Special Issue "Odor Detection: Electronic Nose, Olfactometer, and Advanced Instrumentation"
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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".
Deadline for manuscript submissions: closed (31 August 2012)
Special Issue Editor
Guest Editor
Prof. Dr. Ki-Hyun Kim
Department of Environment & Energy, Sejong University, Goon Ja Dong 98, Gwang Jin Goo, Seoul, 143-747, Korea
Website: http://environment.cafe24.com/
E-Mail: khkim@sejong.ac.kr
Phone: +82 2 3408 3233
Interests: environmental monitoring; volatile organic compounds; reduced sulfur compounds; carbonyls
Special Issue Information
Dear Clleagues,
The monitoring of odorants is often considered a crucial element in the assessment of indoor and outdoor air quality. This is because many odorants exert adverse impacts on human health and comfort, while some can also participate in the formation of photochemical smog. Considering their socio-economic impact, a number of odorants have recently been designated as criteria offensive odorants in many countries. Both qualitative and quantitative detection of odorous pollutants in all environmental matrices (e.g., air, water, and soil) has always been a challenge because of their highly reactive nature and presence in complex matrices at a wide concentration range. More efforts are thus needed to improve our application of sensing techniques to the detection and accurate evaluation of various odorants. This special issue aims to present articles emphasizing more than one of all the available tools to detect odor phenomena or individual odorants: (1) sampling techniques for odor (and odorants), (2) olfactometric approach, (3) electronic nose, (4) other sensing tools and techniques, and (5) advanced instrumentation (e.g., combination of thermal desorption with GC-MS or MS-MS, GC-GC, etc).
Prof. Dr. Ki-Hyun Kim
Guest Editor
Submission
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Keywords
- odor
- sampling
- monitoring
- nuisance
- olfactory
- electronic nose
- instrumentation
Published Papers (20 papers)
Kouki Fujioka, Mika Shirasu, Yoshinobu Manome, Nobuo Ito, Satoshi Kakishima, Tomohiro Minami, Tadashi Tominaga, Fumio Shimozono, Takeo Iwamoto, Keiichi Ikeda, Kenji Yamamoto, Jin Murata and Yasuko Tomizawa
Received: 1 January 2012; in revised form: 7 February 2012 / Accepted: 8 February 2012 / Published: 15 February 2012
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Abstract: As olfactory perceptions vary from person to person, it is difficult to describe smells objectively. In contrast, electronic noses also detect smells with their sensors, but in addition describe those using electronic signals. Here we showed a virtual connection method between a human nose perceptions and electronic nose responses with the smell of standard gases. In this method, Amorphophallus titanum flowers, which emit a strong carrion smell, could objectively be described using an electronic nose, in a way resembling the skill of sommeliers. We could describe the flower smell to be close to that of a mixture of methyl mercaptan and propionic acid, by calculation of the dilution index from electronic resistances. In other words, the smell resembled that of “decayed cabbage, garlic and pungent sour” with possible descriptors. Additionally, we compared the smells of flowers which bloomed on different dates and at different locations and showed the similarity of odor intensities visually, in standard gas categories. We anticipate our assay to be a starting point for a perceptive connection between our noses and electronic noses.
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Received: 21 January 2012; in revised form: 14 March 2012 / Accepted: 22 March 2012 / Published: 27 March 2012
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Abstract: Electrophysiological techniques are used in insect neuroscience to measure the response of olfactory neurons to volatile odour stimuli. Widely used systems to deliver an olfactory stimulus to a test insect include airstream guided flow through glass cartridges loaded with a given volatile compound on a sorbent support. Precise measurement of the quantity of compound reaching the sensory organ of the test organism is an urgent task in insect electrophysiology. In this study we evaluated the performances of the recent realised proton transfer reaction-time of flight mass-spectrometry (PTR-ToF-MS) as a fast and selective gas sensor. In particular, we characterised the gas emission from cartridges loaded with a set of volatile compounds belonging to different chemical classes and commonly used in electrophysiological experiments. PTR-ToF-MS allowed a fast monitoring of all investigated compounds with sufficient sensitivity and time resolution. The detection and the quantification of air contaminants and solvent or synthetic standards impurities allowed a precise quantification of the stimulus exiting the cartridge. The outcome of this study was twofold: on one hand we showed that PTR-ToF-MS allows monitoring fast processes with high sensitivity by real time detection of a broad number of compounds; on the other hand we provided a tool to solve an important issue in insect electrophysiology.
