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In an effort to deepen our understanding of mammalian olfactory coding, we have used an objective method to analyze a large set of odorant-evoked activity maps collected systematically across the rat olfactory bulb to determine whether such an approach could identify specific glomerular regions that are activated by related odorants. To that end, we combined fuzzy c-means clustering methods with a novel validity approach based on cluster stability to evaluate the significance of the fuzzy partitions on a data set of glomerular layer responses to a large diverse group of odorants. Our results confirm the existence of glomerular response clusters to similar odorants. They further indicate a partial hierarchical chemotopic organization wherein larger glomerular regions can be subdivided into smaller areas that are rather specific in their responses to particular functional groups of odorants. These clusters bear many similarities to, as well as some differences from, response domains previously proposed for the glomerular layer of the bulb. These data also provide additional support for the concept of an identity code in the mammalian olfactory system.

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 degrees C, Delta T = 10 degrees 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. (C) 2016 Elsevier B.V. All rights reserved.

We report on improved ultrasonic atomizer method of depositing the sensing films on quartz crystal microbalance (QCM) sensors. The main objectives of the present work were minimizing the sensor-to-sensor response variation, improving the sensing film stability, and reducing amount of materials used during coating (reducing time of coating). In order to achieve the goals, we have redesigned and refabricated large parts of the atomizer system reported previously. The main principle of the operation—deposition of the fine mist generated by the ultrasonic device remained unchanged. The largest modifications encompass introduction of the new, sealed deposition chamber, considerable reduction of the flow resistance, and control over the flow rates of the mist and air streams in the system. The paper reports the new configuration of the ultrasonic atomizer method, optimization of the atomizer system performance (deposition parameters) and the results of improved quality of sensors fabricated using the new system.

This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.

This article presents a sensor excitation and signal processing approach that combines temperature modulation and transient analysis to enhance the selectivity and sensitivity of metal-oxide gas sensors. A staircase waveform is applied to the sensor heater to extract transient information from multiple operating temperatures. Four different transient analysis techniques, Pade–Z-transform, multi-exponential transient spectroscopy (METS), window time slicing (WTS) and a novel ridge regression solution, are evaluated on the basis of their ability to improve the sensitivity and selectivity of the sensor array. The techniques are validated on two experimental databases containing serial dilutions and mixtures of organic solvents. Our results indicate that processing of the thermal transients significantly improves the sensitivity of metal-oxide chemoresistors when compared to the quasi-stationary temperature-modulated responses. # 2003 Elsevier Science B.V. All rights reserved.

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. (C) 2014 Elsevier B.V. All rights reserved.

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.The data presented here have been deposited to the web site of The University of California at Irvine (UCI) Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Gas+sensor+array+under+flow+modulation). The code repository for reproducible analysis applied to the data is hosted at the GutHub web site (https://github.com/variani/pulmon).The data and code can be used upon citation of [1].

We present a methodology based on Information theory tools to optimize the operating temperatures of metal-oxide (MOX) gas sensor arrays and maximize the ability of the system in odor discrimination tasks. We have demonstrated the feasibility of the method by optimizing the temperatures of a foursensor array for an effective discrimination of four odorants. We measured the resistance of four MOX sensors at different operating temperatures when exposed to different concentration levels of ethanol, acetone, 2-butanone and acetic acid. Based on the acquired data we built complete models to shape the responses of the sensors according to the gas exposure conditions and the operating temperature. We applied the Mutual Information (MI) theory to quantify the ability of the sensor array to determine the identity of the stimuli regardless of its concentration, and thereby, select the optimum operation temperature for each sensor.

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.

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