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Version 2

Electronic nose dataset for beef quality monitoring under an uncontrolled environment

Published:2 August 2018|Version 2|DOI:10.17632/mwmhh766fc.2
Contributors:Dedy Rahman Wijaya,
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Description

This dataset is acquired using ten Metal-Oxide Semiconductor gas sensors and humidity&temperature sensor. Extra-lean fresh beef is monitored up to 75 hours to get time series data under fluctuating ambient temperature and humidity. Hence, the first challenge is gas sensor signals that contaminated by noise. Moreover, the second challenge is overlapping selectivity of gas sensor that leads to a not optimum sensor array. This file contains 15 columns including time (minute), class, TVC, ten columns of gas sensor response, humidity, and temperature value. More detail description of this dataset can be found in data article. For utilization of this dataset, please cite the following relevant paper: [1] D.R. Wijaya, R. Sarno, E. Zulaika, Information Quality Ratio as a novel metric for mother wavelet selection, Chemometrics and Intelligent Laboratory Systems. 160 (2016) 59–71. doi:10.1016/j.chemolab.2016.11.012. [2] D.R. Wijaya, R. Sarno, E. Zulaika, Sensor Array Optimization for Mobile Electronic Nose: Wavelet Transform and Filter Based Feature Selection Approach, International Review on Computers and Software. 11 (2016) 659–671. doi:https://doi.org/10.15866/irecos.v11i8.9425. [3] D.R. Wijaya, R. Sarno, E. Zulaika, S.I. Sabila, Development of mobile electronic nose for beef quality monitoring, in: 4th Information Systems International Conference 2017, ISICO 2017, Procedia Computer Science, Elsevier B.V., Bali, 2017: pp. 728–735. doi:10.1016/j.procs.2017.12.211.

Categories

Signal Processing, Food Safety, Electronic Nose

Licence

Creative Commons Attribution 4.0 International

Version 3

Electronic nose dataset for beef quality monitoring under an uncontrolled environment

Published:17 October 2018|Version 3|DOI:10.17632/mwmhh766fc.3
Contributors:Dedy Rahman Wijaya,
,

Description

Table 1 is a list of sensors used in this experiment. The sensory classes of beef are described in Table 2 and the example of ground-truth data is shown in Fig.2. Then, Fig. 1 denotes the schematic experimental design. Fig.3 implies the example of ambient conditions in this experiment. The sample of e-nose signals is depicted in Fig.4. This dataset consists of five series correspond to five beef cuts where one series contains 2160 minutes of measurement points. Every series is distributed in comma-separated value (csv). The first row contains the column header as follows: • Minute: time of measurement point (minute); • Class: discrete label of beef quality [“excellent”, “good”, “acceptable”, “spoiled”]; • TVC: continuous label of microbial population (log10 cfu/g); • MQ_: sensor resistance value of a particular gas sensor (Ω); • Humidity: relative humidity (%) in the sample chamber; • Temperature: temperature (C) in the sample chamber. The dataset is sorted ascendingly by column “minute” to represent the time-series data in beef quality monitoring. The experimental dates for each series are as follows: • Time-series 1 (TS1.csv): May 12, 2016 to May 13, 2016 • Time-series 2 (TS2.csv): October 8, 2018 to October 9, 2018 • Time-series 3 (TS3.csv): October 11, 2018 to October 12, 2018 • Time-series 4 (TS4.csv): October 13, 2018 to October 14, 2018 • Time-series 5 (TS5.csv): October 15, 2018 to October 16, 2018.

Institutions

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Institutions

Institut Teknologi Sepuluh Nopember Fakultas Teknologi Informasi

Telkom University School of Applied Science

Categories

Signal Processing, Food Safety, Electronic Nose

Licence

Creative Commons Attribution 4.0 International