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Received: 30 January 2012; in revised form: 20 March 2012 / Accepted: 22 March 2012 / Published: 27 March 2012
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Abstract: Odor detection thresholds (ODTs) are used for assessing outdoor and indoor air quality. They are obtained experimentally by olfactometry and psychophysical methods, and large compilations are available in the literature. A non-linear regression equation was fitted to describe the ODT variability of 114 aliphatic compounds based on the alkyl chain length for different homologous series (carboxylic acids, aldehydes, 2-ketones, esters, 1-alcohols, amines, thiols, thioethers and hydrocarbons). The resulting equation reveals an effect of the functional group, molecular size and also an interaction between both factors. Although the mechanistic interpretation of results is uncertain, the relatively high goodness-of-fit (R2 = 0.90) suggests that ODT values of aliphatic compounds can be predicted rather accurately, which is not the case for rigid molecules. This equation may serve as a basis for the development of more complex ODT models taking into account diverse structural features of odorants. The variability of power-law exponents was also investigated for the homologous series.
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Received: 10 April 2012; in revised form: 25 May 2012 / Accepted: 5 June 2012 / Published: 11 June 2012
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Abstract: The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification.
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Received: 21 May 2012; in revised form: 13 July 2012 / Accepted: 2 August 2012 / Published: 8 August 2012
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Abstract: Commercial standard gas generators are often complex and expensive devices. The objective of this research was to assess the performance of a simplified glass impinger system for standard gas generation from a permeation tube (PT) device. The performance of the impinger standard gas generation system was assessed for four aromatic VOCs (benzene, toluene, ethylbenzene, and m-xylene; BTEX) at varying flow rates (FR) of 50 to 800 mL·min−1. Because actual permeation rate (APR) values deviated from those computed by the manufacturer’s formula (MPR), new empirical relationships were developed to derive the predicted PR (PPR) of the target components. Experimental results corrected by such a formula indicate that the compatibility between the APR and MPR generally increased with low FR, while the reproducibility was generally reduced with decreasing flow rate. Although compatibility between different PRs is at a relatively small and narrow FR range, the use of correction formula is recommendable for the accurate use of PT.
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Received: 11 May 2012; in revised form: 31 July 2012 / Accepted: 2 August 2012 / Published: 13 September 2012
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Abstract: Species discrimination among three kinds of puffer fish, Takifugu obscurus, Takifugu flavidus and Takifugu rubripes, was conducted using an electronic nose combined with olfactory sensory evaluation. All data were treated by multivariate data processing based on principal component analysis (PCA) and discriminant factor analysis (DFA). The results showed the discriminant model by PCA method and DFA method. Using PCA and DFA, it was shown that the electronic nose was able to reasonably distinguish between each of the eleven puffer fish groups, with a discrimination index of 85. The olfactory sensory evaluation was undertaken in accordance to Sensory analysis—Methodology—Initiation and training of assessors in the detection and recognition of odors (BS ISO 5496-2006), and the results showed that the evaluation was able to identify puffer fish samples according to their species, geographical origin and age. Results from this analysis demonstrate that the E-nose can be used to complement the discrimination of odors by sensory evaluation from the three species of puffer fish studied here.
