Research on Construction of Electronic Tongue Systems Combining Nanomaterials and Machine Learning Algorithms for COD Determination and Anxiolytic Herbal Medicinal Products Analysis

Abstract

Owing to advantages, low cost, high sensitivity and selectivity, rapidness, easy operation and transportability, electrochemical sensors and biosensors involving nanomaterials have been accepted as an effective and promising analytical method for environment monitoring, pharmaceutical analysis, medical diagnosis, food safety etc. , over the past decades. However, this difficulty of improving performance of traditional sensors, like selectivity and sensitivity, led to the concept of electronic tongues (ETs) as an alternative strategy to solve such problems. These biomimetic systems, composed of low selectivity sensor arrays, can acquire added value from the generation of the analytical information containing cross-response features. In this thesis, three voltammetric ET arrays were constructed composed of copper-based electrodes modified with CuO nanoparticles in an electrodeposition procedure and some graphite-epoxy composite (GEC) electrodes incorporating several catalytic nanoparticles. The first ET demonstrated the potential of evaluating wastewaters according to the difficulty of electrochemical oxidation and helped to estimate the accuracy of found COD values detected by prepared sensor. The second ET employed an artificial neural network (ANN) model by combining the responses obtained from five sensors and compensating the differences in the voltammetric responses to two COD standard substances, glucose and glycine. This ET showed promising application of COD determination with better performance compared with single sensor utilizing classic calibration curve method. The third ET array, combined with principal component analysis (PCA), was able to evaluate the components of herb varieties for mental calming products in terms of contents and different forms, which indicated its potential function on product evaluation, characterization and quality control.

Date of Award	15 Dec 2023
Original language	English
Supervisor	Manel del Valle (Director)