electronic nose
The dataset has 99 rows, corresponding to 99 odor samples, their labels are shown in label.xlsx
The dataset has 450 columns, corresponding to the responses of 30 odor sensors under 15 heating voltages (2.6V-5.4V).
Taking the first row as an example, the first to 15th elements correspond to the response values of sensor No.1 at heating voltage range from 2.6 V to 5.4 V in 0.2 V increments; The 16th to 30th elements correspond to the response values of sensor No.2 at heating voltage range from 2.6 V to 5.4 V in 0.2 V increments; And so on.
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Here, we used a self-developed general purpose E-nose platform to analyze the chemical selection in calibration and measure the drift between different chemicals. The platform employs 41 gas sensors, including 37 distinct types of gas sensors and 4 environmentaldetection sensors to create a stable, automatic gas acquisition system.
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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.
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The development of electronic nose (e-nose) for a rapid, simple, and low-cost meat assessment system becomes the concern of researchers in recent years. Hence, we provide time-series datasets that were recorded from e-nose for beef quality monitoring experiment. This dataset is originated from 12 type of beef cuts including round (shank), top sirloin, tenderloin, flap meat (flank), striploin (shortloin), brisket, clod/chuck, skirt meat (plate), inside/outside, rib eye, shin, and fat.
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This dataset contains the resuts of an experiment in which an electronic nose implemented with six MOX sensors acquired samples of explosives in raw and combined states.
As for the collection of samples, a random experimentation was carried out in order to avoid that data generates any memory effect that could influence the results. Raw TNT and gunpowder data were taken in amounts of 0.1g to 2g. Soap and toothpaste were also used to be mixed with the explosives. In the end, we took samples of the explosive substances in raw and combined states.
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