications.

Keywords: Robot sensing systems, Predictive models, Logic gates, Gas detectors, Training, Temperature measurement, Support vector machines

Haick, H., Vishinking, R., di Natale, C., Marco, S., (2020). Sensor Systems Breathborne Biomarkers and the Human Volatilome (ed. Beauchamp, J., Davis, C., Pleil, J.), Elsevier (Amsterdam, The Netherlands) , 201-202

Mas, S., Torro, A., Bec, N., Fernández, L., Erschov, G., Gongora, C., Larroque, C., Martineau, P., de Juan, A., Marco, S., (2019). Use of physiological information based on grayscale images to improve mass spectrometry imaging data analysis from biological tissues Analytica Chimica Acta 1074, 69-79

The characterization of cancer tissues by matrix-assisted laser desorption ionization-mass spectrometry images (MALDI-MSI) is of great interest because of the power of MALDI-MS to understand the composition of biological samples and the imaging side that allows for setting spatial boundaries among tissues of different nature based on their compositional differences. In tissue-based cancer research, information on the spatial location of necrotic/tumoral cell populations can be approximately known from grayscale images of the scanned tissue slices. This study proposes as a major novelty the introduction of this physiologically-based information to help in the performance of unmixing methods, oriented to extract the MS signatures and distribution maps of the different tissues present in biological samples. Specifically, the information gathered from grayscale images will be used as a local rank constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the analysis of MALDI-MSI of cancer tissues. The use of this constraint, setting absence of certain kind of tissues only in clear zones of the image, will help to improve the performance of MCR-ALS and to provide a more reliable definition of the chemical MS fingerprint and location of the tissues of interest. The general strategy to address the analysis of MALDI-MSI of cancer tissues will involve the study of the MCR-ALS results and the posterior use of MCR-ALS scores as dimensionality reduction for image segmentation based on K-means clustering. The resolution method will provide the MS signatures and their distribution maps for each tissue in the sample. Then, the resolved distribution maps for each biological component (MCR scores) will be submitted as initial information to K-means clustering for image segmentation to obtain information on the boundaries of the different tissular regions in the samples studied. MCR-ALS prior to K-means not only provides the desired dimensionality reduction, but additionally resolved non-biological signal contributions are not used and the weight given to the different biological components in the segmentation process can be modulated by suitable preprocessing methods.

Keywords: MCR-ALS, K-means, Local rank constraints, MALDI-MSI, Grayscale images

Burgués, J., Marco, S., (2019). Wind-independent estimation of gas source distance from transient features of metal oxide sensor signals IEEE Access 7, 140460-140469

The intermittency of the instantaneous concentration of a turbulent chemical plume is a fundamental cue for estimating the chemical source distance using chemical sensors. Such estimate is useful in applications such as environmental monitoring or localization of fugitive gas emissions by mobile robots or sensor networks. However, the inherent low-pass filtering of metal oxide (MOX) gas sensors-typically used in odor-guided robots and dense sensor networks due to their low cost, weight and size-hinders the quantification of concentration intermittency. In this paper, we design a digital differentiator to invert the low-pass dynamics of the sensor response, thus obtaining a much faster signal from which the concentration intermittency can be effectively computed. Using a fast photo-ionization detector as a reference instrument, we demonstrate that the filtered signal is a good approximation of the instantaneous concentration in a real turbulent plume. We then extract transient features from the filtered signal-the so-called “bouts”-to predict the chemical source distance, focusing on the optimization of the filter parameters and the noise threshold to make the predictions robust against changing wind conditions. This represents an advantage over previous bout-based models which require wind measurements-typically taken with expensive and bulky anemometers-to produce accurate predictions. The proposed methodology is demonstrated in a wind tunnel scenario where a MOX sensor is placed at various distances downwind of an emitting chemical source and the wind speed varies in the range 10-34 cm/s. The results demonstrate that models optimized with our methodology can provide accurate source distance predictions at different wind speeds.

Keywords: Gas detectors, Chemical sensors, Signal processing, Machine learning, Time series analysis

Palacín, J., Martínez, D., Clotet, E., Pallejà, T., Burgués, J., Fonollosa, J., Pardo, A., Marco, Santiago, (2019). Application of an array of metal-oxide semiconductor gas sensors in an assistant personal robot for early gas leak detection Sensors 19, (9), 1957

This paper proposes the application of a low-cost gas sensor array in an assistant personal robot (APR) in order to extend the capabilities of the mobile robot as an early gas leak detector for safety purposes. The gas sensor array is composed of 16 low-cost metal-oxide (MOX) gas sensors, which are continuously in operation. The mobile robot was modified to keep the gas sensor array always switched on, even in the case of battery recharge. The gas sensor array provides 16 individual gas measurements and one output that is a cumulative summary of all measurements, used as an overall indicator of a gas concentration change. The results of preliminary experiments were used to train a partial least squares discriminant analysis (PLS-DA) classifier with air, ethanol, and acetone as output classes. Then, the mobile robot gas leak detection capabilities were experimentally evaluated in a public facility, by forcing the evaporation of (1) ethanol, (2) acetone, and (3) ethanol and acetone at different locations. The positive results obtained in different operation conditions over the course of one month confirmed the early detection capabilities of the proposed mobile system. For example, the APR was able to detect a gas leak produced inside a closed room from the external corridor due to small leakages under the door induced by the forced ventilation system of the building.

Keywords: Metal-oxide semiconductor, Gas sensor, Gas leak detection, Assistant personal robot, Mobile robot

Martinez, Dominique, Burgués, Javier, Marco, Santiago, (2019). Fast measurements with MOX sensors: A least-squares approach to blind deconvolution Sensors 19, (18), 4029

Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we show that the slow response of MOX sensors can be compensated by deconvolution, provided that an invertible, parametrized, sensor model is available. We consider a nonlinear, first-order dynamic model that is mathematically tractable for MOX identification and deconvolution. By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques. Moreover, we use the MOX diversity in a sensor array to avoid training with a supervised signal. The information provided by two (or more) sensors, exposed to the same flow but responding with different dynamics, is exploited to recover the ground truth signal (gas input). This approach is known as blind deconvolution. We demonstrate its efficiency on MOX sensors recorded in turbulent plumes. The reconstructed signal is similar to the one obtained with a fast photo-ionization detector (PID). The technique is thus relevant to track a fast-changing gas concentration with MOX sensors, resulting in a compensated response time comparable to that of a PID.

Keywords: MOX sensors, Blind deconvolution, Blind identification, Least-squares, Turbulent plumes.

Burgués, Javier, Hernández, Victor, Lilienthal, Achim J., Marco, Santiago, (2019). Smelling nano aerial vehicle for gas source localization and mapping Sensors 19, (3), 478

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments.

Keywords: Robotics, Signal processing, Electronics, Gas source localization, Gas distribution mapping, Gas sensors, Drone, UAV, MOX sensor, Quadcopter

Burgues, J., Marco, S., (2019). Feature extraction of gas sensor signals for gas source localization ISOEN 2019 18th International Symposium on Olfaction and Electronic Nose , IEEE (Fukuoka, Japan) , 1-3

This paper explores which signal features of a gas sensor are optimum for assessing the proximity to a gas source in an open environment. Specifically, we compare three statistical descriptors of the signal (mean, variance and maximum response) against the 'bout' frequency, a feature computed in the derivative of the response. The experimental setup includes a generator of turbulent plumes and a sensing board composed of three metal oxide (MOX) sensors of different types. The main conclusion is that the maximum response is the most robust feature across the three sensors. The 'bout' frequency can be very sensitive to an additional parameter (the noise threshold).

