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      Electronic nose dataset for beef quality monitoring in uncontrolled ambient conditions

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      a , b , * , a , c
      Data in Brief
      Elsevier

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          Abstract

          In recent years, the development of a rapid, simple, and low-cost meat assessment system using an electronic nose (e-nose) has been the concern of researchers. In this data article, we provide a time series dataset that was obtained from a beef quality monitoring experiment using an e-nose in uncontrolled ambient conditions. The availability of this dataset will enable discussion on how to deal with noisy e-nose signals and non-optimum sensor array in beef quality monitoring. Hence, the development of proper signal processing and robust machine learning algorithm are several challenges that must be faced. Furthermore, this dataset can also be useful as a comparison dataset for similar e-nose applications, such as air quality monitoring, smart packaging system, and food quality monitoring.

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          Most cited references6

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          Metal Oxide Gas Sensors: Sensitivity and Influencing Factors

          Conductometric semiconducting metal oxide gas sensors have been widely used and investigated in the detection of gases. Investigations have indicated that the gas sensing process is strongly related to surface reactions, so one of the important parameters of gas sensors, the sensitivity of the metal oxide based materials, will change with the factors influencing the surface reactions, such as chemical components, surface-modification and microstructures of sensing layers, temperature and humidity. In this brief review, attention will be focused on changes of sensitivity of conductometric semiconducting metal oxide gas sensors due to the five factors mentioned above.
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            Development of mobile electronic nose for beef quality monitoring

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              Information Quality Ratio as a novel metric for mother wavelet selection

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                Author and article information

                Contributors
                Journal
                Data Brief
                Data Brief
                Data in Brief
                Elsevier
                2352-3409
                22 November 2018
                December 2018
                22 November 2018
                : 21
                : 2414-2420
                Affiliations
                [a ]Informatics Department, Institut Teknologi Sepuluh Nopember, Jalan Raya ITS, Keputih, Sukolilo, 60111 Surabaya, East Java, Indonesia
                [b ]School of Applied Science, Telkom University, Jalan Telekomunikasi, Terusan Buah Batu, 40257 Bandung, West Java, Indonesia
                [c ]Biology Department, Institut Teknologi Sepuluh Nopember, Jalan Raya ITS, Keputih, Sukolilo, 60111 Surabaya, East Java, Indonesia
                Author notes
                [* ]Corresponding author at: School of Applied Science, Telkom University, Jalan Telekomunikasi, Terusan Buah Batu, 40257 Bandung, West Java, Indonesia. dedyrw@ 123456tass.telkomuniversity.ac.id
                Article
                S2352-3409(18)31484-7
                10.1016/j.dib.2018.11.091
                6282642
                30547068
                a82c2ec3-c81c-4c0e-a2d9-6543d76b7e68
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 6 August 2018
                : 14 November 2018
                : 19 November 2018
                Categories
                Computer Science

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