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Received: 21 July 2012; in revised form: 10 September 2012 / Accepted: 10 September 2012 / Published: 26 September 2012
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Abstract: It is well known that the electronic nose can be used to identify differences between human health and disease for a range of disorders. We present a pilot study to investigate if the electronic nose and a newer technology, FAIMS (Field Asymmetric Ion Mobility Spectrometry), can be used to identify and help inform the treatment pathway for patients receiving pelvic radiotherapy, which frequently causes gastrointestinal side-effects, severe in some. From a larger group, 23 radiotherapy patients were selected where half had the highest levels of toxicity and the others the lowest. Stool samples were obtained before and four weeks after radiotherapy and the volatiles and gases emitted analysed by both methods; these chemicals are products of fermentation caused by gut microflora. Principal component analysis of the electronic nose data and wavelet transform followed by Fisher discriminant analysis of FAIMS data indicated that it was possible to separate patients after treatment by their toxicity levels. More interestingly, differences were also identified in their pre-treatment samples. We believe these patterns arise from differences in gut microflora where some combinations of bacteria result to give this olfactory signature. In the future our approach may result in a technique that will help identify patients at “high risk” even before radiation treatment is started.
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Received: 3 September 2012; in revised form: 18 October 2012 / Accepted: 19 October 2012 / Published: 25 October 2012
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Abstract: Exhaustive odour impact assessment should involve the evaluation of the impact of odours directly on citizens. For this purpose it might be useful to have an instrument capable of continuously monitoring ambient air quality, detecting the presence of odours and also recognizing their provenance. This paper discusses the laboratory and field tests conducted in order to evaluate the performance of a new electronic nose, specifically developed for monitoring environmental odours. The laboratory tests proved the instrument was able to discriminate between the different pure substances being tested, and to estimate the odour concentrations giving correlation indexes (R2) of 0.99 and errors below 15%. Finally, the experimental monitoring tests conducted in the field, allowed us to verify the effectiveness of this electronic nose for the continuous detection of odours in ambient air, proving its stability to variable atmospheric conditions and its capability to detect odour peaks.
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Received: 6 August 2012; in revised form: 2 November 2012 / Accepted: 5 November 2012 / Published: 9 November 2012
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Abstract: We propose a method for building a simple electronic nose based on commercially available sensors used to sniff in the market and identify spoiled/contaminated meat stocked for sale in butcher shops. Using a metal oxide semiconductor-based electronic nose, we measured the smell signature from two of the most common meat foods (beef and fish) stored at room temperature. Food samples were divided into two groups: fresh beef with decayed fish and fresh fish with decayed beef. The prime objective was to identify the decayed item using the developed electronic nose. Additionally, we tested the electronic nose using three pattern classification algorithms (artificial neural network, support vector machine and k-nearest neighbor), and compared them based on accuracy, sensitivity, and specificity. The results demonstrate that the k-nearest neighbor algorithm has the highest accuracy.
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Received: 31 August 2012; in revised form: 31 October 2012 / Accepted: 2 November 2012 / Published: 12 November 2012
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Abstract: Just how we discriminate between the different odours we encounter is notcompletely understood yet. While obviously a matter involving biology, the core issue isa matter for physics: what microscopic interactions enable the receptors in our noses-smallprotein switches—to distinguish scent molecules? We survey what is and is not known aboutthe physical processes that take place when we smell things, highlighting the difficultiesin developing a full understanding of the mechanics of odorant recognition. The maincurrent theories, discussed here, fall into two major groups. One class emphasises thescent molecule's shape, and is described informally as a "lock and key" mechanism. Butthere is another category, which we focus on and which we call "swipe card" theories:the molecular shape must be good enough, but the information that identifies the smellinvolves other factors. One clearly-defined "swipe card" mechanism that we discuss hereis Turin's theory, in which inelastic electron tunnelling is used to discern olfactant vibrationfrequencies. This theory is explicitly quantal, since it requires the molecular vibrations totake in or give out energy only in discrete quanta. These ideas lead to obvious experimentaltests and challenges. We describe the current theory in a form that takes into accountmolecular shape as well as olfactant vibrations. It emerges that this theory can explainmany observations hard to reconcile in other ways. There are still some important gapsin a comprehensive physics-based description of the central steps in odorant recognition. We also discuss how far these ideas carry over to analogous processes involving other smallbiomolecules, like hormones, steroids and neurotransmitters. We conclude with a discussionof possible quantum behaviours in biology more generally, the case of olfaction being justone example. This paper is presented in honour of Prof. Marshall Stoneham who passedaway unexpectedly during its writing.