Keywords: Feature extraction, Gas plume, Gas sensors, Gas source localization, MOX, Signal processing

Burgues, J., Valdez, L. F., Marco, S., (2019). High-bandwidth e-nose for rapid tracking of turbulent plumes ISOEN 2019 18th International Symposium on Olfaction and Electronic Nose , IEEE (Fukuoka, Japan) , 1-3

The low bandwidth of metal oxide semiconductor (MOX) sensors (<0.1 Hz) is a major hurdle to gas source localization (GSL) in turbulent environments where detection of intermittent odor patches is key. We present a fast-response miniaturized electronic nose (Fast-eNose) composed of four naked MOX sensors and a digital band-pass filter that can boost the bandwidth of the system close to 1 Hz. The device was attached to a fast photo-ionization detector (330 Hz) to quantify the response time during exposure to turbulent gas plumes. The results indicate that the digital filter can improve the response time by at least a factor of 4, bringing new possibilities to mobile robot olfaction.

Keywords: CFD, Gas plume, Gas sensors, MOX, Response time, Signal processing

Solórzano, A., Rodríguez-Pérez, R., Padilla, M., Graunke, T., Fernandez, L., Marco, S., Fonollosa, J., (2018). Multi-unit calibration rejects inherent device variability of chemical sensor arrays Sensors and Actuators B: Chemical 265, 142-154

Inherent sensor variability limits mass-production applications for metal oxide (MOX) gas sensor arrays because calibration for replicas of a sensor array needs to be performed individually. Recently, calibration transfer strategies have been proposed to alleviate calibration costs of new replicas, but they still require the acquisition of transfer samples. In this work, we present calibration models that can be extended to uncalibrated replicas of sensor arrays without acquiring new samples, i.e., general or global calibration models. The developed methodology consists in including multiple replicas of a sensor array in the calibration process such that sensor variability is rejected by the general model. Our approach was tested using replicas of a MOX sensor array in the classification task of six gases and synthetic air, presented at different background humidity and concentration levels. Results showed that direct transfer of individual calibration models provides poor classification accuracy. However, we also found that general calibration models kept predictive performance when were applied directly to new copies of the sensor array. Moreover, we explored, through feature selection, whether particular combinations of sensors and operating temperatures can provide predictive performances equivalent to the calibration model with the complete array, favoring thereby the existence of more robust calibration models.

Keywords: Gas sensor array, MOX sensor, Robust calibration, Calibration transfer, Machine olfaction

Contreras, M. D. M., Jurado-Campos, N., Sánchez-Carnerero Callado, C., Arroyo-Manzanares, N., Fernández, L., Casano, S., Marco, S., Arce, L., Ferreiro-Vera, C., (2018). Thermal desorption-ion mobility spectrometry: A rapid sensor for the detection of cannabinoids and discrimination of Cannabis sativa L. chemotypes Sensors and Actuators B: Chemical 273, 1413-1424

Existing analytical techniques used for the determination of cannabinoids in Cannabis sativa L. (Cannabis) plants mostly rely on chromatography-based methods. As a rapid alternative for the direct analysis of them, thermal desorption (TD)-ion mobility spectrometry (IMS) was used for obtaining spectral fingerprints of single cannabinoids from Cannabis plant extracts and from plant residues on hands after their manipulation. The ionization source was 63Ni, with automatic switchable polarity. Although in both ionization modes there were signals in the TD-IMS spectra of the plant extracts and residues that could be assigned to concrete cannabinoids and chemotypes, most of them could not be clearly distinguished. Alternatively, the global spectral data of the plant extracts and residues were pre-processed and then, using principal component analysis (PCA)-linear discriminant analysis (LDA), grouped in function of their chemotype in a more feasible way. Using this approach, the possibility of false positive responses was also studied analyzing other non-Cannabis plants and tobacco, which were clustered in a different group to those of Cannabis. Therefore, TD-IMS, as analytical tool, and PCA-LDA, as a strategy for data reduction and pattern recognition, can be applied for on-site chemotaxonomic discrimination of Cannabis varieties and detection of illegal marijuana since the IMS equipment is portable and the analysis time is highly short.

Keywords: Cannabis sativa L., Cannabinoids, Chemometrics, ChemotypeIon mobility spectrometry

Burgués, J., Jiménez-Soto, J. M., Marco, S., (2018). Estimation of the limit of detection in semiconductor gas sensors through linearized calibration models Analytica Chimica Acta 1013, 13-25

The limit of detection (LOD) is a key figure of merit in chemical sensing. However, the estimation of this figure of merit is hindered by the non-linear calibration curve characteristic of semiconductor gas sensor technologies such as, metal oxide (MOX), gasFETs or thermoelectric sensors. Additionally, chemical sensors suffer from cross-sensitivities and temporal stability problems. The application of the International Union of Pure and Applied Chemistry (IUPAC) recommendations for univariate LOD estimation in non-linear semiconductor gas sensors is not straightforward due to the strong statistical requirements of the IUPAC methodology (linearity, homoscedasticity, normality). Here, we propose a methodological approach to LOD estimation through linearized calibration models. As an example, the methodology is applied to the detection of low concentrations of carbon monoxide using MOX gas sensors in a scenario where the main source of error is the presence of uncontrolled levels of humidity.

Keywords: Semiconductor gas sensors, Metal-oxide sensors, Limit of detection, Non-linear, Humidity interference, Temperature modulation

Burgués, Javier, Marco, Santiago, (2018). Multivariate estimation of the limit of detection by orthogonal partial least squares in temperature-modulated MOX sensors Analytica Chimica Acta 1019, 49-64

Metal oxide semiconductor (MOX) sensors are usually temperature-modulated and calibrated with multivariate models such as Partial Least Squares (PLS) to increase the inherent low selectivity of this technology. The multivariate sensor response patterns exhibit heteroscedastic and correlated noise, which suggests that maximum likelihood methods should outperform PLS. One contribution of this paper is the comparison between PLS and maximum likelihood principal components regression (MLPCR) in MOX sensors. PLS is often criticized by the lack of interpretability when the model complexity increases beyond the chemical rank of the problem. This happens in MOX sensors due to cross-sensitivities to interferences, such as temperature or humidity and non-linearity. Additionally, the estimation of fundamental figures of merit, such as the limit of detection (LOD), is still not standardized in multivariate models. Orthogonalization methods, such as Orthogonal Projection to Latent Structures (O-PLS), have been successfully applied in other fields to reduce the complexity of PLS models. In this work, we propose a LOD estimation method based on applying the well-accepted univariate LOD formulas to the scores of the first component of an orthogonal PLS model. The resulting LOD is compared to the multivariate LOD range derived from error-propagation. The methodology is applied to data extracted from temperature-modulated MOX sensors (FIS SB-500-12 and Figaro TGS 3870-A04), aiming at the detection of low concentrations of carbon monoxide in the presence of uncontrolled humidity (chemical noise). We found that PLS models were simpler and more accurate than MLPCR models. Average LOD values of 0.79 ppm (FIS) and 1.06 ppm (Figaro) were found using the approach described in this paper. These values were contained within the LOD ranges obtained with the error-propagation approach. The mean LOD increased to 1.13 ppm (FIS) and 1.59 ppm (Figaro) when considering validation samples collected two weeks after calibration, which represents a 43% and 46% degradation, respectively. The orthogonal score-plot was a very convenient tool to visualize MOX sensor data and to validate the LOD estimates.

Keywords: Metal oxide sensors, Partial least squares, Orthogonal projection to latent structures, Maximum likelihood principal component regression, Limit of detection, Temperature modulation

Fernandez, L., Yan, J., Fonollosa, J., Burgués, J., Gutierrez, A., Marco, S., (2018). A practical method to estimate the resolving power of a chemical sensor array: Application to feature selection Frontiers in Chemistry 6, Article 209

A methodology to calculate analytical figures of merit is not well established for detection systems that are based on sensor arrays with low sensor selectivity. In this work, we present a practical approach to estimate the Resolving Power of a sensory system, considering non-linear sensors and heteroscedastic sensor noise. We use the definition introduced by Shannon in the field of communication theory to quantify the number of symbols in a noisy environment, and its version adapted by Gardner and Barlett for chemical sensor systems. Our method combines dimensionality reduction and the use of algorithms to compute the convex hull of the empirical data to estimate the data volume in the sensor response space. We validate our methodology with synthetic data and with actual data captured with temperature-modulated MOX gas sensors. Unlike other methodologies, our method does not require the intrinsic dimensionality of the sensor response to be smaller than the dimensionality of the input space. Moreover, our method circumvents the problem to obtain the sensitivity matrix, which usually is not known. Hence, our method is able to successfully compute the Resolving Power of actual chemical sensor arrays. We provide a relevant figure of merit, and a methodology to calculate it, that was missing in the literature to benchmark broad-response gas sensor arrays.

