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Journal articles on the topic 'Electronic nose'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

KITA, Junichi, Masayuki OKADA, Hisamitu AKAMARU, and Motoo KINOSHITA. "Electronic Nose." Journal of Japan Association on Odor Environment 37, no. 3 (2006): 172–78. http://dx.doi.org/10.2171/jao.37.172.

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2

Osowski, Stanislaw, Krzysztof Siwek, and Tomasz Grzywacz. "Exploration of noisy data in differential electronic nose." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 35, no. 4 (2016): 1382–92. http://dx.doi.org/10.1108/compel-08-2015-0279.

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Purpose – The paper is concerned with exploration of sensor signals in differential electronic nose. It is a special type of nose, which applies double sensor matrices and exploits only their differential signals, which are used in recognition of patterns associated with them. The purpose of this paper is to study the application of differential nose in dynamic measurement of aroma of 11 brands of cigarettes. Design/methodology/approach – The most important task in pattern recognition using electronic nose is its resistance to the noise corrupting the measurement. The authors will analyze and

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3

Shurmer, H. V. "The electronic nose." Analytical Proceedings including Analytical Communications 31, no. 1 (1994): 39. http://dx.doi.org/10.1039/ai9943100039.

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4

NANTO, Hidehito. "Electronic Nose System." Journal of the Society of Mechanical Engineers 107, no. 1033 (2004): 944–45. http://dx.doi.org/10.1299/jsmemag.107.1033_944.

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5

Ding, Qinghang, Dongjie Zhao, Jun liu, and Zeming Yang. "Detection of fruits in warehouse using Electronic nose." MATEC Web of Conferences 232 (2018): 04035. http://dx.doi.org/10.1051/matecconf/201823204035.

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An electronic nose system for storage environments was built. The system would consist of sensor array, data acquisition module and host computer software. The electronic nose made up with eight metal-oxide-semiconductor gas sensors was used to test four types of stone fruit (Dragon fruit, Snow pear, Kiwi fruit, and Fuji apple). The results showed that the rate of corruption of kiwifruit was the fastest, followed by pitaya, and the apples and pears was comparable. The tested fruits can be divided into fresh and spoiled grades by using PCA. And finally, according to the problems related to node

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6

Edita, Raudienė, Gailius Darius, Rimanté Vinauskienė, et al. "Rapid evaluation of fresh chicken meat quality by electronic nose." Czech Journal of Food Sciences 36, No. 5 (2018): 420–26. http://dx.doi.org/10.17221/419/2017-cjfs.

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A prototype of electronic nose (e-nose) with the gas sensor system for evaluation of fresh chicken meat freshness was developed. In this paper a rapid, simple and not expensive system for fresh chicken meat spoilage detection was investigated that provides objective and reliable results. Quality changes in fresh chicken meat during storage were monitored by the metal oxide sensor (MOS) system and compared with the results of traditional chemical measurements. Gas sensor selection was tested for evaluation of volatile fatty acids (VFA) mainly representing meat spoilage.The study demonstrated th

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7

"Electronic Nose Detects Cancer." Journal of Medical Sciences 11, no. 8 (2011): 309. http://dx.doi.org/10.3923/jms.2011.309.309.

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8

Boeker, Peter. "On ‘Electronic Nose’ methodology." Sensors and Actuators B: Chemical 204 (December 2014): 2–17. http://dx.doi.org/10.1016/j.snb.2014.07.087.

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9

Riyanarto, Sarno, and Rahman Wijaya Dedy. "Recent development in electronic nose data processing for beef quality assessment." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 1 (2019): 337–48. https://doi.org/10.12928/TELKOMNIKA.v17i1.10565.

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Beef is kind of perishable food that easily to decay. Hence, a rapid system for beef quality assessment is needed to guarantee the quality of beef. In the last few years, electronic nose (e-nose) is developed for beef spoilage detection. In this paper, we discuss the challenges of e-nose application to beef quality assessment, especially in e-nose data processing. We also provide a summary of our previous studies that explains several methods to deal with gas sensor noise, sensor array optimization problem, beef quality classification, and prediction of the microbial population in beef sample.

