Open AccessIntegration of a low‐cost electronic nose and a voltammetric electronic tongue for red wines identification
Author(s) -
Han Fangkai,
Zhang Dongjing,
Aheto Joshua H.,
Feng Fan,
Duan Tengfei
Publication year - 2020
Publication title -
food science and nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.614
H-Index - 27
ISSN - 2048-7177
DOI - 10.1002/fsn3.1730
Subject(s) - electronic tongue , electronic nose , tongue , artificial intelligence , wine , computer science , principal component analysis , materials science , chemistry , taste , food science , medicine , pathology
The purpose of this present study was to develop a rapid and effective approach for identification of red wines that differ in geographical origins, brands, and grape varieties, a multi‐sensor fusion technology based on a novel cost‐effective electronic nose (E‐nose) and a voltammetric electronic tongue (E‐tongue) was proposed. The E‐nose sensors was created using porphyrins or metalloporphyrins, pH indicators and Nile red printed on a C2 reverse phase silica gel plate. The voltammetric E‐Tongue with six metallic working electrodes, namely platinum, gold, palladium, tungsten, titanium, and silver was employed to sense the taste of red wines. Principal component analysis (PCA) was utilized for dimensionality reduction and decorrelation of the raw sensors datasets. The fusion models derived from extreme learning machine (ELM) were built with PCA scores of E‐nose and tongue as the inputs. Results showed superior performance (100% recognition rate) using combination of odor and taste sensors than individual artificial systems. The results suggested that fusion of the novel cost‐effective E‐nose created and voltammetric E‐tongue coupled with ELM has a powerful potential in rapid quality evaluation of red wine.