Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea

Palit, Mousumi; Tudu, Bipan; Bhattacharyya, Nabarun; Dutta, Ankur; Dutta, Paramartha; Jana, Arun; Bandyopadhyay, Rajib; Chatterjee, Amit

doi:10.1016/j.aca.2010.06.036

Cited by 63 publications

(31 citation statements)

References 16 publications

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“…For this reason, regardless of the methods used either at the modelling or at the signal compression stages, a data-centering and/or normalization step should be carried out before constructing the model. To this aim, some of the strategies that can be used are: centering, standardization, normalization or auto-scaling of the data (Bro and Smilde 2003;Palit et al 2010;Scott et al 2006). …”

Section: Weightingmentioning

confidence: 99%

Bioelectronic tongues: New trends and applications in water and food analysis

Cetó

Voelcker

Prieto‐Simón

2016

Biosensors and Bioelectronics

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Section: Weightingmentioning

confidence: 99%

Bioelectronic tongues: New trends and applications in water and food analysis

Cetó

Voelcker

Prieto‐Simón

2016

Biosensors and Bioelectronics

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“…Thus, the values of current from the whole range of voltage excitation are treated as features for the classifiers. They are frequently pre-processed using such methods as normalization or auto-scaling [17]. The pre-processing often improves the classification results [18].…”

Section: Discussionmentioning

confidence: 99%

“…However, there is still no rigid guideline which pre-processing technique is the best to maximize the classification efficiency [17]. Here, two pre-processing methods are applied, i.e.…”

Section: Data Pre-processingmentioning

confidence: 99%

Efficiency of Linear and Non-Linear Classifiers for Gas Identification from Electrocatalytic Gas Sensor

Kalinowski¹,

Woźniak²,

Strzelczyk³

et al. 2013

Metrology and Measurement Systems

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Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling.

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“…Wide applications, such as honey identification [3], rice discrimination [4], and beverage classification [5,6], have been a concern in recent years. In beverage classification, scholars mainly focus their attention on the substances with specific aromatic flavors such as tea and liquor [7,8] since the e-tongue identifications are more objective and reproducible than human judgments [9].…”

Section: Introductionmentioning

confidence: 99%

An Active Feature Selection Strategy for DWT in Artificial Taste

Liu

Chen

et al. 2018

Journal of Sensors

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A discrete wavelet transform (DWT) extracts meaningful information in a time-frequency domain and is a favorable feature extraction approach from pulse-like responses in large pulse voltammetry (LAPV) electronic tongues (e-tongue). A regular DWT generates lots of coefficients to describe signal details and approximations at different scales. Thus, coefficient selection is necessary to reduce the feature size. However, the common DWT-based feature selection follows a passive mode: manipulation through human experience or exhaustive trials. It is subjective, time consuming, and barely works in nonlaboratory conditions. In this paper, we present an active feature selection strategy consisting of a dispersion ratio computation and optimal searching search. To evaluate the performance of the proposed method, we prepared several beverage samples and performed experiments with a LAPV e-tongue. Meanwhile, the features of raw response, peak-inflection point, referenced DWT method, and our proposed method were presented to indicate the effects of the refined features of the proposed method. Furthermore, we utilized several classifiers such as the k-nearest neighbor (k-NN), support vector machine (SVM), and random forest (RF) to evaluate the improvement of recognition by the refined features. Compared with other regular feature extraction methods, the proposed method can automatically explore high-quality features with an acceptable feature size. Moreover, the highest average accuracy was achieved by the proposed method for each classifier. It is an alternative feature extraction approach for a LAPV e-tongue without any manipulation in real applications.

show abstract

Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea

Cited by 63 publications

References 16 publications

Bioelectronic tongues: New trends and applications in water and food analysis

Bioelectronic tongues: New trends and applications in water and food analysis

Efficiency of Linear and Non-Linear Classifiers for Gas Identification from Electrocatalytic Gas Sensor

An Active Feature Selection Strategy for DWT in Artificial Taste

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