Keywords: Gas sensor array, MOX sensors, Resolving Power, Sensor resolution, Dimensionality reduction, Machine olfaction

Rodríguez-Pérez, R., Fernández, L., Marco, S., (2018). Overoptimism in cross-validation when using partial least squares-discriminant analysis for omics data: a systematic study Analytical and Bioanalytical Chemistry 410, (23), 5981-5992

Advances in analytical instrumentation have provided the possibility of examining thousands of genes, peptides, or metabolites in parallel. However, the cost and time-consuming data acquisition process causes a generalized lack of samples. From a data analysis perspective, omics data are characterized by high dimensionality and small sample counts. In many scenarios, the analytical aim is to differentiate between two different conditions or classes combining an analytical method plus a tailored qualitative predictive model using available examples collected in a dataset. For this purpose, partial least squares-discriminant analysis (PLS-DA) is frequently employed in omics research. Recently, there has been growing concern about the uncritical use of this method, since it is prone to overfitting and may aggravate problems of false discoveries. In many applications involving a small number of subjects or samples, predictive model performance estimation is only based on cross-validation (CV) results with a strong preference for reporting results using leave one out (LOO). The combination of PLS-DA for high dimensionality data and small sample conditions, together with a weak validation methodology is a recipe for unreliable estimations of model performance. In this work, we present a systematic study about the impact of the dataset size, the dimensionality, and the CV technique used on PLS-DA overoptimism when performance estimation is done in cross-validation. Firstly, by using synthetic data generated from a same probability distribution and with assigned random binary labels, we have obtained a dataset where the true classification rate (CR) is 50%. As expected, our results confirm that internal validation provides overoptimistic estimations of the classification accuracy (i.e., overfitting). We have characterized the CR estimator in terms of bias and variance depending on the internal CV technique used and sample to dimensionality ratio. In small sample conditions, due to the large bias and variance of the estimator, the occurrence of extremely good CRs is common. We have found that overfitting peaks when the sample size in the training subset approaches the feature vector dimensionality minus one. In these conditions, the models are neither under- or overdetermined with a unique solution. This effect is particularly intense for LOO and peaks higher in small sample conditions. Overoptimism is decreased beyond this point where the abundance of noisy produces a regularization effect leading to less complex models. In terms of overfitting, our study ranks CV methods as follows: Bootstrap produces the most accurate estimator of the CR, followed by bootstrapped Latin partitions, random subsampling, K-Fold, and finally, the very popular LOO provides the worst results. Simulation results are further confirmed in real datasets from mass spectrometry and microarrays.

Keywords: Metabolomics, Mass spectrometry, Microarrays, Chemometrics, Data analysis, Classification, Method validation

Fonollosa, Jordi, Solórzano, Ana, Marco, Santiago, (2018). Chemical sensor systems and associated algorithms for fire detection: A review Sensors 18, (2), 553

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative

Keywords: Fire detection, Gas sensor, Pattern recognition, Sensor fusion, Machine learning, Toxicants, Carbon monoxide, Hydrogen cyanide, Standard test fires, Transducers, Smoke

Taghadomi-Saberi, S., Garcia, S. M., Masoumi, A. A., Sadeghi, M., Marco, S., (2018). Classification of bitter orange essential oils according to fruit ripening stage by untargeted chemical profiling and machine learning Sensors 18, (6), 1922

The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods.

Keywords: Bitter orange essential oil, Headspace gas chromatography–mass spectrometry, Artificial neural network, Foodomics, Chemometrics, Feature selection

Burgués, J., Marco, S., (2018). Low power operation of temperature-modulated metal oxide semiconductor gas sensors Sensors 18, (2), 339

Mobile applications based on gas sensing present new opportunities for low-cost air quality monitoring, safety, and healthcare. Metal oxide semiconductor (MOX) gas sensors represent the most prominent technology for integration into portable devices, such as smartphones and wearables. Traditionally, MOX sensors have been continuously powered to increase the stability of the sensing layer. However, continuous power is not feasible in many battery-operated applications due to power consumption limitations or the intended intermittent device operation. This work benchmarks two low-power, duty-cycling, and on-demand modes against the continuous power one. The duty-cycling mode periodically turns the sensors on and off and represents a trade-off between power consumption and stability. On-demand operation achieves the lowest power consumption by powering the sensors only while taking a measurement. Twelve thermally modulated SB-500-12 (FIS Inc. Jacksonville, FL, USA) sensors were exposed to low concentrations of carbon monoxide (0–9 ppm) with environmental conditions, such as ambient humidity (15–75% relative humidity) and temperature (21–27 ◦C), varying within the indicated ranges. Partial Least Squares (PLS) models were built using calibration data, and the prediction error in external validation samples was evaluated during the two weeks following calibration. We found that on-demand operation produced a deformation of the sensor conductance patterns, which led to an increase in the prediction error by almost a factor of 5 as compared to continuous operation (2.2 versus 0.45 ppm). Applying a 10% duty-cycling operation of 10-min periods reduced this prediction error to a factor of 2 (0.9 versus 0.45 ppm). The proposed duty-cycling powering scheme saved up to 90% energy as compared to the continuous operating mode. This low-power mode may be advantageous for applications that do not require continuous and periodic measurements, and which can tolerate slightly higher prediction errors.

Keywords: Smartphone, Metal-oxide semiconductor, Gas sensor, Low power, Temperature-modulation, Interferences

Rodríguez, R., Cortés, R., Verónica Guamán, A., Pardo, A., Torralba, Y., Gómez, F., Roca, J., Barberà, J.A., Cascante, M., Marco, S., (2018). Instrumental drift removal in GC-MS data for breath analysis: the short-term and long-term temporal validation of putative biomarkers for COPD Journal of Breath Research 12, (3), 036007

Abstract Breath analysis holds the promise of a non-invasive technique for the diagnosis of diverse respiratory conditions including COPD and lung cancer. Breath contains small metabolites that may be putative biomarkers of these conditions. However, the discovery of reliable biomarkers is a considerable challenge in the presence of both clinical and instrumental confounding factors. Among the latter, instrumental time drifts are highly relevant, as since question the short and long-term validity of predictive models. In this work we present a methodology to counter instrumental drifts using information from interleaved blanks for a case study of GC-MS data from breath samples. The proposed method includes feature filtering, and additive, multiplicative and multivariate drift corrections, the latter being based on Component Correction. Biomarker discovery was based on Genetic Algorithms in a filter configuration using Fisher´s ratio computed in the Partial Least Squares – Discriminant Analysis subspace as a figure of merit. Using our protocol, we have been able to find nine peaks that provide a statistically significant Area under the ROC Curve (AUC) of 0.75 for COPD discrimination. The method developed has been successfully validated using blind samples in short-term temporal validation. However, in the attempt to use this model for patient screening six months later was not successful. This negative result highlights the importance of increasing validation rigour when reporting biomarker discovery results.