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Received: 31 August 2012; in revised form: 30 October 2012 / Accepted: 8 November 2012 / Published: 22 November 2012
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Abstract: Volatile organic compounds (VOCs) in cut onions (Allium cepa L.) were continuously measured by PTR-MS during the first 120 min after cutting. The headspace composition changed rapidly due to the very reactive volatile sulfurous compounds emitted from onion tissue after cell disruption. Mass spectral signals corresponding to propanethial S-oxide (the lachrymatory factor) and breakdown products of this compound dominated 0–10 min after cutting. Subsequently, propanethiol and dipropyl disulfide predominantly appeared, together with traces of thiosulfinates. The concentrations of these compounds reached a maximum at 60 min after cutting. Propanethiol was present in highest concentrations and had an odor activity value 20 times higher than dipropyl disulfide. Thus, propanethiol is suggested to be the main source of the characteristic onion odor. Monitoring the rapid changes of VOCs in the headspace of cut onion necessitates a high time resolution, and PTR-MS is demonstrated to be a very suitable method for monitoring the headspace of freshly cut onions directly after cutting without extraction or pre-concentration.
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Received: 30 August 2012; in revised form: 5 November 2012 / Accepted: 13 November 2012 / Published: 22 November 2012
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Abstract: We present an effective portable e-nose system that performs well even in noisy environments. Considering the characteristics of the e-nose data, we use an image covariance matrix-based method for extracting discriminant features for vapor classification. To construct composite vectors, primitive variables of the data measured by a sensor array are rearranged. Then, composite features are extracted by utilizing the information about the statistical dependency among multiple primitive variables, and a classifier for vapor classification is designed with these composite features. Experimental results with different volatile organic compounds data show that the proposed system has better classification performance than other methods in a noisy environment.
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Received: 20 September 2012; in revised form: 15 November 2012 / Accepted: 19 November 2012 / Published: 23 November 2012
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Abstract: This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals.
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Received: 4 September 2012; in revised form: 22 November 2012 / Accepted: 26 November 2012 / Published: 27 November 2012
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Abstract: We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets.
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Received: 13 October 2012; in revised form: 26 November 2012 / Accepted: 4 December 2012 / Published: 7 December 2012
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Abstract: Sewer odors have been a concern to citizens of the Metropolitan Seoul region, which has installed combined sewer systems (CSSs) in 86% of its area. Although a variety of odorants are released from sewers, volatile sulfur compounds (VSCs) have been recognized as major ones. A number of technologies have been proposed to monitor or control odors from sewers. One of the most popular strategies adopted for the control of sewage odor is by applying a commercial odor-reducing agent into the sewer. In this study, the effectiveness of five different commercial odor-reducing agents (i.e., an odor masking agent, an alkaline solution, two microbial agents, and a chemical oxidant) was evaluated by continuously monitoring VSCs released from the sewer with an on-line total reduced sulfur (TRS) analyzer before and after each agent was sprayed into CSSs at five different locations of the city. In short, when the effectiveness of odor treatment was tested in the sewer system using five commercial odor reducing treatments, only the chemical oxidant was good enough to reduce the odor in terms of TRS levels measured before and after the application (p < 0.01).
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Received: 10 November 2012; in revised form: 17 December 2012 / Accepted: 19 December 2012 / Published: 24 December 2012
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Abstract: This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN). This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 μm standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy.
