- -

Development of the MOOSY4 eNose IoT for Sulphur-Based VOC Water Pollution Detection

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Development of the MOOSY4 eNose IoT for Sulphur-Based VOC Water Pollution Detection

Show full item record

Climent-Martí, E.; Pelegrí Sebastiá, J.; Sogorb Devesa, T.; Talens-Felis, J.; Chilo, J. (2017). Development of the MOOSY4 eNose IoT for Sulphur-Based VOC Water Pollution Detection. Sensors. 17(8):1-10. https://doi.org/10.3390/s17081917

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/98785

Files in this item

Item Metadata

Title: Development of the MOOSY4 eNose IoT for Sulphur-Based VOC Water Pollution Detection
Author: Climent-Martí, Enric Pelegrí Sebastiá, José Sogorb Devesa, Tomás Talens-Felis, Juan-Bta Chilo, José
UPV Unit: Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres
Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
Issued date:
Abstract:
[EN] In this paper, we describe a new low-cost and portable electronic nose instrument, the Multisensory Odor Olfactory System MOOSY4. This prototype is based on only four metal oxide semiconductor (MOS) gas sensors suitable ...[+]
Subjects: ANN , MOOSY4 , WEKA , Electronic nose , Embedded , Water quality
Copyrigths: Reconocimiento (by)
Source:
Sensors. (eissn: 1424-8220 )
DOI: 10.3390/s17081917
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/s17081917
Project ID:
info:eu-repo/grantAgreement/GVA//AICO%2F2016%2F046/
Thanks:
This work was supported by the I+D+i Program of the Generalitat Valenciana, Spain [AICO/2016/046], and the II Program UPV-La Fe [2013/0504].
Type: Artículo

References

Babovic, Z. B., Protic, J., & Milutinovic, V. (2016). Web Performance Evaluation for Internet of Things Applications. IEEE Access, 4, 6974-6992. doi:10.1109/access.2016.2615181

Getting Startedhttps://docs.smartcitizen.me/#/start/detailed-specifications

Xu, L. D., He, W., & Li, S. (2014). Internet of Things in Industries: A Survey. IEEE Transactions on Industrial Informatics, 10(4), 2233-2243. doi:10.1109/tii.2014.2300753 [+]
Babovic, Z. B., Protic, J., & Milutinovic, V. (2016). Web Performance Evaluation for Internet of Things Applications. IEEE Access, 4, 6974-6992. doi:10.1109/access.2016.2615181

Getting Startedhttps://docs.smartcitizen.me/#/start/detailed-specifications

Xu, L. D., He, W., & Li, S. (2014). Internet of Things in Industries: A Survey. IEEE Transactions on Industrial Informatics, 10(4), 2233-2243. doi:10.1109/tii.2014.2300753

Huang, J., Meng, Y., Gong, X., Liu, Y., & Duan, Q. (2014). A Novel Deployment Scheme for Green Internet of Things. IEEE Internet of Things Journal, 1(2), 196-205. doi:10.1109/jiot.2014.2301819

Gardner, J. W., & Bartlett, P. N. (1994). A brief history of electronic noses. Sensors and Actuators B: Chemical, 18(1-3), 210-211. doi:10.1016/0925-4005(94)87085-3

Gardner, J. W., & Bartlett, P. N. (1996). Performance definition and standardization of electronic noses. Sensors and Actuators B: Chemical, 33(1-3), 60-67. doi:10.1016/0925-4005(96)01819-9

Wilson, A., & Baietto, M. (2009). Applications and Advances in Electronic-Nose Technologies. Sensors, 9(7), 5099-5148. doi:10.3390/s90705099

Jia, X.-M., Meng, Q.-H., Jing, Y.-Q., Qi, P.-F., Zeng, M., & Ma, S.-G. (2016). A New Method Combining KECA-LDA With ELM for Classification of Chinese Liquors Using Electronic Nose. IEEE Sensors Journal, 16(22), 8010-8017. doi:10.1109/jsen.2016.2606163