Keywords: Instrumental shifts, Chemometrics, Biomarker validation

Oller-Moreno, Sergio, Cominetti, Ornella, Galindo, Antonio Núñez, Irincheeva, Irina, Corthésy, John, Astrup, Arne, Saris, Wim H. M., Hager, Jörg, Kussmann, Martin, Dayon, Loïc, (2018). The differential plasma proteome of obese and overweight individuals undergoing a nutritional weight loss and maintenance intervention PROTEOMICS - Clinical Applications 12, (1), 1600150

Purpose : The nutritional intervention program “DiOGenes” focuses on how obesity can be prevented and treated from a dietary perspective. We generated differential plasma proteome profiles in the DiOGenes cohort to identify proteins associated with weight loss and maintenance and explore their relation to body mass index, fat mass, insulin resistance and sensitivity. Experimental Design : Relative protein quantification was obtained at baseline and after combined weight loss/maintenance phases using isobaric tagging and MS/MS. A Welch t-test determined proteins differentially present after intervention. Protein relationships with clinical variables were explored using univariate linear models, considering collection center, gender and age as confounding factors. Results : 473 subjects were measured at baseline and end of the intervention; 39 proteins were longitudinally differential. Proteins with largest changes were sex hormone-binding globulin, adiponectin, C-reactive protein, calprotectin, serum amyloid A, and proteoglycan 4 (PRG4), whose association with obesity and weight loss is known. We identified new putative biomarkers for weight loss/maintenance. Correlation between PRG4 and proline-rich acidic protein 1 (PRAP1) variation and Matsuda insulin sensitivity increment was showed. Conclusions and Clinical Relevance : MS-based proteomic analysis of a large cohort of non-diabetic overweight and obese individuals concomitantly identified known and novel proteins associated with weight loss and maintenance.

Keywords: Biomarker, Diabetes, Large-scale study, Mass spectrometry, Obesity, Proteomics

Burgués, Javier, Hernandez, Victor, Lilienthal, Achim J., Marco, Santiago, (2018). 3D Gas distribution with and without artificial airflow: An experimental study with a grid of metal oxide semiconductor gas sensors Proceedings EUROSENSORS 2018 , MDPI (Graz, Austria) 2, (13), 911

Gas distribution modelling can provide potentially life-saving information when assessing the hazards of gaseous emissions and for localization of explosives, toxic or flammable chemicals. In this work, we deployed a three-dimensional (3D) grid of metal oxide semiconductor (MOX) gas sensors deployed in an office room, which allows for novel insights about the complex patterns of indoor gas dispersal. 12 independent experiments were carried out to better understand dispersion patters of a single gas source placed at different locations of the room, including variations in height, release rate and air flow profiles. This dataset is denser and richer than what is currently available, i.e., 2D datasets in wind tunnels. We make it publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

Keywords: MOX, Metal oxide, Flow visualization, Gas sensors, Gas distribution mapping, Sensor grid, 3D, Gas source localization, Indoor

Rodríguez-Pérez, Raquel, Padilla, Marta, Marco, S., (2018). The need of external validation for metabolomics predictive models Volatile Organic Compound Analysis in Biomedical Diagnosis Applications (ed. Cumeras, R., Correig, X.), Apple Academic Press (New York, USA) PART III: COMPUTATIONAL TOOLS, 225-252

Over the last decade, metabolomics research has produced thousands of research works. Many of them were describing the ability of machine learning methods to detect diverse health conditions based on massspectrometry, nuclear magnetic resonance or artificial olfaction analysis of body fluids.

Pomareda, V., Magrans, R., Jiménez-Soto, J., Martínez, D., Tresánchez, M., Burgués, J., Palacín, J., Marco, S., (2017). Chemical source localization fusing concentration information in the presence of chemical background noise Sensors 17, (4), 904

We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presented.

Keywords: Machine olfaction, Odor robots, Chemical sensors, Bayesian inference

Burgues, J., Fonollosa, J., Marco, S., (2017). Discontinuously operated MOX sensors for low power applications IEEE Conference Publications ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) , IEEE (Montreal, Canada) , 1-3

Metal oxide semiconductor sensors are limited by their low selectivity, high power consumption and temporal drift. This paper proposes a novel discontinuous temperature modulation operation mode characterized by on-demand measurements and periodic warm-up cycles. The performance of two sets of FIS SB-500-12 sensors, one group continuously operated and the other group discontinuously operated, was compared in a scenario of carbon monoxide detection at low concentrations for five consecutive days. Results showed that the discontinuous operating mode moderately increased the prediction error and the limit of detection but was advantageous in terms of energy savings (up to 60% with respect to the continuous temperature modulation mode).

Keywords: Discontinuous operation, Duty-cycling, Low power, MOX sensors, Temperature modulation

Solorzano, A., Fonollosa, J., Fernandez, L., Eichmann, J., Marco, S., (2017). Fire detection using a gas sensor array with sensor fusion algorithms IEEE Conference Publications ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) , IEEE (Montreal, Canada) , 1-3

Conventional fire alarms are based on smoke detection. Nevertheless, in some fire scenarios volatiles are released before smoke. Fire detectors based only on chemical sensors have already been proposed as they may provide faster response, but they are still prone to false alarms in the presence of nuisances. These systems rely heavily on pattern recognition techniques to discriminate fires from nuisances. In this context, it is important to test the systems according to international standards for fires and testing the system against a diversity of nuisances. In this work, we investigate the behavior of a gas sensor array coupled to sensor fusion algorithms for fire detection when exposed to standardized fires and several nuisances. Results confirmed the ability to detect fires (97% Sensitivity), although the system still produces a significant rate of false alarms (35%) for nuisances not presented in the training set.

Keywords: Fire alarm, Gas sensor array, Machine Olfaction, Multisensor system, Sensor fusion

Fernandez, L., Martin-Gomez, A., Mar Contreras, M., Padilla, M., Marco, S., Arce, L., (2017). Ham quality evaluation assisted by gas chromatography ion mobility spectrometry IEEE Conference Publications ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) , IEEE (Montreal, Canada) , 1-3

In recent years, Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) has been successfully employed in food science as a control technique for the prevention of fraud according to food and labeling regulations. In this work, we propose the use of GC-IMS technique to assess the quality of Iberian ham with regard to the Iberian Pig's diet (either nourished with feed or with acorns). For this purpose, we have acquired a dataset composed of 53 samples of Iberian ham from different food providers using a commercial GC-IMS (FlavourSpec, from G.A.S. Dortmund, Germany). Intensive signal pre-processing for GC-IMS was applied to the raw data. This dataset was employed to create four Partial Least Squares Discriminant Analysis (PLSDA) models corresponding to different train/test partitions of the dataset. Nearly perfect classification rates (above 91 %) were obtained for each partition of the dataset, denoting the high power of GC-IMS to characterize food samples.

Keywords: Classification, Food Science, GC-IMS, Ham quality, PLSDA

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

Fernandez, L., Guney, S., Gutierrez-Galvez, A., Marco, S., (2016). Calibration transfer in temperature modulated gas sensor arrays Sensors and Actuators B: Chemical 231, 276-284

Abstract Shifts in working temperature are an important issue that prevents the successful transfer of calibration models from one chemical instrument to another. This effect is of special relevance when working with gas sensor arrays modulated in temperature. In this paper, we study the use of multivariate techniques to transfer the calibration model from a temperature modulated gas sensor array to another when a global change of temperature occurs. To do so, we built 12 identical master sensor arrays composed of three different types of commercial Figaro sensors and acquired a dataset of sensor responses to three pure substances (ethanol, acetone and butanone) dosed at 7 concentrations. The master arrays are then shifted in temperature (from −50 to 50 °C, ΔT = 10 °C) and considered as slave arrays. Data correction is performed for an increasing number of transfer samples with 4 different calibration transfer techniques: Direct Standardization, Piece-wise Direct Standardization, Orthogonal Signal Correction and Generalized Least Squares Weighting. In order to evaluate the performance of the calibration transfer, we compare the Root Mean Square Error of Prediction (RMSEP) of master and slave arrays, for each instrument correction. Best results are obtained from Piece-wise Direct standardization, which exhibits the lower RMSEP values after correction for the smaller number of transfer samples.

Keywords: Calibration transfer, Gas sensor array, MOX, Temperature modulation

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

Martínez, D., Moreno, J., Tresanchez, M., Clotet, E., Jiménez-Soto, J.M., Magrans, R., Pardo, A., Marco, S., Palacín, J., (2016). Measuring gas concentration and wind intensity in a turbulent wind tunnel with a mobile robot Journal of Sensors , 2016, Article ID 7184980

This paper presents the measurement of gas concentration and wind intensity performed with a mobile robot in a custom turbulent wind tunnel designed for experimentation with customizable wind and gas leak sources. This paper presents the representation in different information layers of the measurements obtained in the turbulent wind tunnel under different controlled environmental conditions in order to describe the plume of the gas and wind intensities inside the experimentation chamber. The information layers have been generated from the measurements gathered by individual onboard gas and wind sensors carried out by an autonomous mobile robot. On the one hand, the assumption was that the size and cost of these specialized sensors do not allow the creation of a net of sensors or other measurement alternatives based on the simultaneous use of several sensors, and on the other hand, the assumption is that the information layers created will have application on the development and test of automatic gas source location procedures based on reactive or nonreactive algorithms.