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10

Aditama, Farel Ahadyatulakbar, Lalu Zulfikri, Laili Mardiana, Tri Mulyaningsih, Nurul Qomariyah, and Rahadi Wirawan. "Electronic nose sensor development using ANN backpropagation for Lombok Agarwood classification." Research in Agricultural Engineering 66, No. 3 (2020): 97–103. http://dx.doi.org/10.17221/26/2020-rae.

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The aim of the present study is the development of an electronic nose system prototype for the classification of Gyrinops versteegii agarwood. The prototype consists of three gas sensors, i.e., TGS822, TGS2620, and TGS2610. The data acquisition and quality classification of the nose system are controlled by the Artificial Neural Network backpropagation algorithm in the Arduino Mega2650 microcontroller module. The testing result shows that an electronic nose can distinguish the quality of Gyrinops versteegii agarwood. The good-quality agarwood has an output of [1 –1], while the poor-quality aga

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11

Imam, Syed A., and M. R. Khan. "TGS Sensors in Electronic Nose for Multimedia Applications: A Practical Approach." Asia Pacific Business Review 3, no. 2 (2007): 102–12. http://dx.doi.org/10.1177/097324700700300211.

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Multimedia systems are widely used in consumer electronics environments today, where humans can work and communicate through multi-sensory interfaces. Unfortunately smell detection and generation systems are not part of today's multimedia systems. In this paper, we propose an Electronic Nose based on TGS-822 sensors that can be used in a multimedia environment. TGS-822 sensor based electronic nose can detect a large number of Volatile Organic Compounds (VOCs) that have some smell and will have a significantly lower cost compared to the other detection systems. The results and the calibration g

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12

Arasaradnam, R. P., C. U. Nwokolo, K. D. Bardhan, and J. A. Covington. "Electronic nose versus canine nose: clash of the titans." Gut 60, no. 12 (2011): 1768. http://dx.doi.org/10.1136/gut.2011.241216.

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13

Korsa, Matiyas Tsegay, Josep Maria Carmona Domingo, Lawrence Nsubuga, et al. "Optimizing Piezoelectric Cantilever Design for Electronic Nose Applications." Chemosensors 8, no. 4 (2020): 114. http://dx.doi.org/10.3390/chemosensors8040114.

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This work demonstrates a method to optimize materials and dimensions of piezoelectric cantilevers for electronic nose applications via finite element analysis simulations. Here we studied the optimum piezoelectric cantilever configuration for detection of cadaverine, a biomarker for meat ageing, to develop a potential electronic nose for the meat industry. The optimized cantilevers were fabricated, characterized, interfaced using custom-made electronics, and tested by approaching meat pieces. The results show successful measurements of cadaverine levels for meat pieces with different ages, hen

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14

García, M., M. Aleixandre, J. Gutiérrez, and M. C. Horrillo. "Electronic nose for wine discrimination." Sensors and Actuators B: Chemical 113, no. 2 (2006): 911–16. http://dx.doi.org/10.1016/j.snb.2005.03.078.

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15

García, M., M. Aleixandre, J. Gutiérrez, and M. C. Horrillo. "Electronic nose for ham discrimination." Sensors and Actuators B: Chemical 114, no. 1 (2006): 418–22. http://dx.doi.org/10.1016/j.snb.2005.04.045.

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16

Shurmer, H., A. Fard, J. Barker, P. Bartlett, G. Dodd, and U. Hayat. "Development of an electronic nose." Physics in Technology 18, no. 4 (1987): 170–76. http://dx.doi.org/10.1088/0305-4624/18/4/i04.

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17

Baltes, H., D. Lange, and A. Koll. "The electronic nose in Lilliput." IEEE Spectrum 35, no. 9 (1998): 35–38. http://dx.doi.org/10.1109/6.715182.

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18

Abbasi, Jennifer. "“Electronic Nose” Predicts Immunotherapy Response." JAMA 322, no. 18 (2019): 1756. http://dx.doi.org/10.1001/jama.2019.18225.

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19

Ye, Zhenyi, Yuan Liu, and Qiliang Li. "Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods." Sensors 21, no. 22 (2021): 7620. http://dx.doi.org/10.3390/s21227620.

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Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in ra

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20

Winquist, F., C. Krantz-Rülcker, and I. Lundström. "Electronic Tongues." MRS Bulletin 29, no. 10 (2004): 726–31. http://dx.doi.org/10.1557/mrs2004.210.

