[Paper] Lei Zhang, Xuehan Wang, Guangbin Huang, Tao Liu, and Xiaoheng Tan, "Taste Recognition in E-Tongue using Local Discrimiant Preservation Projection," IEEE Trans. Cybernetics, 2017. published.
[CQU E-Tongue Dataset: Introduction]
Abstract: This archive contains 114 measurements from 6 chemical sensors of E-Tongue based on multifrequency large amplitude pulse voltammetry (MLAPV) in a discrimination task of 13 liquid samples at various levels of concentrations.
Data Set Characteristics: Multivariate
Number of instances: 114
Area:Computer and Engineering
Attribute Characteristics: Real
Number of Attributes: 12300
Associated Tasks: Classification
Creators: Lei Zhang (email@example.com), College of Communication Engineering, Chongqing University,China
Data Set Information:
This archive contains 114 measurements from 6 chemical sensors of E-Tongue based on MLAPV in a discrimination task of 13 liquid samples at various levels of concentrations. The primary purpose of providing this dataset is to make it freely accessible on-line to the chemo-sensor research community and artificial intelligence to develop strategies to improve the performance of E-Tongue. The dataset can be used exclusively for research purposes. Commercial purposes are fully excluded.
The dataset was gathered in a E-Tongue platform facility situated at the College of Communication Engineering, Chongqing University.
The resulting dataset comprises recordings from 13 distinct liquid substances, including beer, red wine, white spirit, black tea, Mao Feng tea, Pu’er tea, oolong tea, coffee, milk, cola, vinegar, medicine and salt, each dosed at various concentration. The numbers after sample name represents concentration and times of experiments. For example, "beer23.txt" represents the third experiment data with second concentration of beer.
The system contains 6 electrode sensors, and the response of each sensor has 2050 sampling points. Each sample includes 6×2050 data points. The electrode from the 1st row to the 6th rows in each .txt file denotes the gold electrode, platinum electrode, palladium electrode, titanium electrode, tungsten electrode and silver electrode, respectively. Note that the response of the 4th electrode is invalid, therefore, not used in current work.
Features used in this paper:
You can find the "sample.mat" file, which contains a 114×150 data matrix X and a 114×1 label vector y. The features for each sample are extracted as follows.
30 informative points are selected from 12300 attributes as features in response of each sensor and 5 electrodes are used in experiments, therefore the features of each sample is 5×30=150.
The values of label vector range from 1 to 13, where '1' denotes beer, '2' denotes coffee, '3' denotes milk, '4' denotes vinegar, '5' denotes black tea, '6' denotes Mao Feng tea, '7' denotes Pu’er tea, '8' denotes oolong tea, '9' denotes red wine, '10' denotes cola, '11' denotes medicine, '12' denotes salt and '13' denotes white spirit.
The centralization is used for scale normalization, which is computated as y=(x-mu)/sigma. Note that "mu" is the mean value and sigma is the standard deviation.
How to get the dataset?
Any inquiry on Datasets and Codes can be sent to: firstname.lastname@example.org; The dataset and codes are released for academic use with free download as follows.
 L. Zhang, X. Wang, G. Huang, T. Liu and X. Tan, “Taste Recognition in E-Tongue using Local Discrimiant Preservation Projection,” IEEE Transactions on Cybernetics, Accept, 2017.
 L. Zhang, Y. Liu, and P. Deng, "Odor Recognition in Multiple E-nose Systems with Cross-domain Discriminative Subspace Learning," IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 7, pp. 1679-1692, July 2017. [E-nose data and codes] [download here]