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

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

Fernandez, L., Marco, S., Gutierrez-Galvez, A., (2015). Robustness to sensor damage of a highly redundant gas sensor array Sensors and Actuators B: Chemical 218, 296-302

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.

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

Fonollosa, Jordi, Neftci, Emre, Rabinovich, Mikhail, (2015). Learning of chunking sequences in cognition and behavior 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.

Maynou, Joan, Pairo, Erola, Marco, Santiago, Perera, Alexandre, (2015). Sequence information gain based motif analysis BMC Bioinformatics , 16, (1), 377

BACKGROUND:The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions.RESULTS:This methodology (SIGMA) has been tested on genomic sequence data for Homo sapiens and Mus musculus. SIGMA has been compared with different publicly available alternatives for motif detection, such as MEME/MAST, Biostrings (Bioconductor package), MotifRegressor, and previous work such Qresiduals projections or information theoretic based detectors. Comparative results, in the form of Receiver Operating Characteristic curves, show how, in 70 % of the studied Transcription Factor Binding Sites, the SIGMA detector has a better performance and behaves more robustly than the methods compared, while having a similar computational time. The performance of SIGMA can be explained by its parametric simplicity in the modelling of the non-linear co-variability in the binding motif positions.CONCLUSIONS:Sequence Information Gain based Motif Analysis is a generalisation of a non-linear model of the cis-regulatory sequences detection based on Information Theory. This generalisation allows us to detect transcription factor binding sites with maximum performance disregarding the covariability observed in the positions of the training set of sequences. SIGMA is freely available to the public at

Castillo, Y., Gutiérrez, A., (2015). Determining the tertiary structure of an olfactory receptor CASEIB Proceedings XXXIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2015) , Sociedad Española de Ingeniería Biomédica (Madrid, Spain) , 245-248

Olfactory receptors (ORs) are transmembrane proteins that interact with odorant molecules, triggering the first step in the mechanism of olfaction. They also occupy a large part of mammalian genome and their dysfunction is involved in some degenerative diseases. Proteins structure is related to their function, but experimentally determining the three-dimensional structure of ORs is tricky. However, recent advances in bioinformatics have made it easier. The objectives of this project were to computationally model several ORs and to study their interactions with odorants. We carried out a comparative modelling process, using Chimera and Modeller programs as alignment, visualization and 3D reconstruction tools. After validating the models by several procedures, we performed docking experiments with DOCK. We predicted binding pockets location and studied which odorants bind which ORs, comparing our results with electrophysiological measures of olfactory neurons activity. At the end, we successfully built six mouse ORs models and tested them against 24 common odorants, obtaining a good correlation with previous studies and thus validating our protocol. Moreover, we reconstructed eight human ORs which had never been modelled before, though their predicted interactions with 63 odorants were not clearly correlated to previous experimental studies. In this way, we have shown the efficiency of these computational algorithms, which can contribute to the research about biological processes such as the mechanism of olfaction. With small improvements, they could be in an early future a suitable alternative to experimental approaches, leading to accurate protein models in a practical, faster and easier way.

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

Oller-Moreno, S., Singla-Buxarrais, G., Jiménez-Soto, J. M., Pardo, Antonio, Garrido-Delgado, R., Arce, L., Marco, Santiago, (2015). Sliding window multi-curve resolution: Application to gas chromatography - Ion Mobility Spectrometry Sensors and Actuators B: Chemical 15th International Meeting on Chemical Sensors , Elsevier (Buenos Aires, Argentina) 217, 13-21

Abstract Blind Source Separation (BSS) techniques aim to extract a set of source signals from a measured mixture in an unsupervised manner. In the chemical instrumentation domain source signals typically refer to time-varying analyte concentrations, while the measured mixture is the set of observed spectra. Several techniques exist to perform BSS on Ion Mobility Spectrometry, being Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and Multivariate Curve Resolution (MCR) the most commonly used. The addition of a multi-capillary gas chromatography column using the ion mobility spectrometer as detector has been proposed in the past to increase chemical resolution. Short chromatography times lead to high levels of co-elution, and ion mobility spectra are key to resolve them. For the first time, BSS techniques are used to deconvolve samples of the gas chromatography - ion mobility spectrometry tandem. We propose a method to extract spectra and concentration profiles based on the application of MCR in a sliding window. Our results provide clear concentration profiles and pure spectra, resolving peaks that were not detected by the conventional use of MCR. The proposed technique could also be applied to other hyphenated instruments with similar strong co-elutions.

Keywords: Blind Source Separation, Multivariate Curve Resolution, Ion Mobility Spectrometry, Gas Chromatography, Hyphenated instrumentation, SIMPLISMA, co-elution

Palleja, T., Balsa, R., Tresanchez, M., Moreno, J., Teixido, M., Font, D., Marco, S., Pomareda, V., Palacin, J., (2014). Corridor gas-leak localization using a mobile Robot with a photo ionization detector sensor Sensor Letters , 12, (6-7), 974-977

The use of an autonomous mobile robot to locate gas-leaks and air quality monitoring in indoor environments are promising tasks that will avoid risky human operations. However, these are challenging tasks due to the chaotic gas profile propagation originated by uncontrolled air flows. This paper proposes the localization of an acetone gas-leak in a 44 m-length indoor corridor with a mobile robot equipped with a PID sensor. This paper assesses the influence of the mobile robot velocity and the relative height of the PID sensor in the profile of the measurements. The results show weak influence of the robot velocity and strong influence of the relative height of the PID sensor. An estimate of the gas-leak location is also performed by computing the center of mass of the highest gas concentrations.

Keywords: Gas source detection, LIDAR sensor, Mobile robot, PID sensor, SLAM, Acetone, Air quality, Gases, Indoor air pollution, Mobile robots, Robots, Air quality monitoring, Autonomous Mobile Robot, Gas sources, Indoor environment, Leak localization, LIDAR sensors, Profile propagation, SLAM, Ionization of gases

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

Fresco-Cala, B., Jimenez-Soto, J. M., Cardenas, S., Valcarcel, M., (2014). Single-walled carbon nanohorns immobilized on a microporous hollow polypropylene fiber as a sorbent for the extraction of volatile organic compounds from water samples Microchimica Acta , 181, (9-10), 1117-1124

We have evaluated the behavior of single-walled carbon nanohorns as a sorbent for headspace and direct immersion (micro)solid phase extraction using volatile organic compounds (VOCs) as model analytes. The conical carbon nanohorns were first oxidized in order to increase their solubility in water and organic solvents. A microporous hollow polypropylene fiber served as a mechanical support that provides a high surface area for nanoparticle retention. The extraction unit was directly placed in the liquid sample or the headspace of an aqueous standard or a water sample to extract and preconcentrate the VOCs. The variables affecting extraction have been optimized. The VOCs were then identified and quantified by GC/MS. We conclude that direct immersion of the fiber is the most adequate method for the extraction of VOCs from both liquid samples and headspace. Detection limits range from 3.5 to 4.3 ng L-1 (excepted for toluene with 25 ng L-1), and the precision (expressed as relative standard deviation) is between 3.9 and 9.6 %. The method was applied to the determination of toluene, ethylbenzene, various xylene isomers and styrene in bottled, river and tap waters, and the respective average recoveries of spiked samples are 95.6, 98.2 and 86.0 %.

Keywords: (Micro)solid phase extraction, Nanotechnology, Oxidized single-walled carbon nanohorns, Volatiles compounds, Waters

Marco, Santiago, (2014). The need for external validation in machine olfaction: emphasis on health-related applications Analytical and Bioanalytical Chemistry Springer Berlin Heidelberg 406, (16), 3941-3956

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.