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Received: 17 October 2012; in revised form: 5 December 2012 / Accepted: 17 December 2012 / Published: 24 December 2012
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Abstract: Wine quality is related to its intrinsic visual, taste, or aroma characteristics and is reflected in the price paid for that wine. One of the most important wine faults is the excessive concentration of acetic acid which can cause a wine to take on vinegar aromas and reduce its varietal character. Thereby it is very important for the wine industry to have methods, like electronic noses, for real-time monitoring the excessive concentration of acetic acid in wines. However, aroma characterization of alcoholic beverages with sensor array electronic noses is a difficult challenge due to the masking effect of ethanol. In this work, in order to detect the presence of acetic acid in synthetic wine samples (aqueous ethanol solution at 10% v/v) we use a detection unit which consists of a commercial electronic nose and a HSS32 auto sampler, in combination with a neural network classifier (MLP). To find the characteristic vector representative of the sample that we want to classify, first we select the sensors, and the section of the sensors response curves, where the probability of detecting the presence of acetic acid will be higher, and then we apply Principal Component Analysis (PCA) such that each sensor response curve is represented by the coefficients of its first principal components. Results show that the PEN3 electronic nose is able to detect and discriminate wine samples doped with acetic acid in concentrations equal or greater than 2 g/L.
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Received: 13 December 2012; in revised form: 8 January 2013 / Accepted: 14 January 2013 / Published: 15 January 2013
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Abstract: Sampling is one of the main issues pertaining to odor characterization and measurement. The aim of sampling is to obtain representative information on the typical characteristics of an odor source by means of the collection of a suitable volume fraction of the effluent. The most important information about an emission source for odor impact assessment is the so-called Odor Emission Rate (OER), which represents the quantity of odor emitted per unit of time, and is expressed in odor units per second (ou∙s−1). This paper reviews the different odor sampling strategies adopted depending on source type. The review includes an overview of odor sampling regulations and a detailed discussion of the equipment to be used as well as the mathematical considerations to be applied to obtain the OER in relation to the sampled source typology.
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Received: 31 October 2012; in revised form: 9 January 2013 / Accepted: 16 January 2013 / Published: 25 January 2013
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Abstract: This paper investigates a rapid and accurate detection system for spoilage in meat. We use unsupervised feature learning techniques (stacked restricted Boltzmann machines and auto-encoders) that consider only the transient response from undoped zinc oxide, manganese-doped zinc oxide, and fluorine-doped zinc oxide in order to classify three categories: the type of thin film that is used, the type of gas, and the approximate ppm-level of the gas. These models mainly offer the advantage that features are learned from data instead of being hand-designed. We compare our results to a feature-based approach using samples with various ppm level of ethanol and trimethylamine (TMA) that are good markers for meat spoilage. The result is that deep networks give a better and faster classification than the feature-based approach, and we thus conclude that the fine-tuning of our deep models are more efficient for this kind of multi-label classification task.
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Received: 25 January 2013; in revised form: 21 February 2013 / Accepted: 22 February 2013 / Published: 27 February 2013
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Abstract: Static headspace gas chromatographic (SHS-GC) analysis was performed to determine the release of 13 odorants in hydrocolloid model systems containing original or regio-selectively carboxylated cellulose at different pH values. The release of most odor compounds was decreased in the hydrocolloid solutions compared to control, with the amounts of 2-propanol, 3-methyl-1-butanol, and 2,3-butanedione released into the headspace being less than those of any other odor compound in the hydrocolloid model systems. However, there was no considerable difference between original cellulose-containing and carboxylated-cellulose containing systems in the release of most compounds, except for relatively long-chain esters such as ethyl caprylate and ethyl nonanoate. The release from the original and carboxylated cellulose solutions controlled to pH 10 was significantly higher than that from solutions adjusted to pH 4 and 7 in the case of some esters (ethyl acetate, methyl propionate, ethyl propionate, ethyl butyrate, butyl propionate, ethyl caproate) and alcohols (2-propanol, 3-methyl-1-butanol), in particular, ethyl butyrate and 3-methyl-1-butanol. In contrast, the release of 2,3-butanedione from both the original and carboxylated cellulose solutions was increased at pH 4 and 7 compared to that at pH 10 by about 70% and 130%, respectively. Our study demonstrated that the release of some odorants could be changed significantly by addition of both original and carboxylated cellulose in hydrocolloid model systems, but only minor effect was observed in pH of the solution.
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Last update: 18 May 2012