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AbstractThe use of multivariate data analysis combined with sensors with partially overlapping selectivities has become a very powerful tool in measurement technology. These systems are often referred to as artificial senses, because they function in a way similar to the human senses. One such system is the electronic nose. This article focuses on similar concepts as the electronic nose, but for use in aqueous solutions. Because these systems are related to the human sense of taste in the same way the electronic nose is related to olfaction, they have been termed taste sensors, or “electronic

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21

Cai, Minhao, Sai Xu, Xingxing Zhou, and Huazhong Lu. "Electronic Nose Humidity Compensation System Based on Rapid Detection." Sensors 24, no. 18 (2024): 5881. http://dx.doi.org/10.3390/s24185881.

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In this study, we present an electronic nose (e-nose) humidity compensation system based on rapid detection to solve the issue of humidity drift’s potential negative impact on the performance of electronic noses. First, we chose the first ten seconds of non-steady state (rapid detection mode) sensor data as the dataset, rather than waiting for the electronic nose to stabilize during the detection process. This was carried out in the hope of improving the detection efficiency of the e-nose and to demonstrate that the e-nose can collect gasses efficiently in rapid detection mode. The random fore

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22

Zailani, Nur Ain Zarani, and Muhammad Sharfi Najib. "Inspection of Crude Oil Condition using Electronic Nose (E-Nose)." MEKATRONIKA 2, no. 1 (2020): 47–51. http://dx.doi.org/10.15282/mekatronika.v2i1.6729.

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Oil and gas production and distribution processes technologies are highly complex and capital-intensive. Crude oil is a high demand commodity in Malaysia and across the world. Physical and chemical properties are used to classify crude oil in oil and gas industries. The human's nose cannot distinguish the difference of smell among various crude oils grade. Conventional approaches to detect odour are expensive and difficult to operate. Due to declining production and increasing demand, using E-nose technologies to inspect the odour condition of crude oil might be a significant change in the ind

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23

Yang, Yoon-Seok, Yong-Shin Kim, Seung-Chul Ha, et al. "A portable electronic nose (E-Nose) system using PDA device." Journal of Sensor Science and Technology 14, no. 2 (2005): 69–77. http://dx.doi.org/10.5369/jsst.2005.14.2.069.

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24

Aronzon, Anna, C. William Hanson, and Erica R. Thaler. "Differentiation between Cerebrospinal Fluid and Serum with Electronic Nose." Otolaryngology–Head and Neck Surgery 133, no. 1 (2005): 16–19. http://dx.doi.org/10.1016/j.otohns.2005.03.021.

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OBJECTIVE: The study investigates the ability of the electronic nose to differentiate between cerebrospinal fluid (CSF) and serum and to identify an unknown specimen as CSF or serum. STUDY DESIGN AND SETTING: CSF and serum specimens were heated and tested with an organic semiconductor-based Cyranose 320 electronic nose (Cyrons Sciences, Pasadena, CA). Data from the 32-element sensor array were subjected to principal component analysis to depict differences in odorant patterns. RESULTS: The electronic nose was able to distinguish between CSF and serum isolates with Mahalanobis distance >5. F

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25

Arroyo, Patricia, Félix Meléndez, José Ignacio Suárez, José Luis Herrero, Sergio Rodríguez, and Jesús Lozano. "Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements." Sensors 20, no. 3 (2020): 786. http://dx.doi.org/10.3390/s20030786.

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This paper introduces a miniaturized personal electronic nose (39 mm × 33 mm), which is managed through an app developed on a smartphone. The electronic nose (e-nose) incorporates four new generation digital gas sensors. These MOx-type sensors incorporate a microcontroller in the same package, being also smaller than the previous generation. This makes it easier to integrate them into the electronics and improves their performance. In this research, the application of the device is focused on the detection of atmospheric pollutants in order to complement the information provided by the referen

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26

Shen, F., Q. Wu, A. Su, P. Tang, X. Shao, and B. Liu. "Detection of adulteration in freshly squeezed orange juice by electronic nose and infrared spectroscopy." Czech Journal of Food Sciences 34, No. 3 (2016): 224–32. http://dx.doi.org/10.17221/303/2015-cjfs.