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

Polese, Davide, Martinelli, Eugenio, Marco, Santiago, Di Natale, Corrado, Gutierrez-Galvez, Agustin, (2014). Understanding odor information segregation in the olfactory bulb by means of mitral and tufted cells PLoS ONE 9, (10), e109716

Odor identification is one of the main tasks of the olfactory system. It is performed almost independently from the concentration of the odor providing a robust recognition. This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself. Significant experimental evidence has indicated that the olfactory system is able to infer simultaneously odor identity and intensity. However, it is still unclear at what level or levels of the olfactory pathway this segregation of information occurs. In this work, we study whether this odor information segregation is performed at the input stage of the olfactory bulb: the glomerular layer. To this end, we built a detailed neural model of the glomerular layer based on its known anatomical connections and conducted two simulated odor experiments. In the first experiment, the model was exposed to an odor stimulus dataset composed of six different odorants, each one dosed at six different concentrations. In the second experiment, we conducted an odor morphing experiment where a sequence of binary mixtures going from one odor to another through intermediate mixtures was presented to the model. The results of the experiments were visualized using principal components analysis and analyzed with hierarchical clustering to unveil the structure of the high-dimensional output space. Additionally, Fisher's discriminant ratio and Pearson's correlation coefficient were used to quantify odor identity and odor concentration information respectively. Our results showed that the architecture of the glomerular layer was able to mediate the segregation of odor information obtaining output spiking sequences of the principal neurons, namely the mitral and external tufted cells, strongly correlated with odor identity and concentration, respectively. An important conclusion is also that the morphological difference between the principal neurons is not key to achieve odor information segregation.

Martinez, Dani, Teixidó, Mercè, Font, Davinia, Moreno, Javier, Tresanchez, Marcel, Marco, Santiago, Palacín, Jordi, (2014). Ambient intelligence application based on environmental measurements performed with an assistant mobile robot Sensors 14, (4), 6045-6055

This paper proposes the use of an autonomous assistant mobile robot in order to monitor the environmental conditions of a large indoor area and develop an ambient intelligence application. The mobile robot uses single high performance embedded sensors in order to collect and geo-reference environmental information such as ambient temperature, air velocity and orientation and gas concentration. The data collected with the assistant mobile robot is analyzed in order to detect unusual measurements or discrepancies and develop focused corrective ambient actions. This paper shows an example of the measurements performed in a research facility which have enabled the detection and location of an uncomfortable temperature profile inside an office of the research facility. The ambient intelligent application has been developed by performing some localized ambient measurements that have been analyzed in order to propose some ambient actuations to correct the uncomfortable temperature profile.

Keywords: Ambient intelligence, Human thermal comfort, Robotic exploration

Bennetts, Victor, Schaffernicht, Erik, Pomareda, Victor, Lilienthal, Achim, Marco, Santiago, Trincavelli, Marco, (2014). Combining non selective gas sensors on a mobile robot for identification and mapping of multiple chemical compounds Sensors 14, (9), 17331-17352

In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

Keywords: Environmental monitoring, Gas discrimination, Gas distribution mapping, Service robots, Open sampling systems, PID, Metal oxide sensors

Marco, S., Gutiérrez-Gálvez, A., Lansner, A., Martinez, D., Rospars, J. P., Beccherelli, R., Perera, A., Pearce, T. C., Verschure, P. F. M. J., Persaud, K., (2014). A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation Microsystem Technologies , 20, (4-5), 729-742

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, in a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy and efficient combinatorial coding, with unmatched chemical information processing mechanisms. The last decade has seen important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. The EU-funded Project NEUROCHEM (Bio-ICT-FET- 216916) developed novel computing paradigms and biologically motivated artefacts for chemical sensing, taking its inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built that features a very large-scale sensor array (65,536 elements) using conducting polymer technology which mimics the olfactory receptor neuron layer. It implements derived computational neuroscience algorithms in an embedded system that interfaces the chemical sensors and processes their signals in real-time. This embedded system integrates abstracted computational models of the main anatomic building blocks in the olfactory pathway: the olfactory bulb, and olfactory cortex in vertebrates (respectively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor, an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions implemented in software. Finally, the algorithmic models are tested in mixed chemical plumes with an odour robot having navigation capabilities.

Oller-Moreno, S., Pardo, A., Jimenez-Soto, J. M., Samitier, J., Marco, S., (2014). Adaptive Asymmetric Least Squares baseline estimation for analytical instruments SSD 2014 Proceedings 11th International Multi-Conference on Systems, Signals & Devices (SSD) , IEEE (Castelldefels-Barcelona, Spain) , 1569846703

Automated signal processing in analytical instrumentation is today required for the analysis of highly complex biomedical samples. Baseline estimation techniques are often used to correct long term instrument contamination or degradation. They are essential for accurate peak area integration. Some methods approach the baseline estimation iteratively, trying to ignore peaks which do not belong to the baseline. The proposed method in this work consists of a modification of the Asymmetric Least Squares (ALS) baseline removal technique developed by Eilers and Boelens. The ALS technique suffers from bias in the presence of intense peaks (in relation to the noise level). This is typical of diverse instrumental techniques such as Gas Chromatography-Mass Spectrometry (GC-MS) or Gas Chromatography-Ion Mobility Spectrometry (GC-IMS). In this work, we propose a modification (named psalsa) to the asymmetry weights of the original ALS method in order to better reject large peaks above the baseline. Our method will be compared to several versions of the ALS algorithm using synthetic and real GC signals. Results show that our proposal improves previous versions being more robust to parameter variations and providing more accurate peak areas.

Keywords: Gas chromatography, Instruments, Radioactivity measurement, Signal processing, Analytical instrument, Analytical Instrumentation, Asymmetric least squares, Baseline estimation, Baseline removal, Gas chromatography-mass spectrometries (GC-MS), Instrumental techniques, Noise levels, Iterative methods

Fernandez, L., Marco, S., (2014). Calibration transfer between e-noses Signal Processing and Communications Applications Conference (SIU) Signal Processing and Communications Applications Conference (SIU), 2014 22nd , IEEE (Trabzon, Turkey) , 650-653

Electronic nose is an instrument which is composed of gas sensor array and pattern recognition unit. It is generally used for classifying, identifying or quantifying the odors or volatile organic components for these commonly used devices, calibration transfer is an important issue because of differences in each instrument, sensor drift, changes in environmental conditions or background changes. Calibration transfer is a transfer of model between different instruments which have different conditions. In this study, calibration transfer is applied to the e-noses which have different temperature conditions. Also the results of the direct standardization, piecewise direct standardization and orthogonal signal correction which are different calibration methods were compared. The results of the piecewise direct standardization method are more successful than the other methods for the dataset which is used in this study.

Keywords: Calibration, Conferences, Electronic noses, Ethanol, Instruments, Signal processing, Standardization

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

Martínez, D., Moreno, J., Tresanchez, M., Teixidó, M., Font, D., Pardo, A., Marco, S., Palacín, J., (2014). Experimental application of an autonomous mobile robot for gas leak detection in indoor environments 17th International Conference on Information Fusion (FUSION) , IEEE (Salamanca, Spain) , 1-6

This paper presents the experimental application of an autonomous mobile robot for gas leak detection in indoor environments. The application is focused to automatize a human-risky operation in indoor areas. The goal of the autonomous mobile robot is the localization of a toxic gas leak source. So, the mobile robot has to explore the whole area and perform an auto-localization procedure based on a SLAM method and a LIDAR sensor. The mobile robot measures gas concentration by using a photoionization detector. The experimentation was realized in a large indoor environment in a university facility with a simulated gas leak source. The combination of the results from the auto-localization procedure with the information of the sensors allows the estimation of the gas leak source location.