Jing, Y.-Q., Meng, Q.-H., Qi, P.-F., Cao, M.-L., Zeng, M., & Ma, S.-G. (2016). A Bioinspired Neural Network for Data Processing in an Electronic Nose. IEEE Transactions on Instrumentation and Measurement, 65(10), 2369-2380. doi:10.1109/tim.2016.2578618

Fine, G. F., Cavanagh, L. M., Afonja, A., & Binions, R. (2010). Metal Oxide Semi-Conductor Gas Sensors in Environmental Monitoring. Sensors, 10(6), 5469-5502. doi:10.3390/s100605469

Santra, S., Guha, P. K., Ali, S. Z., Hiralal, P., Unalan, H. E., Covington, J. A., … Udrea, F. (2010). ZnO nanowires grown on SOI CMOS substrate for ethanol sensing. Sensors and Actuators B: Chemical, 146(2), 559-565. doi:10.1016/j.snb.2010.01.009

Wilson, A. (2013). Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry. Sensors, 13(2), 2295-2348. doi:10.3390/s130202295

Lorwongtragool, P., Sowade, E., Watthanawisuth, N., Baumann, R., & Kerdcharoen, T. (2014). A Novel Wearable Electronic Nose for Healthcare Based on Flexible Printed Chemical Sensor Array. Sensors, 14(10), 19700-19712. doi:10.3390/s141019700

Son, M., Cho, D., Lim, J. H., Park, J., Hong, S., Ko, H. J., & Park, T. H. (2015). Real-time monitoring of geosmin and 2-methylisoborneol, representative odor compounds in water pollution using bioelectronic nose with human-like performance. Biosensors and Bioelectronics, 74, 199-206. doi:10.1016/j.bios.2015.06.053

Gardner, J. W., Shin, H. W., Hines, E. L., & Dow, C. S. (2000). An electronic nose system for monitoring the quality of potable water. Sensors and Actuators B: Chemical, 69(3), 336-341. doi:10.1016/s0925-4005(00)00482-2

Goschnick, J., Koronczi, I., Frietsch, M., & Kiselev, I. (2005). Water pollution recognition with the electronic nose KAMINA. Sensors and Actuators B: Chemical, 106(1), 182-186. doi:10.1016/j.snb.2004.05.055

Guadayol, M., Cortina, M., Guadayol, J. M., & Caixach, J. (2016). Determination of dimethyl selenide and dimethyl sulphide compounds causing off-flavours in bottled mineral waters. Water Research, 92, 149-155. doi:10.1016/j.watres.2016.01.016

Wilson, A. D. (2012). Review of Electronic-nose Technologies and Algorithms to Detect Hazardous Chemicals in the Environment. Procedia Technology, 1, 453-463. doi:10.1016/j.protcy.2012.02.101

Becher, C., Kaul, P., Mitrovics, J., & Warmer, J. (2010). The detection of evaporating hazardous material released from moving sources using a gas sensor network. Sensors and Actuators B: Chemical, 146(2), 513-520. doi:10.1016/j.snb.2009.12.030

Berrueta, L. A., Alonso-Salces, R. M., & Héberger, K. (2007). Supervised pattern recognition in food analysis. Journal of Chromatography A, 1158(1-2), 196-214. doi:10.1016/j.chroma.2007.05.024

Lajara, R. J., Perez-Solano, J. J., & Pelegri-Sebastia, J. (2015). A Method for Modeling the Battery State of Charge in Wireless Sensor Networks. IEEE Sensors Journal, 15(2), 1186-1197. doi:10.1109/jsen.2014.2361151

Batista, B. L., da Silva, L. R. S., Rocha, B. A., Rodrigues, J. L., Berretta-Silva, A. A., Bonates, T. O., … Barbosa, F. (2012). Multi-element determination in Brazilian honey samples by inductively coupled plasma mass spectrometry and estimation of geographic origin with data mining techniques. Food Research International, 49(1), 209-215. doi:10.1016/j.foodres.2012.07.015

Benedetti, S., Mannino, S., Sabatini, A. G., & Marcazzan, G. L. (2004). Electronic nose and neural network use for the classification of honey. Apidologie, 35(4), 397-402. doi:10.1051/apido:2004025

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record