Electronic Nose: Current Status and Future Trends

Institute of Physical and Theoretical Chemistry, University of Tübingen, Auf der Morgenstelle 15, Tübingen, Germany.
Chemical Reviews (Impact Factor: 46.57). 03/2008; 108(2):705-25. DOI: 10.1021/cr068121q
Source: PubMed


The development of the electronic nose have paved the way for the classification of bacteria, to monitor air quality on the space shuttle, or to check the spoilage of foodstuff. However, the electronic nose still is unable to discriminated between flavors, perfumes, smells and as a replacement for the human nose. Although it has been used to detect some important nonodorant gases, it is not adapted to substances of daily importance in mammalian life such as the scent of other animals, foodstuff or spoilage. Due to such limitations, the electronic nose was developed to mimic the human nose. It turns out that the human nose's unequaled performance is not due to the high number of different human receptor cells, but their selectivity and their unsurpassed sensitivity for some analyte gases. As such, the success of the electronic nose will not rely on increasing the number of individual sensors and creating redundant information by adding more similar sensors, but rather on DNA, molecular, imprinted molecules or even mobilized natural receptors, which promise to increase the sensitivity and importantly selectivity. An increase in the sensitivity can be achieved by appropriate sample pretreatment and preconcentration techniques, whereas filters and separation units can be used to increase the selectivity and reduce interfering substances.

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    • "Odourous compounds are usually detected in it owing to the presence of a set of non-specific chemical sensors. However, its possibilities are much smaller than those of its " biological counterpart " , for example, due to the necessity of using a complex mathematical apparatus, which is responsible for proper interpretation of results (Rock et al. 2008; Wilson and Baietto 2009; Sankaran et al. 2012; Gebicki et al. 2014a, b; Boeker 2014). Figure 5 presents a diagram with the principle of electronic nose operation. "
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    ABSTRACT: The study presents information about the measurement techniques used for the assessment of air quality in terms of the olfactory intensity resulting from the operation of municipal sewage treatment plants. Advantages and disadvantages of the measurement techniques used are presented. Sources of malodourous substance emission from sewage treatment plants were described, and the malodourous substances emitted were characterised. Trends in development of analysis and monitoring of the malodourous substances in the air were also presented.
    Full-text · Article · Jan 2016 · Environmental Monitoring and Assessment
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    • "An electronic nose (eNose) is a chemical vapor analyzer, containing an array of cross-reactive sensors [1] [2]. Metal-oxides, polymers, cantilevers and optical arrays are among the techniques that have been utilized for breath analysis. "
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    ABSTRACT: Currently, many different methods are being used for pre-processing, statistical analysis and validation of data obtained by electronic nose technology from exhaled air. These various methods, however, have never been thoroughly compared. We aimed to empirically evaluate and compare the influence of different dimension reduction, classification and validation methods found in published studies on the diagnostic performance in several datasets. Our objective was to facilitate the selection of appropriate statistical methods and to support reviewers in this research area. We reviewed the literature by searching Pubmed up to the end of 2014 for all human studies using an electronic nose and methodological quality was assessed using the QUADAS-2 tool tailored to our review. Forty-six studies were evaluated regarding the range of different approaches to dimension reduction, classification and validation. From forty-six reviewed articles only seven applied external validation in an independent dataset, mostly with a case-control design. We asked their authors to share the original datasets with us. Four of the seven datasets were available for re-analysis. Published statistical methods for eNose signal analysis found in the literature review were applied to the training set of each dataset. The performance (area under the receiver operating characteristics curve (ROC-AUC)) was calculated for the training cohort (in-set) and after internal validation (leave-one-out cross validation). The methods were also applied to the external validation set to assess the external validity of the performance. Risk of bias was high in most studies due to non-random selection of patients. Internal validation resulted in a decrease in ROC-AUCs compared to in-set performance: -0.15,-0.14,-0.1,-0.11 in dataset 1 through 4, respectively. External validation resulted in lower ROC-AUC compared to internal validation in dataset 1 (-0.23) and 3 (-0.09). ROC-AUCs did not decrease in dataset 2 (+0.07) and 4 (+0.04). No single combination of dimension reduction and classification methods gave consistent results between internal and external validation sets in this sample of four datasets. This empirical evaluation showed that it is not meaningful to estimate the diagnostic performance on a training set alone, even after internal validation. Therefore, we recommend the inclusion of an external validation set in all future eNose projects in medicine.
    Full-text · Article · Dec 2015 · Journal of Breath Research
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    • "In gas applications, the presence of complex compounds like water vapor with the gases of interest creates one of the challenging issues for the gas identification using EN [4]. The presence of battery further limits the life and durability of the sensor tag. "

    Full-text · Article · Nov 2015 · Sensors and Transducers
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