Martínez, D., Moreno, J., Tresanchez, M., Teixidó, M., Palací, J., Marco, S., (2014). Preliminary results on measuring gas and wind intensity with a mobile robot in an indoor area ETFA 2014 19th IEEE International Conference on Emerging Technologies and Factory Automation , IEEE (Barcelona, Spain) , 1-5

This paper presents the preliminary results obtained when using a mobile robot to measure gas and wind intensity in an indoor area by means of several attached sensors such as a LIDAR, an e-nose, and an anemometer. The robot navigation was performed by means of a random path planning and the robot self location was performed by means of an SLAM procedure. This paper presents the first preliminary results obtained in a set of measurement experiments. In all cases, the indoor area has a fixed artificial simulated airflow and an induced gas leak source placed in different locations of the experimentation area. Results have shown different gas diffusion profiles in the different experiments performed.

Fernandez, L., Gutierrez-Galvez, A., Marco, S., (2014). Robustness to sensor damage of a highly redundant gas sensor array Procedia Engineering 28th European Conference on Solid-State Transducers (EUROSENSORS 2014) , Eurosensors (Brescia, Italy) 87, 851-854

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.

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

Martínez, Dani, Pallejà, T., Moreno, Javier, Tresanchez, Marcel, Teixidó, M., Font, Davinia, Pardo, Antonio, Marco, Santiago, Palacín, Jordi, (2014). A mobile robot agent for gas leak source detection Advances in Intelligent Systems and Computing Trends in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection (ed. Bajo Perez, Javier, Corchado Rodríguez, Juan M., Mathieu, Philippe, Campbell, Andrew, Ortega, Alfonso, Adam, Emmanuel, Navarro, Elena M., Ahrndt, Sebastian, Moreno, Maríaa N., Julián, Vicente), Springer International Publishing 293, 19-25

This paper presents an autonomous agent for gas leak source detection. The main objective of the robot is to estimate the localization of the gas leak source in an indoor environment without any human intervention. The agent implements an SLAM procedure to scan and map the indoor area. The mobile robot samples gas concentrations with a gas and a wind sensor in order to estimate the source of the gas leak. The mobile robot agent will use the information obtained from the onboard sensors in order to define an efficient scanning path. This paper describes the measurement results obtained in a long corridor with a gas leak source placed close to a wall.

Keywords: Gas detection, Mobile robot agent, Laser sensor, Self-localization

Karpas, Z., Guamán, A. V., Pardo, A., Marco, S., (2013). Comparison of the performance of three ion mobility spectrometers for measurement of biogenic amines Analytica Chimica Acta 758, (3), 122-129

The performance of three different types of ion mobility spectrometer (IMS) devices: GDA2 with a radioactive ion source (Airsense, Germany), UV-IMS with a photo-ionization source (G.A.S. Germany) and VG-Test with a corona discharge source (3QBD, Israel) was studied. The gas-phase ion chemistry in the IMS devices affected the species formed and their measured reduced mobility values. The sensitivity and limit of detection for trimethylamine (TMA), putrescine and cadaverine were compared by continuous monitoring of a stream of air with a given concentration of the analyte and by measurement of headspace vapors of TMA in a sealed vial. Preprocessing of the mobility spectra and the effectiveness of multivariate curve resolution techniques (MCR-LASSO) improved the accuracy of the measurements by correcting baseline effects and adjusting for variations in drift time as well as enhancing the signal to noise ratio and deconvolution of the complex data matrix to their pure components. The limit of detection for measurement of the biogenic amines by the three IMS devices was between 0.1 and 1.2 ppm (for TMA with the VG-Test and GDA, respectively) and between 0.2 and 0.7 ppm for putrescine and cadaverine with all three devices. Considering the uncertainty in the LOD determination there is almost no statistically significant difference between the three devices although they differ in their operating temperature, ionization method, drift tube design and dopant chemistry. This finding may have general implications on the achievable performance of classic IMS devices.

Keywords: Biogenic amines, Comparison of performance, Ion mobility spectrometry, Sensitivity, Signal processing, Vapor concentration

Pomareda, Victor, Lopez-Vidal, Silvia, Calvo, Daniel, Pardo, Antonio, Marco, Santiago, (2013). A novel differential mobility analyzer as VOCs detector and multivariate techniques for identification and quantification Analyst , 138, (12), 3512-3521

A Differential Mobility Analyser (DMA) is a specific configuration of an Ion Mobility Spectrometer (IMS) where ions with different electrical mobilities are separated in space, instead of in time of drift, as in classical drift-time IMS. This work presents an instrument developed by the company Ioner, a parallel plate DMA instrument, but with crucial differences in the sheath flow and detection system when compared to other instruments in the market. These differences improve the resolving powers and sensitivities of the instrument. Additionally, datasets from IMS or DMA instruments are typically processed with univariate techniques when only qualitative detection is of interest. However, good performance in quantitative measurements can be achieved using multivariate data processing. This work presents for the first time, measurements with a stand alone DMA instrument and the multivariate data processing related for VOCs and environmental interesting samples.

Ziyatdinov, A., Diaz, E. Fernández, Chaudry, A., Marco, S., Persaud, K., Perera, A., (2013). A software tool for large-scale synthetic experiments based on polymeric sensor arrays Sensors and Actuators B: Chemical 177, 596-604

This manuscript introduces a software tool that allows for the design of synthetic experiments in machine olfaction. The proposed software package includes both, a virtual sensor array that reproduces the diversity and response of a polymer array and tools for data generation. The synthetic array of sensors allows for the generation of chemosensor data with a variety of characteristics: unlimited number of sensors, support of multicomponent gas mixtures and full parametric control of the noise in the system. The artificial sensor array is inspired from a reference database of seventeen polymeric sensors with concentration profiles for three analytes. The main features in the sensor data, like sensitivity, diversity, drift and sensor noise, are captured by a set of models under simplified assumptions. The generator of sensor signals can be used in applications related to test and benchmarking of signal processing methods, neuromorphic simulations in machine olfaction and educational tools. The software is implemented in R language and can be freely accessed.

Keywords: Gas Sensor Array, Conducting Polymers, Electronic Nose, Sensor Simulation, Synthetic Dataset, Benchmark, Educational Tool

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

Marco, S., Gutiérrez-Gálvez, A., Lansner, A., Martinez, D., Rospars, J. P., Beccherelli, R., Perera, A., Pearce, T., Vershure, P., Persaud, K., (2013). Biologically inspired large scale chemical sensor arrays and embedded data processing Proceedings of SPIE - The International Society for Optical Engineering Smart Sensors, Actuators, and MEMS VI , SPIE Digital Library (Grenoble, France) 8763, 1-15

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. EU Funded Project NEUROCHEM (Bio-ICT-FET- 216916) has developed novel computing paradigms and biologically motivated artefacts for chemical sensing taking inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built featuring a large scale sensor array (65K elements) in conducting polymer technology mimicking the olfactory receptor neuron layer, and abstracted biomimetic algorithms have been implemented in an embedded system that interfaces the chemical sensors. The embedded system integrates computational models of the main anatomic building blocks in the olfactory pathway: The olfactory bulb, and olfactory cortex in vertebrates (alternatively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions. Finally, the algorithmic models are tested with an odour robot with navigation capabilities in mixed chemical plumes.

Keywords: Antennal lobes, Artificial olfaction, Computational neuroscience, Olfactory bulbs, Plume tracking, Abstracting, Actuators, Algorithms, Biomimetic processes, Chemical sensors, Conducting polymers, Data processing, Flavors, Odors, Robots, Smart sensors, Embedded systems

Santano-Martínez, R., Leiva-González, R., Avazbeigi, M., Gutiérrez-Gálvez, A., Marco, S., (2013). Identification of molecular properties coding areas in rat's olfactory bulb by rank products Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing BIOSIGNALS 2013 , SciTePress (Barcelona, Spain) , 383-387

Neural coding of chemical information is still under strong debate. It is clear that, in vertebrates, neural representation in the olfactory bulb is a key for understanding a putative odour code. To explore this code, in this work we have studied a public dataset of radio images of 2-Deoxyglucose uptake (2-DG) in the olfactory bulb of rats in response to diverse odorants using univariate pixel selection algorithms: rank-products and Mann-Whitney U (MWU) test. Initial results indicate that some chemical properties of odorants preferentially activate certain areas of the rat olfactory bulb. While non-parametric test (MWU) has difficulties to detect these regions, rank-product provides a higher power of detection.

