Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue Signals

Pradip Saha; Santanu Ghorai; Bipan Tudu; Rajib Bandyopadhyay; Nabarun Bhattacharyya

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Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue Signals

机译：电子舌信号的自回归建模预测茶叶质量

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摘要

In this paper, a novel method to model the responses of electronic tongue (ET) sensors using autoregressive (AR) and AR moving average techniques is presented. The transient response of each electrode present in the sensor array of an ET is characterized with tea samples of different qualities. Models coefficients are used as the characteristics features of the ET response corresponding to the tea samples. Three different classifiers, namely, artificial neural network, vector valued regularized kernel function approximation, and one-versus-one support vector machine, are employed to evaluate the performance of these features to discriminate the quality of black tea. Experimental results on three types of voltammetric measurement data show that the proposed method may be very useful for prediction of tea quality. The present model-based classification method is very straightforward and provides better or similar performance compared with some other methods proposed in the literature for ET signal classification.

机译：在本文中，提出了一种使用自回归（AR）和AR移动平均技术对电子舌（ET）传感器的响应进行建模的新方法。 ET传感器阵列中存在的每个电极的瞬态响应都用不同质量的茶样品来表征。模型系数用作与茶样品相对应的ET响应的特征。三种不同的分类器分别是人工神经网络，向量值正则核函数逼近和一对多支持向量机，用于评估这些特征的性能以区分红茶的质量。对三种伏安测量数据的实验结果表明，该方法对于茶质量的预测可能非常有用。与文献中提出的用于ET信号分类的其他方法相比，本基于模型的分类方法非常简单，并且提供了更好或相似的性能。

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来源
《IEEE sensors journal》 |2016年第11期|4470-4477|共8页
作者
Pradip Saha; Santanu Ghorai; Bipan Tudu; Rajib Bandyopadhyay; Nabarun Bhattacharyya;
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作者单位

Department of Applied Electronics and Instrumentation Engineering, Heritage Institute of Technology, Kolkata, India;

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原文格式 PDF
正文语种 eng
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关键词
Electronic tongue (ET); OVO-SVM; autoregressive (AR) model; autoregressive moving average (ARMA) model; feature extraction;

机译：电子舌（ET）;OVO-SVM;自回归（AR）模型;自回归移动平均（ARMA）模型;特征提取;

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专利

1. Tea Quality Prediction by Sparse Modeling of Electronic Tongue Signals [J] . Pradip Saha, Santanu Ghorai, Bipan Tudu, IEEE Transactions on Instrumentation and Measurement . 2019,第9期

机译：电子舌信号的稀疏建模预测茶叶质量
2. A Novel Technique of Black Tea Quality Prediction Using Electronic Tongue Signals [J] . Saha P., Ghorai S., Tudu B., Instrumentation and Measurement, IEEE Transactions on . 2014,第10期

机译：电子舌信号预测红茶质量的新技术
3. A Tool for General Quality Assessment of Black Tea—Retail Price Prediction by an Electronic Tongue [J] . Maria Khaydukova, Xavier Cetó, Dmitry Kirsanov, Food Analytical Methods . 2015,第5期

机译：红茶一般质量评估工具—电子舌头的零售价格预测
4. WHT-based tea quality prediction using electronic tongue signals [C] . P. Saha, S. Ghorai, B. Tudu, International Conference on Advancement of Computer Communication and Electrical Technology . 2017

机译：基于WHT的茶叶质量预测，使用电子舌信号
5. Design of a recognition system for monitoring the depth of anesthesia, based on the autoregressive modeling and neural network analysis of the EEG signals. [D] . Sharma, Ashutosh. 1993

机译：基于EEG信号的自回归建模和神经网络分析，设计用于监测麻醉深度的识别系统。
6. A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment [O] . Ruicong Zhi, Lei Zhao, Dezheng Zhang 2017

机译：电子鼻和电子舌多层次融合的茶质量评估框架
7. A tool for general quality assessment of black tea : retail price prediction by an electronic tongue [O] . Khaydukova Maria 2015

机译：红茶一般质量评估的工具：电子舌头的零售价格预测

Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue Signals

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