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The use of electronic nose and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) as rapid tools for detection of orange juice adulteration has been preliminarily investigated and compared. Freshly squeezed orange juices were tentatively adulterated with 100% concentrated orange juices at levels ranging from 0% to 30% (v/v). Then the E-nose response signals and FTIR spectra collected from samples were subjected to multivariate analysis by principal component analysis (PCA) and linear discriminant analysis (LDA). PCA indicated that authentic juices and adulterated o

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27

Valente, Nuno, Alisa Rudnitskaya, João Oliveira, Elvira Gaspar, and M. Gomes. "Cheeses Made from Raw and Pasteurized Cow’s Milk Analysed by an Electronic Nose and an Electronic Tongue." Sensors 18, no. 8 (2018): 2415. http://dx.doi.org/10.3390/s18082415.

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Cheese prepared from whole milk, raw and pasteurized, were analysed by an electronic nose based on piezoelectric quartz crystals and an electronic tongue based on potentiometric sensors, immediately after their preparation and along ripening (after 7 and 21 days). Whey was also analysed by the potentiometric electronic tongue. Results obtained by the electronic nose and tongue were found to be complementary, with the electronic nose being more sensitive to differences in the milk and the electronic tongue being more sensitive to milk pasteurization. Electronic tongue was able to distinguish ch

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28

Bassi, PierFrancesco, Luca Di Gianfrancesco, Luigi Salmaso, et al. "Improved Non-Invasive Diagnosis of Bladder Cancer with an Electronic Nose: A Large Pilot Study." Journal of Clinical Medicine 10, no. 21 (2021): 4984. http://dx.doi.org/10.3390/jcm10214984.

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Background: Bladder cancer (BCa) emits specific volatile organic compounds (VOCs) in the urine headspace that can be detected by an electronic nose. The diagnostic performance of an electronic nose in detecting BCa was investigated in a pilot study. Methods: A prospective, single-center, controlled, non-randomized, phase 2 study was carried out on 198 consecutive subjects (102 with proven BCa, 96 controls). Urine samples were evaluated with an electronic nose provided with 32 volatile gas analyzer sensors. The tests were repeated at least two times per sample. Accuracy, sensitivity, specificit

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29

Thaler, Erica R., and C. William Hanson. "Medical applications of electronic nose technology." Expert Review of Medical Devices 2, no. 5 (2005): 559–66. http://dx.doi.org/10.1586/17434440.2.5.559.

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30

Mantini, Alessandro, Corrado Di Natale, Antonella Macagnano, Roberto Paolesse, Alessandro Finazzi-Agro, and Amaldo D'Amico. "Biomedical Application of an Electronic Nose." Critical Reviews in Biomedical Engineering 28, no. 3-4 (2000): 481–85. http://dx.doi.org/10.1615/critrevbiomedeng.v28.i34.210.

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31

Makin, Simon. "Restoring smell with an electronic nose." Nature 606, no. 7915 (2022): S12—S13. http://dx.doi.org/10.1038/d41586-022-01630-1.

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32

Deng, Yibo. "Processing and Prospect of Electronic Nose." E3S Web of Conferences 271 (2021): 01038. http://dx.doi.org/10.1051/e3sconf/202127101038.

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Recently, a Novel Coronavirus Pneumonia (NCP) swept the globe. This kind of new virus has extremely high infection efficiency, which has brought great disaster to human's production and daily life. However, it is reported that lung cancer and other diseases with a high incidence will lead to a very high mortality rate if they are not diagnosed and treated in time. Therefore, it is urgent to design an accurate and convenient new diagnostic equipment. Due to the increasing innovation of sensor module in the field of Information and Communication Technology (ICT), electronic nose emerges as the t

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33

Montuschi, Paolo, Nadia Mores, Andrea Trové, Chiara Mondino, and Peter J. Barnes. "The Electronic Nose in Respiratory Medicine." Respiration 85, no. 1 (2013): 72–84. http://dx.doi.org/10.1159/000340044.

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34

Stinson, Stephen. "Electronic nose moves closer to reality." Chemical & Engineering News 69, no. 12 (1991): 23–24. http://dx.doi.org/10.1145/127261.127265.

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35

Keerthana, S., and B. Santhi. "Survey on Applications of Electronic Nose." Journal of Computer Science 16, no. 3 (2020): 314–20. http://dx.doi.org/10.3844/jcssp.2020.314.320.

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36

Castro, R., M. Kr Mandal, P. Ajemba, and M. A. Istihad. "An electronic nose for multimedia applications." IEEE Transactions on Consumer Electronics 49, no. 4 (2003): 1431–37. http://dx.doi.org/10.1109/tce.2003.1261251.