Keywords: 2-Deoxyglucose uptake, Chemotopy, Feature selection, Odour coding, Olfaction, Olfactory bulb

Fernandez, L., Gutierrez-Galvez, A., Marco, S., (2013). Multi-way analysis of diversity and redundancy factors in large MOX gas sensor data Metal Oxide-based Sensors 14th International Meeting on Chemical Sensors - IMCS 2012 , AMA Science Portal (Nuremberg, Germany) P2.07, 1279-1280

We propose the use of multi-way methods to analyze the contribution of diversity and redundancy to odor identification and concentration estimation in a large chemical sensor array. We use a chemical sensing system based on a large array of metal oxide sensors (MOX) and inspired on the diversity and redundancy of the olfactory epithelium. In order to analyze the role of diversity (different sensor type and temperature modulation) and redundancy (replicates of sensors and different load resistors) in odor quantification and discrimination tasks, we have acquired two datasets and modeled the data using multi-way techniques.

Keywords: Artificial Olfaction, Large array, MOX gas sensor, Multi-way methods

Hernandez Bennetts, V. M., Lilienthal, A. J., Khaliq, A. A., Pomareda Sese, V., Trincavelli, M., (2013). Towards real-world gas distribution mapping and leak localization using a mobile robot with 3d and remote gas sensing capabilities 2013 IEEE International Conference on Robotics and Automation (ICRA) (ed. Parker, Lynne E.), IEEE (Karlsruhe, Germany) , 2335-2340

Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor. This sensor provides integral concentration measurements over the path of the laser beam. Existing gas distribution mapping algorithms can only handle local measurements obtained from traditional in-situ chemical sensors. In this paper we also describe an algorithm to generate 3D methane concentration maps from integral concentration and depth measurements. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced.

Keywords: Laser beams, Measurement by laser beam, Mobile robots, Robot kinematics, Robot sensing systems

Gutiérrez-Gálvez, A., Marco, S., (2013). Study of the coding efficiency of populations of olfactory receptor neurons and olfactory glomeruli Frontiers in Neuroengineering Series Neuromorphic Olfaction (ed. Persaud, K. , Marco, S., Gutiérrez-Gálvez, A.), CRC Press (London, UK) , 59-82

Persaud, K. , Marco, S., Gutiérrez-Gálvez, A., (2013). Neuromorphic Olfaction Frontiers in Neuroengineering Series Neuromorphic Olfaction , CRC Press (London, UK)


  • Gas chromatograph/mass spectrometer (Thermoscientific) with robotic head-space sampler
  • Gas Chromatograph/ Thermal Conductivity Detector (Thermoscientific) with robotic head-space sampler
  • 2 Infusion pumps K-systems
  • Gas Chromatography-Ion Mobility Spectrometry FlavourspecTM (Gas Dortmund)
  • 6 channel vapor generator plus humidity control (Owlstone, UK)
  • Ion Mobility Spectrometer: Gas Detector Array (Airsense Analytics GmbH)
  • Computing and General Purpose Electronic Instrumentation
  • Field Asymmetric Ion Mobility Spectrometer (Owlstone, UK)
  • Corona Discharge Ion Mobility Spectrometer (3QBD, Israel)
  • Ultraviolet Ion Mobility Spectrometer (Gas Dortmund, Germany)
  • Fast Photo Ionization Detector (Aurora Scientific, Canada)


  • Dr. Lourdes Arce
    Dept. Química Analítica, Universidad de Córdoba, Spain
  • Prof. J. W. Gardner
    Microsensors and Bioelectronics Lab, Dept. of Electric and Electronic Engineering, University of Warwick, UK
  • Prof. Achim Lilienthal
    Mobile Robotics and Olfaction Lab, University of Örebro, Sweden
  • Dr. Ivan Montoliu and Dra. Sofia Moço
    Nestlé Institute of Health Sciences, Laussane, Switzerland
  • Dr. Jordi Palacín
    Robotics Lab, Universitat de Lleida, Spain
  • Dra. Cristina Castro
    Sensors Technology, BSH-Zaragoza, Spain
  • Dr. Jens Eichman
    MINIMAX, Bad Oldesloe, Germany
  • Dr. Ulf Struckmeier
    AMS sensors, Reutlingen, Germany
  • Dr. Fernando Azpiroz
    Dept. Digestive Diseases, Vall d’Hebron, Barcelona, Spain
  • Dra. Anna de Juan
    Dept. Química Analítica i Enginyeria Química, Universitat de Barcelona, Spain
  • Dra. Sofia Moço 
    Nestlé ResearchLaussane, Switzerland 
  • Dra. Silvia Mas
    IRSTEA; Montpellier, France. 
  • Dr. Dominique Martinez
    LORIA-INRIA, Nancy, France 
  • Dr. Oriol Sibila & Dr. Àlvar Agustí
    Inflamación y reparación en enfermedades respiratorias, Hospital Clínic de Barcelona 


El nanodron de Santi Marco en prensa internacional

Investigadores del grupo de Procesamiento de señales e información para sistemas de sensores del IBEC, dirigido por Santiago Marco, diseñan un nanodrón que podría identificar gases tóxicos en edificios derrumbados por el efecto de terremotos o de explosiones. El nuevo aparato, que pesa 35 gramos, también podría ser útil para detectar la presencia de víctimas en espacios cerrados y de difícil acceso.

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Diseñado un nanodrón capaz de detectar gases tóxicos en situaciones de emergencia

Investigadores del grupo de Procesamiento de señales e información para sistemas de sensores del IBEC, dirigido por Santiago Marco, diseñan un nanodrón que podría identificar gases tóxicos en edificios derrumbados por el efecto de terremotos o de explosiones. El nuevo aparato, que pesa 35 gramos, también podría ser útil para detectar la presencia de víctimas en espacios cerrados y de difícil acceso.

Detectar gases peligrosos en edificios derrumbados por terremotos o explosiones e, incluso, identificar la presencia de posibles víctimas en lugares difícilmente accesibles son algunos escenarios de acción del smelling nanoaerial vehicle (SNAV), un nanodrón que han diseñado y desarrollado los investigadores Santiago Marco y Javier Burgués, de la Facultad de Física de la Universidad de Barcelona y del Instituto de Bioingeniería de Cataluña (IBEC).

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Santiago Marco nombrado nuevo vicepresidente de la International Society for Olfaction and Chemical Sensing

Santiago Marco, investigador principal del grupo Procesamiento de señales e información para sistemas de sensores en el IBEC, ha sido nombrado recientemente como nuevo vicepresidente para los próximos dos años de la “International Society for Olfaction and Chemical Sensing” (ISOCS) durante la asamblea general celebrada en el “International Symposium on Olfaction and Electronic Nose Conference” (ISOEN) en ACROS, Fukuoka, Japón.

La ISOCS se fundó en mayo de 2008 por la “Network of Excellence General Olfaction and Sensing Projects on a European Level” (GOSPEL), la red Europea de Excelencia en olfacción artificial, además, Santiago Marco es uno de sus miembros fundadores.

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Dos proyectos del programa ATTRACT para investigadores del IBEC

Santiago Marco, investigador principal del grupo de Procesamiento de señales e información para sistemas de sensores y Samuel Ojosnegros, responsable de la unidad Bioingeniería en Salud Reproductiva, han sido seleccionados por sus proyectos de investigación en la convocatoria del programa ATTRACT. La convocatoria recibió más de 1200 propuestas de las cuales se seleccionaron 170.

El programa ATTRACT es un proyecto pionero de investigación e innovación que forma parte del programa Horizon 2020 financiado por la Unión Europea y respaldado por un consorcio formado por 9 socios. El objetivo principal de este programa es crear un ecosistema de innovación conjunta entre la investigación fundamental y las comunidades industriales para desarrollar tecnologías avanzadas de detección e imagen para usos científicos y comerciales.

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