Signal and data processing for machine olfaction and chemical sensing: A review

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

Odor-Based Molecular Communications: State-of-the-Art, Vision, Challenges, and Frontier Directions

Humankind mimics the processes and strategies that nature has perfected and uses them as a model to address its problems. That has recently found a new direction, i.e., a novel communication technology called molecular communication (MC), using molecules to encode, transmit, and receive information. Despite extensive research, an innate MC method with plenty of natural instances, i.e., olfactory or odor communication, has not yet been studied with the tools of information and communication technologies (ICT). Existing studies focus on digitizing this sense and developing actuators without inspecting the principles of odor-based information coding and MC, which significantly limits its application potential. Hence, there is a need to focus cross-disciplinary research efforts to reveal the fundamentals of this unconventional communication modality from an ICT perspective. The ways of natural odor MC in nature need to be anatomized and engineered for end-to-end communication among humans and human-made things to enable several multi-sense augmented reality technologies reinforced with olfactory senses for novel applications and solutions in the Internet of Everything (IoE). This paper introduces the concept of odor-based molecular communication (OMC) and provides a comprehensive examination of olfactory systems. It explores odor communication in nature, including aspects of odor information, channels, reception, spatial perception, and cognitive functions. Additionally, a comprehensive comparison of various communication systems sets the foundation for further investigation. By highlighting the unique characteristics, advantages, and potential applications of OMC through this comparative analysis, the paper lays the groundwork for exploring the modeling of an end-to-end OMC channel, considering the design of OMC transmitters and receivers, and developing innovative OMC techniques

Sensor-based machine olfaction with neuromorphic models of the olfactory system

Electronic noses combine an array of cross-selective gas sensors with a pattern recognition engine to identify odors. Pattern recognition of multivariate gas sensor response is usually performed using existing statistical and chemometric techniques. An alternative solution involves developing novel algorithms inspired by information processing in the biological olfactory system. The objective of this dissertation is to develop a neuromorphic architecture for pattern recognition for a chemosensor array inspired by key signal processing mechanisms in the olfactory system. Our approach can be summarized as follows. First, a high-dimensional odor signal is generated from a chemical sensor array. Three approaches have been proposed to generate this combinatorial and high dimensional odor signal: temperature-modulation of a metal-oxide chemoresistor, a large population of optical microbead sensors, and infrared spectroscopy. The resulting high-dimensional odor signals are subject to dimensionality reduction using a self-organizing model of chemotopic convergence. This convergence transforms the initial combinatorial high-dimensional code into an organized spatial pattern (i.e., an odor image), which decouples odor identity from intensity. Two lateral inhibitory circuits subsequently process the highly overlapping odor images obtained after convergence. The first shunting lateral inhibition circuits perform gain control enabling identification of the odorant across a wide range of concentration. This shunting lateral inhibition is followed by an additive lateral inhibition circuit with center-surround connections. These circuits improve contrast between odor images leading to more sparse and orthogonal patterns than the one available at the input. The sharpened odor image is stored in a neurodynamic model of a cortex. Finally, anti-Hebbian/ Hebbian inhibitory feedback from the cortical circuits to the contrast enhancement circuits performs mixture segmentation and weaker odor/background suppression, respectively. We validate the models using experimental datasets and show our results are consistent with recent neurobiological findings

Odour Detection Methods: Olfactometry and Chemical Sensors

The complexity of the odours issue arises from the sensory nature of smell. From the evolutionary point of view olfaction is one of the oldest senses, allowing for seeking food, recognizing danger or communication: human olfaction is a protective sense as it allows the detection of potential illnesses or infections by taking into account the odour pleasantness/unpleasantness. Odours are mixtures of light and small molecules that, coming in contact with various human sensory systems, also at very low concentrations in the inhaled air, are able to stimulate an anatomical response: the experienced perception is the odour. Odour assessment is a key point in some industrial production processes (i.e., food, beverages, etc.) and it is acquiring steady importance in unusual technological fields (i.e., indoor air quality); this issue mainly concerns the environmental impact of various industrial activities (i.e., tanneries, refineries, slaughterhouses, distilleries, civil and industrial wastewater treatment plants, landfills and composting plants) as sources of olfactory nuisances, the top air pollution complaint. Although the human olfactory system is still regarded as the most important and effective “analytical instrument” for odour evaluation, the demand for more objective analytical methods, along with the discovery of materials with chemo-electronic properties, has boosted the development of sensor-based machine olfaction potentially imitating the biological system. This review examines the state of the art of both human and instrumental sensing currently used for the detection of odours. The olfactometric techniques employing a panel of trained experts are discussed and the strong and weak points of odour assessment through human detection are highlighted. The main features and the working principles of modern electronic noses (E-Noses) are then described, focusing on their better performances for environmental analysis. Odour emission monitoring carried out through both the techniques is finally reviewed in order to show the complementary responses of human and instrumental sensing

Active Wavelength Selection for Chemical Identification Using Tunable Spectroscopy

Spectrometers are the cornerstone of analytical chemistry. Recent advances in microoptics manufacturing provide lightweight and portable alternatives to traditional spectrometers. In this dissertation, we developed a spectrometer based on Fabry-Perot interferometers (FPIs). A FPI is a tunable (it can only scan one wavelength at a time) optical filter. However, compared to its traditional counterparts such as FTIR (Fourier transform infrared spectroscopy), FPIs provide lower resolution and lower signal-noiseratio (SNR). Wavelength selection can help alleviate these drawbacks. Eliminating uninformative wavelengths not only speeds up the sensing process but also helps improve accuracy by avoiding nonlinearity and noise. Traditional wavelength selection algorithms follow a training-validation process, and thus they are only optimal for the target analyte. However, for chemical identification, the identities are unknown. To address the above issue, this dissertation proposes active sensing algorithms that select wavelengths online while sensing. These algorithms are able to generate analytedependent wavelengths. We envision this algorithm deployed on a portable chemical gas platform that has low-cost sensors and limited computation resources. We develop three algorithms focusing on three different aspects of the chemical identification problems. First, we consider the problem of single chemical identification. We formulate the problem as a typical classification problem where each chemical is considered as a distinct class. We use Bayesian risk as the utility function for wavelength selection, which calculates the misclassification cost between classes (chemicals), and we select the wavelength with the maximum reduction in the risk. We evaluate this approach on both synthesized and experimental data. The results suggest that active sensing outperforms the passive method, especially in a noisy environment. Second, we consider the problem of chemical mixture identification. Since the number of potential chemical mixtures grows exponentially as the number of components increases, it is intractable to formulate all potential mixtures as classes. To circumvent combinatorial explosion, we developed a multi-modal non-negative least squares (MMNNLS) method that searches multiple near-optimal solutions as an approximation of all the solutions. We project the solutions onto spectral space, calculate the variance of the projected spectra at each wavelength, and select the next wavelength using the variance as the guidance. We validate this approach on synthesized and experimental data. The results suggest that active approaches are superior to their passive counterparts especially when the condition number of the mixture grows larger (the analytes consist of more components, or the constituent spectra are very similar to each other). Third, we consider improving the computational speed for chemical mixture identification. MM-NNLS scales poorly as the chemical mixture becomes more complex. Therefore, we develop a wavelength selection method based on Gaussian process regression (GPR). GPR aims to reconstruct the spectrum rather than solving the mixture problem, thus, its computational cost is a function of the number of wavelengths. We evaluate the approach on both synthesized and experimental data. The results again demonstrate more accurate and robust performance in contrast to passive algorithms

Coding and learning of chemosensor array patterns in a neurodynamic model of the olfactory system

Arrays of broadly-selective chemical sensors, also known as electronic noses, have been developed during the past two decades as a low-cost and high-throughput alternative to analytical instruments for the measurement of odorant chemicals. Signal processing in these gas-sensor arrays has been traditionally performed by means of statistical and neural pattern recognition techniques. The objective of this dissertation is to develop new computational models to process gas sensor array signals inspired by coding and learning mechanisms of the biological olfactory system. We have used a neurodynamic model of the olfactory system, the KIII, to develop and demonstrate four odor processing computational functions: robust recovery of overlapping patterns, contrast enhancement, background suppression, and novelty detection. First, a coding mechanism based on the synchrony of neural oscillations is used to extract information from the associative memory of the KIII model. This temporal code allows the KIII to recall overlapping patterns in a robust manner. Second, a new learning rule that combines Hebbian and anti-Hebbian terms is proposed. This learning rule is shown to achieve contrast enhancement on gas-sensor array patterns. Third, a new local learning mechanism based on habituation is proposed to perform odor background suppression. Combining the Hebbian/anti-Hebbian rule and the local habituation mechanism, the KIII is able to suppress the response to continuously presented odors, facilitating the detection of the new ones. Finally, a new learning mechanism based on anti-Hebbian learning is proposed to perform novelty detection. This learning mechanism allows the KIII to detect the introduction of new odors even in the presence of strong backgrounds. The four computational models are characterized with synthetic data and validated on gas sensor array patterns obtained from an e-nose prototype developed for this purpose

Cellular reactions to long-term volatile organic compound (VOC) exposures

Investigations of cellular processes initiated by volatile organic compounds (VOCs) are limited when modelling realistic long-term exposure scenarios at low concentrations. Exposure to indoor VOCs is associated with a range of adverse effects, but data on molecular changes at regulatory threshold limits are lacking. Activity analysis of VOC in vitro can be a valuable complement to inhalation toxicological evaluations. We developed an exposure platform that generates a stable VOC atmosphere and allows the exposure of cells for longer periods. Using formaldehyde as a model analyte, air-liquid interface cultured A549 lung epithelial cells were exposed to critical concentrations of 0.1 and 0.5 ppm for 3 days. Owing to the lack of known exposure biomarkers, we applied a genome-wide transcriptional analysis to investigate cellular responses at these sublethal concentrations. We demonstrate a minor overlap of differentially expressed transcripts for both treatment concentrations, which can be further analyzed for their use as exposure biomarkers. Moreover, distinct expression patterns emerge for 0.1 and 0.5 ppm formaldehyde exposure, which is reflected in significant enrichment of distinct biological processes. More specifically, metabolism of specific compound classes, lipid biosynthesis and lung-associated functions are affected by lower exposure levels and processes affecting proliferation and apoptosis dominate the higher exposure levels

Mechanisms for Taste Sensation of Carbonation

Carbonation, or the presence of carbon dioxide (CO2) dissolved in solution, is a commonly encountered feature of beverages in the contemporary human diet. While the popularity of carbonation may be attributed to its distinct sensory qualities, the specific orosensory pathways mediating CO2 detection in mammals have not previously been delineated. This dissertation describes the identification of specific cellular and molecular mechanisms that mediate taste sensation of carbonation, using the mouse as a model system. The mammalian gustatory system is sensitive to CO2, and these responses are sensitive to inhibition of carbonic anhydrases, enzymes that catalyze the interconversion of carbon dioxide with carbonic acid. Through gene expression profiling I discovered that the gene carbonic anhydrase IV (Car4), encoding an extracellular enzyme, is specifically expressed in acid sensing taste receptor cells (TRCs). Genetic ablation of the Car4 locus resulted in a major deficit in gustatory CO2 sensation that is stimulus specific, not affecting responses to acid. Ablation or silencing of acid sensing TRCs likewise produced a profound deficit in taste responses to CO2. These studies identified a primary pathway of the gustatory carbonation response, substantiating acid sensing TRC and the Car4 enzyme as key mediators. A smaller gustatory neural response to carbonation remains even in the absence of sour-sensing TRC and/or Car4. To identify additional carbonation sensing pathways, I applied an in vivo calcium-imaging assay to define the ensemble of primary gustatory neurons activated by CO2. These studies revealed that in addition to robust activation of sour sensing neurons, a secondary gustatory pathway for CO2 detection is mediated by subpopulations of bitter and sweet responsive neurons. I identified carbonic anhydrase VII (Car7) as an intracellular carbonic anhydrase specifically expressed by sweet, bitter and umami sensing TRC. Pharmacological and gene expression data support a role for Car7 in transducing the secondary CO2 sensing pathway. These studies suggested that carbonation acts as a complex gustatory stimulus, stimulating sour, sweet and bitter taste qualities simultaneously. The rules governing peripheral encoding of multi-modal taste stimuli are not well understood. To address this issue, I examined the peripheral gustatory response to binary mixtures of taste qualities. I found that most combinations of taste qualities are represented as a superimposition of the component responses. However, neural responses to attractive stimuli, including natural sugars, artificial sweeteners and umami tastants, are selectively suppressed by simultaneous co-stimulation with a sour (acidic) stimulus. Acid-mediated suppression of sweet is cell autonomous, occurring even in the absence of gustatory acid sensing. Remarkably, carbonation stimulates sour signaling without suppressing sweet taste response. These studies suggest that cross-modal interactions at the periphery modulate the sensory response to complex taste stimuli