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37

Manser, Abderrazak, and Tarik Saidi. "Electronic nose system design and analysis." ITM Web of Conferences 69 (2024): 02006. https://doi.org/10.1051/itmconf/20246902006.

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This paper presents the design and analysis of a conditioning circuit for Metal-Oxide Semiconductor (MOS) gas sensors within an electronic nose system. By effectively addressing the challenges of signal amplification, filtering, and stabilization, the proposed circuit enhances the reliability and accuracy of gas detection. The system architecture, which includes temperature control and signal modulation, is optimized for precise Odor identification in various applications, ranging from industrial safety to healthcare diagnostics. Simulation results demonstrate the circuit’s ability to improve

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38

Berna, Amalia Z., Alisha R. Anderson, and Stephen C. Trowell. "Bio-Benchmarking of Electronic Nose Sensors." PLoS ONE 4, no. 7 (2009): e6406. http://dx.doi.org/10.1371/journal.pone.0006406.

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39

Abdullah, A. H., A. H. Adom, A. Y. Md. Shakaff, et al. "Electronic Nose System for Ganoderma Detection." Sensor Letters 9, no. 1 (2011): 353–58. http://dx.doi.org/10.1166/sl.2011.1479.

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40

Haugen, John-Erik, and Knut Kvaal. "Electronic nose and artificial neural network." Meat Science 49 (January 1998): S273—S286. http://dx.doi.org/10.1016/s0309-1740(98)90054-7.

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41

STINSON, STEPHEN. "Electronic nose moves closer to reality." Chemical & Engineering News 69, no. 12 (1991): 23–24. http://dx.doi.org/10.1021/cen-v069n012.p023.

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42

T. H. Dodd, S. A. Hale, and S. M. Blanchard. "ELECTRONIC NOSE ANALYSIS OF TILAPIA STORAGE." Transactions of the ASAE 47, no. 1 (2004): 135–40. http://dx.doi.org/10.13031/2013.15840.

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43

Falasconi, M., M. Pardo, M. Vezzoli, and G. Sberveglieri. "Cluster validation for electronic nose data." Sensors and Actuators B: Chemical 125, no. 2 (2007): 596–606. http://dx.doi.org/10.1016/j.snb.2007.03.004.

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44

Lozano, J., T. Arroyo, J. P. Santos, J. M. Cabellos, and M. C. Horrillo. "Electronic nose for wine ageing detection." Sensors and Actuators B: Chemical 133, no. 1 (2008): 180–86. http://dx.doi.org/10.1016/j.snb.2008.02.011.

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45

Dutta, Ritaban, K. R. Kashwan, M. Bhuyan, E. L. Hines, and J. W. Gardner. "Electronic nose based tea quality standardization." Neural Networks 16, no. 5-6 (2003): 847–53. http://dx.doi.org/10.1016/s0893-6080(03)00092-3.

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46

Shurmer, H. V. "Basic limitations for an electronic nose." Sensors and Actuators B: Chemical 1, no. 1-6 (1990): 48–53. http://dx.doi.org/10.1016/0925-4005(90)80170-5.

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47

Shurmer, Harold V., and Julian W. Gardner. "Odour discrimination with an electronic nose." Sensors and Actuators B: Chemical 8, no. 1 (1992): 1–11. http://dx.doi.org/10.1016/0925-4005(92)85001-d.

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48

Voss, Andreas, Vico Baier, Renate Reisch, et al. "Smelling Renal Dysfunction via Electronic Nose." Annals of Biomedical Engineering 33, no. 5 (2005): 656–60. http://dx.doi.org/10.1007/s10439-005-1438-2.

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49

Young, Rebecca C., William J. Buttner, Bruce R. Linnell, and Rajeshuni Ramesham. "Electronic nose for space program applications." Sensors and Actuators B: Chemical 93, no. 1-3 (2003): 7–16. http://dx.doi.org/10.1016/s0925-4005(03)00338-1.

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50

Di Natale, C., A. Macagnano, F. Davide, et al. "An electronic nose for food analysis." Sensors and Actuators B: Chemical 44, no. 1-3 (1997): 521–26. http://dx.doi.org/10.1016/s0925-4005(97)00175-5.

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