Jordi Fonollosa

Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors Validated by Gas Chromatography-Mass Spectrometry

Chemical detection systems based on chemo-resistive sensors usually include a gas chamber to control the sample air flow and to minimize turbulence. However, such a kind of experimental setup does not reproduce the gas concentration fluctuations observed in natural environments and destroys the spatio-temporal information contained in gas plumes. Aiming at reproducing more realistic environments, we utilize a wind tunnel with two independent gas sources that get naturally mixed along a turbulent flow. For the first time, chemo-resistive gas sensors are exposed to dynamic gas mixtures generated with several concentration levels at the sources. Moreover, the ground truth of gas concentrations at the sensor location was estimated by means of gas chromatography-mass spectrometry. We used a support vector machine as a tool to show that chemo-resistive transduction can be utilized to reliably identify chemical components in dynamic turbulent mixtures, as long as sufficient gas concentration coverage is used. We show that in open sampling systems, training the classifiers only on high concentrations of gases produces less effective classification and that it is important to calibrate the classification method with data at low gas concentrations to achieve optimal performance.

Quality coding by neural populations in the early olfactory pathway: analysis using information theory and lessons for artificial olfactory systems

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

Estimation of the limit of detection using information theory measures.

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.

Learning of Chunking Sequences in Cognition and Behavior

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.

Two-dimensional wavelet transform feature extraction for porous silicon chemical sensors

Designing reliable, fast responding, highly sensitive, and low-power consuming chemo-sensory systems has long been a major goal in chemo-sensing. This goal, however, presents a difficult challenge because having a set of chemo-sensory detectors exhibiting all these aforementioned ideal conditions are still largely un-realizable to-date. This paper presents a unique perspective on capturing more in-depth insights into the physicochemical interactions of two distinct, selectively chemically modified porous silicon (pSi) film-based optical gas sensors by implementing an innovative, based on signal processing methodology, namely the two-dimensional discrete wavelet transform. Specifically, the method consists of using the two-dimensional discrete wavelet transform as a feature extraction method to capture the non-stationary behavior from the bi-dimensional pSi rugate sensor response. Utilizing a comprehensive set of measurements collected from each of the aforementioned optically based chemical sensors, we evaluate the significance of our approach on a complex, six-dimensional chemical analyte discrimination/quantification task problem. Due to the bi-dimensional aspects naturally governing the optical sensor response to chemical analytes, our findings provide evidence that the proposed feature extractor strategy may be a valuable tool to deepen our understanding of the performance of optically based chemical sensors as well as an important step toward attaining their implementation in more realistic chemo-sensing applications.

A highly sensitive IR-optical sensor for ethylene-monitoring

Precise and continuous ethylene detection is needed in various fruit storage applications. The aim of this work is the development of a miniaturised mid-infrared filter spectrometer for ethylene detection at 10.6 μm wavelength. For this reason optical components and signal processing electronics need to be developed, tested and integrated in a compact measurement system. The present article describes the proposed system set-up, the status of the development of component prototypes and results of gas measurements performed using a first system set-up. Next to a microstructured IR-emitter, a miniaturised multi-reflection cell and a thermopile-array with integrated optical filters and microstructured Fresnel lenses for the measurement of ethylene, two interfering gases and one reference channel are proposed. Recently a miniaturised White cell as absorption path is tested with various commercial and a self-developed thermal emitter. First ethylene measurements have been performed with commercial twofold thermopile detectors and a Lock-in-amplifier. These showed significant absorption at an ethylene concentration of 100ppm. For the detection module different types of thermopiles were tested, first prototypes of Fresnel lenses have been fabricated and characterised and the parameters of the optical filters were specified. Furthermore a compact system electronics for signal processing containing a preamplification stage and Lock-in-technique is in development.

Optical Label-Free Nanoplasmonic Biosensing Using a Vertical-Cavity Surface-Emitting Laser and Charge-Coupled Device

We present a compact platform for biochemosensing based on the combination of a vertical-cavity surface-emitting laser (VCSEL) light source, microelectromechanical systems (MEMS)-based microoptics, a specially designed nanoplasmonic sensing chip, and charge-coupled device (CCD) detector. The platform does not require any spectral analyzer for signal evaluation, showing good promise for facile integration, neither does it use any microscope setup for the signal collection or imaging. The analytical capabilities of the developed biochemosensing platform are demonstrated by evaluation of the protein-substrate (biotinylated bovine serum albumin-gold) and the protein-protein (biotin-NeutrAvidin) binding kinetics, which is further compared to detection based on conventional optical extinction spectroscopy. The instrument is able to detect low femtomoles of adsorbed proteins with the limit of detection comparable to the state-of-the-art research and commercial optical label-free biochemosensors.

Fresnel lenses: study and fabrication in silicon technology for medium-IR applications

Diffractive Fresnel Lenses (FL) were designed, fabricated and tested. The lens aims for increasing the sensitivity of a Non-Dispersive InfraRed (NDIR) silicon based optical gas system, focusing as much radiation as possible onto the detector. The studied wavelengths are 10.6μm and 3.4μm, which are the main absorption lines for ethylene and ethanol respectively. The lens diameter (5mm) and the focal length (4mm) are fixed by the detector package. Those diffractive lenses are compatible with the planar nature of silicon microtechnology. A theoretical study about the global lens efficiency as a function of the technological constrains and the process complexity has been carried out. Using only three photolithographic masks, eight quantization steps can be etched and a theoretical lens efficiency of 95% can be achieved. Once the devices were fabricated, the focal length and the spot size have been measured.

A compact optical ethylene monitoring system

In various fruit storage applications precise and continuous ethylene detection is needed. The aim of this work is the development of a miniaturised mid-infrared filter spectrometer for ethylene detection at 10.6 &mgr;m wavelength. For this reason optical components and signal processing electronics were developed, tested and integrated in a compact measurement system. The present article describes the optical components, the integration of the optical system, electronics and results of gas measurements. Next to a Silicon-based macroporous IR-emitter, a miniaturised absorption cell and a detector module for the simultaneous measurement at four channels for ethylene, two interfering gases and the reference signal were integrated in the optical system. Optical filters were attached to fourfold thermopile-arrays by flip-chip- technology. Silicon-based Fresnel multilenses were processed and attached to the cap of the detector housing. Because of the high reflection losses at the silicon-air surface the Fresnel lenses were coated with Antireflection layers made of Zinc sulphide. For the signal processing electronics a preamplification stage and a Lock-in-board has been developed. First ethylene measurements with the optical system with miniaturised gas cell, Silicon-based IR-emitter, a commercial thermopile detector and the self-developed system electronics showed a detection limit of smaller than 20ppm.

Chemical gas sensor array dataset.

To address drift in chemical sensing, an extensive dataset was collected over a period of three years. An array of 16 metal-oxide gas sensors was exposed to six different volatile organic compounds at different concentration levels under tightly-controlled operating conditions. Moreover, the generated dataset is suitable to tackle a variety of challenges in chemical sensing such as sensor drift, sensor failure or system calibration. The data is related to “Chemical gas sensor drift compensation using classifier ensembles”, by Vergara et al. [1], and “On the calibration of sensor arrays for pattern recognition using the minimal number of experiments”, by Rodriguez-Lujan et al. [2] The dataset can be accessed publicly at the UCI repository upon citation of: http://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations