An experimental biomimetic platform for artificial olfaction

The Role of a Polymer-Based E-Nose in the Detection of Head and Neck Cancer from Exhaled Breath

The aim of our study was to assess whether a polymer-based e-nose can distinguish head and neck cancer subjects from healthy controls, as well as from patients with allergic rhinitis. A total number of 45 subjects participated in this study. The first group was composed of 15 patients with histology confirmed diagnosis of head and neck cancer. The second group was made up of 15 patients with diagnoses of allergic rhinitis. The control group consisted of 15 subjects with a negative history of upper airways and/or chest symptoms. Exhaled breath was collected from all participants and sampled by a polymer-based e-nose (Cyranose 320, Sensigent, Pasadena, CA, USA). In the Principal Component Analysis plot, patients with head and neck cancer clustered distinctly from the controls as well as from patients with allergic rhinitis. Using canonical discriminant analysis, the three groups were discriminated, with a cross validated accuracy% of 75.1, p < 0.01. The area under the curve of the receiver operating characteristic curve for the discrimination between head and neck cancer patients and the other groups was 0.87. To conclude, e-nose technology has the potential for application in the diagnosis of head and neck cancer, being an easy, quick, non-invasive and cost-effective tool.

Principles of odor coding in vertebrates and artificial chemosensory systems

The biological olfactory system is the sensory system responsible for the detection of the chemical composition of the environment. Several attempts to mimic biological olfactory systems have led to various artificial olfactory systems using different technical approaches. Here we provide a parallel description of biological olfactory systems and their technical counterparts. We start with a presentation of the input to the systems, the stimuli, and treat the interface between the external world and the environment where receptor neurons or artificial chemosensors reside. We then delineate the functions of receptor neurons and chemosensors as well as their overall I-O relationships. Up to this point, our account of the systems goes along similar lines. The next processing steps differ considerably: while in biology the processing step following the receptor neurons is the "integration" and "processing" of receptor neuron outputs in the olfactory bulb, this step has various realizations in electronic noses. For a long period of time, the signal processing stages beyond the olfactory bulb, i.e., the higher olfactory centers were little studied. Only recently there has been a marked growth of studies tackling the information processing in these centers. In electronic noses, a third stage of processing has virtually never been considered. In this review, we provide an up-to-date overview of the current knowledge of both fields and, for the first time, attempt to tie them together. We hope it will be a breeding ground for better information, communication, and data exchange between very related but so far little connected fields.

Gravimetric biosensors

Gravimetric transducers produce a signal based on a change in mass. These transducers can be used to construct gas sensors or biosensors using odorant binding proteins (OBPs) as recognition elements for small volatile organic compounds. The methods described in this chapter are based on the immobilization of the OBPs onto functionalized (activated) self-assembled monolayer (SAMs) on gold and on nanocrystalline diamond surfaces. Depending on the surface immobilization methods used to fabricate the biosensor, recombinant proteins can be engineered to express six histidine tags either on the N-terminal or C-terminal of the proteins and these can also be used to facilitate protein immobilization. These methods are used to produce functional sensors based on quartz crystal microbalances or surface acoustic wave devices and are also applicable to other types of gravimetric transducers.

Selective detection of volatile organic compounds in microfluidic gas detectors based on "like dissolves like"

This paper studies the effect of channel coating hydrophobicity and analyte polarity on the gas detection capability of a microfluidic-based gas detector. Two detectors with two different channel surface coating combinations (resulting in different levels of hydrophobicity) are fabricated and tested against seven analytes with different polarities (methanol, ethanol, 1-propanol, 2-pentanol, acetone, pentane, and hexane). A feature extraction method is utilized to compare the discrimination capability of each of the fabricated detector. The analysis of the combined feature space presented for both detectors reveals that the Euclidean distance, which is an indicator of the device discrimination capability between different gases, between the feature vectors of the two sensors are greater for non-polar gases compared to those obtained for the polar ones. This shows that the analyte discrimination in microfluidic gas detectors is not a purely diffusion-based process, and there are analyte/channel surface interaction parameters involved in enhancing/impeding sensor selectivity. To understand these effects, the surface free energy of each fabricated channel was determined. It is shown that the difference between the solid-liquid surface tension values estimated for the two channel surfaces is higher for the non-polar analytes as compared to the polar analytes. This effect along with the low diffusion coefficients of non-polar analyte magnifies adsorption of the analytes in the diffusion-physisorption process, resulting in a greater difference in Euclidean distances between the features obtained from the two detectors responses against non-polar analytes as compared to the polar ones. This shows that the choice of the detector's channel coating material plays a key role in the selectivity of the device between different gases. As a result, non-polar channel coating surfaces are suggested for better classification of the non-polar gases, and it is shown in the cases of polar gases changing the coating surface has less effect.

Porphyrins for olfaction mimic: The Rome Tor Vergata approach

The impressive chemistry shown by porphyrins in natural systems is particularly attractive for exploitation in chemical sensors. In these devices the sensing mechanisms can mimic most of the porphyrin biological reactivity, such as reversible binding, activation of small molecules, redox activity, and photoactivated processes. The simultaneous presence of multiple binding mechanisms allows porphyrins to interact with a large variety of analytes. This feature reduces the selectivity, but prompts the development of sensor arrays, where the cross-selectivity of more sensors is used to classify and identify samples characterized by a complex composition. Since 1995 the Sensors Group of the University of Rome Tor Vergata has exploited these features to prepare sensor arrays based on different transducers and aimed at several applications. These kinds of devices have been reported as electronic noses (gaseous phase analytes) and electronic tongues (liquid phase analytes) to underline that their working mechanisms are tentatively similar to that of biological senses. We report here some of the results obtained.

Porphyrinoids for Chemical Sensor Applications

Porphyrins and related macrocycles have been intensively exploited as sensing materials in chemical sensors, since in these devices they mimic most of their biological functions, such as reversible binding, catalytic activation, and optical changes. Such a magnificent bouquet of properties allows applying porphyrin derivatives to different transducers, ranging from nanogravimetric to optical devices, also enabling the realization of multifunctional chemical sensors, in which multiple transduction mechanisms are applied to the same sensing layer. Potential applications are further expanded through sensor arrays, where cross-selective sensing layers can be applied for the analysis of complex chemical matrices. The possibility of finely tuning the macrocycle properties by synthetic modification of the different components of the porphyrin ring, such as peripheral substituents, molecular skeleton, coordinated metal, allows creating a vast library of porphyrinoid-based sensing layers. From among these, one can select optimal arrays for a particular application. This feature is particularly suitable for sensor array applications, where cross-selective receptors are required. This Review briefly describes chemical sensor principles. The main part of the Review is divided into two sections, describing the porphyrin-based devices devoted to the detection of gaseous or liquid samples, according to the corresponding transduction mechanism. Although most devices are based on porphyrin derivatives, seminal examples of the application of corroles or other porphyrin analogues are evidenced in dedicated sections.

Stable odor recognition by a neuro-adaptive electronic nose

Sensitivity, selectivity and stability are decisive properties of sensors. In chemical gas sensors odor recognition can be severely compromised by poor signal stability, particularly in real life applications where the sensors are exposed to unpredictable sequences of odors under changing external conditions. Although olfactory receptor neurons in the nose face similar stimulus sequences under likewise changing conditions, odor recognition is very stable and odorants can be reliably identified independently from past odor perception. We postulate that appropriate pre-processing of the output signals of chemical sensors substantially contributes to the stability of odor recognition, in spite of marked sensor instabilities. To investigate this hypothesis, we use an adaptive, unsupervised neural network inspired by the glomerular input circuitry of the olfactory bulb. Essentially the model reduces the effect of the sensors’ instabilities by utilizing them via an adaptive multicompartment feed-forward inhibition. We collected and analyzed responses of a 4 × 4 gas sensor array to a number of volatile compounds applied over a period of 18 months, whereby every sensor was sampled episodically. The network conferred excellent stability to the compounds’ identification and was clearly superior over standard classifiers, even when one of the sensors exhibited random fluctuations or stopped working at all.

Data simulation in machine olfaction with the R package chemosensors

In machine olfaction, the design of applications based on gas sensor arrays is highly dependent on the robustness of the signal and data processing algorithms. While the practice of testing the algorithms on public benchmarks is not common in the field, we propose software for performing data simulations in the machine olfaction field by generating parameterized sensor array data. The software is implemented as an R language package chemosensors which is open-access, platform-independent and self-contained. We introduce the concept of a virtual sensor array which can be used as a data generation tool. In this work, we describe the data simulation workflow which basically consists of scenario definition, virtual array parameterization and the generation of sensor array data. We also give examples of the processing of the simulated data as proof of concept for the parameterized sensor array data: the benchmarking of classification algorithms, the evaluation of linear- and non-linear regression algorithms, and the biologically inspired processing of sensor array data. All the results presented were obtained under version 0.7.6 of the chemosensors package whose home page is chemosensors.r-forge.r-project.org.

Exhaled breath analysis by electronic nose in airways disease. Established issues and key questions

Exhaled air contains many volatile organic compounds (VOCs) that are the result of normal and disease-associated metabolic processes anywhere in the body. Different omics techniques can assess the pattern of these VOCs. One such omics technique suitable for breath analysis is represented by electronic noses (eNoses), providing fingerprints of the exhaled VOCs, called breathprints. Breathprints have been shown to be altered in different disease states, including in asthma and COPD. This review describes the current status on clinical validation and application of breath analysis by electronic noses in the diagnosis and monitoring of chronic airways diseases. Furthermore, important methodological issues including breath sampling, modulating factors and incompatibility between eNoses are raised and discussed. Next steps towards clinical application of electronic noses are provided, including further validation in suspected disease, assessment of the influence of different comorbidities, the value in longitudinal monitoring of patients with asthma and COPD and the possibility to predict treatment responses. Eventually, a Breath Cloud may be constructed, a large database containing disease-specific breathprints. When collaborative efforts are put into optimization of this technique, it can provide a rapid and non-invasive first line diagnostic test.

From a Laboratory Exercise for Students to a Pioneering Biosensing Technology

Surface plasmon resonance (SPR) for biosensing was demonstrated 30 years ago. In the present contribution, its general background is described together with the necessary developments both in instrumentation and surface chemistry, leading to the final so-called BIAcore technology. The description is naturally colored by my personal opinion of the developments. SPR for the elucidation of organic mono- and multilayers introduced at the end of the 1970s formed the basis for the first biosensing demonstration of SPR in the beginning of the 1980s. It is pointed out how the need of an up-to-date laboratory exercise for the undergraduate students and the multidisciplinary environment at the Laboratory of Applied Physics at Linköping University led to this demonstration. The initial experiments are touched upon and the further developments at Pharmacia, which led to the BIAcore technology, are described in some details. Some of the present activities in Linköping related to optical biosensing with ubiquitous instrumentation are also described, including SPR detection with a computer screen and a web camera and most recently with a cellular phone.

The looks of an odour--visualising neural odour response patterns in real time

BACKGROUND

Calcium imaging in insects reveals the neural response to odours, both at the receptor level on the antenna and in the antennal lobe, the first stage of olfactory information processing in the brain. Changes of intracellular calcium concentration in response to odour presentations can be observed by employing calcium-sensitive, fluorescent dyes. The response pattern across all recorded units is characteristic for the odour.

METHOD

Previously, extraction of odour response patterns from calcium imaging movies was performed offline, after the experiment. We developed software to extract and to visualise odour response patterns in real time. An adaptive algorithm in combination with an implementation for the graphics processing unit enables fast processing of movie streams. Relying on correlations between pixels in the temporal domain, the calcium imaging movie can be segmented into regions that correspond to the neural units.

RESULTS

We applied our software to calcium imaging data recorded from the antennal lobe of the honeybee Apis mellifera and from the antenna of the fruit fly Drosophila melanogaster. Evaluation on reference data showed results comparable to those obtained by previous offline methods while computation time was significantly lower. Demonstrating practical applicability, we employed the software in a real-time experiment, performing segmentation of glomeruli--the functional units of the honeybee antennal lobe--and visualisation of glomerular activity patterns.

CONCLUSIONS

Real-time visualisation of odour response patterns expands the experimental repertoire targeted at understanding information processing in the honeybee antennal lobe. In interactive experiments, glomeruli can be selected for manipulation based on their present or past activity, or based on their anatomical position. Apart from supporting neurobiology, the software allows for utilising the insect antenna as a chemosensor, e.g. to detect or to classify odours.

Spherical porphyrin sensor array based on encoded colloidal crystal beads for VOC vapor detection

A spherical porphyrin sensor array using colloidal crystal beads (CCBs) as the encoding microcarriers has been developed for VOC vapor detection. Six different porphyrins were coated onto the CCBs with distinctive encoded reflection peaks via physical adsorption and the sensor array was fabricated by placing the prepared porphyrin-modified CCBs together. The change in fluorescence color of the porphyrin-modified CCBs array serves as the detection signal for discriminating between different VOC vapors and the reflection peak of the CCBs serves as the encoding signal to distinguish between different sensors. It was demonstrated that the VOC vapors detection using the prepared sensor array showed excellent discrimination: not only could the compounds from the different chemical classes be easily differentiated (e.g., alcohol vs acids vs ketones) but similar compounds from the same chemical family (e.g., methanol vs ethanol) and the same compound with different concentration ((e.g., Sat. ethanol vs 60 ppm ethanol vs 10 ppm ethanol) could also be distinguished. The detection reproducibility and the humidity effect were also investigated. The present spherical sensor array, with its simple preparation, rapid response, high sensitivity, reproducibility, and humidity insensitivity, and especially with stable and high-throughput encoding, is promising for real applications in artificial olfactory systems.

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.

Spectral fingerprinting of porphyrins for distributed chemical sensing

Recent progress in spectral fingerprinting of fluorescent indicators using distributed instrumentation based on consumer electronic devices is reviewed. In particular, the evaluation of disposable assays using a computer screen photo-assisted technique (CSPT) is discussed. Sample identification and optimization strategies are analyzed as well as the underlying theoretical background for polychromatic spectral fingerprinting.

An investigation on the role of spike latency in an artificial olfactory system

Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time.

Polymers with embedded chemical indicators as an artificial olfactory mucosa

Physiological investigations suggest that the olfactory mucosa probably plays an ancillary role in the recognition of odours introducing a sort of chromatographic separation that, together with the zonal distribution of olfactory receptors, gives place to selective spatio-temporal response patterns. It has been recently suggested that this behaviour may be simulated by chemical sensors embedded in continuous polymer layers. In this paper, in analogy to the biology of olfaction, a simple and compact platform able to separate and detect gases and vapours on the basis of their diffusion properties is proposed. In such a system, broadly selective colour indicators, such as metalloporphyrins, are embedded in continuous layers of polymers with different sorption properties. The exposure to various alcohols and amines shows that the porphyrins are mainly responsible for the recognition of the molecular family, while the occurring spatio-temporal signal patterns make possible the identification of the individual chemical species.

Urinary volatile compounds as biomarkers for lung cancer: a proof of principle study using odor signatures in mouse models of lung cancer

A potential strategy for diagnosing lung cancer, the leading cause of cancer-related death, is to identify metabolic signatures (biomarkers) of the disease. Although data supports the hypothesis that volatile compounds can be detected in the breath of lung cancer patients by the sense of smell or through bioanalytical techniques, analysis of breath samples is cumbersome and technically challenging, thus limiting its applicability. The hypothesis explored here is that variations in small molecular weight volatile organic compounds (“odorants”) in urine could be used as biomarkers for lung cancer. To demonstrate the presence and chemical structures of volatile biomarkers, we studied mouse olfactory-guided behavior and metabolomics of volatile constituents of urine. Sensor mice could be trained to discriminate between odors of mice with and without experimental tumors demonstrating that volatile odorants are sufficient to identify tumor-bearing mice. Consistent with this result, chemical analyses of urinary volatiles demonstrated that the amounts of several compounds were dramatically different between tumor and control mice. Using principal component analysis and supervised machine-learning, we accurately discriminated between tumor and control groups, a result that was cross validated with novel test groups. Although there were shared differences between experimental and control animals in the two tumor models, we also found chemical differences between these models, demonstrating tumor-based specificity. The success of these studies provides a novel proof-of-principle demonstration of lung tumor diagnosis through urinary volatile odorants. This work should provide an impetus for similar searches for volatile diagnostic biomarkers in the urine of human lung cancer patients.