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The dataset for pork adulteration from electronic nose system
- Citation Author(s):
- Submitted by:
- shoffi izza
- Last updated:
- Tue, 05/17/2022 - 22:17
- DOI:
- 10.21227/txmn-eg92
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Abstract
E-nose can be used for food authentication and adulteration assessment. Recently, halal authentication has gained attention because of cases of pork adulteration in beef. In this study, The electronic nose was built using nine MQ series gas sensors from Zhengzhou Winsen Electronics Technology Co., Ltd for detection pork adulteration in beef. The list of gas sensors are MQ2, MQ4, MQ6, MQ9, MQ135, MQ136, MQ137, and MQ138. These gas sensors were assembled with an Arduino microcontroller. For data communication, a universal serial bus (USB) interface was used to transfer the signals from the microcontroller to the computer. The gas sensors were placed in a sample chamber made of transparent glass.
The samples used were ground beef and ground pork bought in fresh condition from the same store on the same date. In the experiment, samples of seven combinations of beef and pork were used. Both ground beef and pork were used in samples with a weight of 100 gr with various compositions. The samples divided into 7 combination mixtures, which are the first and seventh combinations were 100 gr beef and 100 gr pork, respectively. The second, third, fourth, fifth, and sixth combinations contained 10 gr, 25 gr, 50 gr, 75 gr, and 90 gr of beef from a total sample of 100 gr, respectively. A scale was used to ensure that the weight of the mixture was appropriate. The following steps were used to collect the data samples:
1. the e-nose was turned on and the sensors were warmed up for 15 minutes;
2. the sample was placed in the sample chamber with the gas sensors;
3. the processes of data retrieval and transfer to the computer using the USB interface took 15 to 20 minutes for each sample;
4. the sample chamber was cleaned using a flashing fan for 5 minutes after every sampling so the next sampling was not affected by gas residue from the previous sampling.
There are 60 data for each combination, so the total number of recorded data was 420 for seven combinations. Each data had 10 digital outputs that saved in .csv format with filename according to the seven combinations that have been built, which are S000 for the first combination, S010 for the second combination, S025, S050, S075, S090, dan S100. The columns are filled in the order of the signal output, the first column for the MQ2 sensor output, the second column for the MQ4 sensor output, MQ6, MQ9, MQ135, MQ136, MQ137, and MQ138, temperature and, humidity, respectively. Each file had 60 line digital outputs.
