Abstract
An electronic tongue is a device composed of a sensor array that takes advantage of the cross sensitivity property of several sensors to perform classification and quantification in liquid substances. In practice, electronic tongues generate a large amount of information that needs to be correctly analyzed, to define which interactions and features are more relevant to distinguish one substance from another. This work focuses on implementing and validating feature selection methodologies in the liquid classification process of a multifrequency large amplitude pulse voltammetric (MLAPV) electronic tongue. Multi-layer perceptron neural network (MLP NN) and support vector machine (SVM) were used as supervised machine learning classifiers. Different feature selection techniques were used, such as Variance filter, ANOVA F-value, Recursive Feature Elimination and model-based selection. Both 5-fold Cross validation and GridSearchCV were used in order to evaluate the performance of the feature selection methodology by testing various configurations and determining the best one. The methodology was validated in an imbalanced MLAPV electronic tongue dataset of 13 different liquid substances, reaching a 93.85% of classification accuracy.
Highlights
Electronic tongues are bio-inspired devices that seek to resemble the bodily sense of taste, using an array of sensors of various specifications that interact with a fluid and respond differently to each substance, allowing their identification and quantification [9,10]
This work focuses on the implementation and validation of several features selection techniques in the liquid classification process of an array of sensors type in a multifrecuency large amplitude pulse voltammetry (MLAPV) electronic tongue, on which in addition an adjustment of hyper-parameters is carried out using tools such as gridsearchCV together with 5 fold cross validation, to select the model that grants the highest possible Accuracy and allows a higher response speed later by selecting a much smaller amount of features than the initial arrangement
The materials and methods section defines the data set of a MLAPV electronic tongue used in this study as well as the methods of feature selection to process the data
Summary
Electronic tongues are bio-inspired devices that seek to resemble the bodily sense of taste, using an array of sensors of various specifications that interact with a fluid and respond differently to each substance, allowing their identification and quantification [9,10]. This work focuses on the implementation and validation of several features selection techniques in the liquid classification process of an array of sensors type in a multifrecuency large amplitude pulse voltammetry (MLAPV) electronic tongue, on which in addition an adjustment of hyper-parameters is carried out using tools such as gridsearchCV together with 5 fold cross validation, to select the model that grants the highest possible Accuracy and allows a higher response speed later by selecting a much smaller amount of features than the initial arrangement. All this using Linear SVC and MLPC as classifiers. The conclusion section outlines the principal findings of this research
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
