# Virtual Electronic Tongue Combining Electrochemical Impedance Spectroscopy and the Artificial Neural Network for Accurate Identification of Noncompliant Gasoline

## TL;DR

A virtual electronic tongue using EIS and ANNs accurately identifies and quantifies noncompliant gasoline adulterants.

## Contribution

A novel virtual electronic tongue combining EIS and ANNs for reliable identification of gasoline adulterants.

## Key findings

- ANN models using Z″ showed no misclassifications in adulterant type identification.
- Regression performance reached R² values of 0.846 for n-hexane and 0.965 for toluene in mixed systems.
- Focusing on the semicircle domain in Nyquist plots improved feature informativeness for adulteration detection.

## Abstract

Noncompliant gasoline compromises engine performance,
durability,
and emissions. In this study, a virtual electronic tongue combining
electrochemical impedance spectroscopy (EIS) and artificial neural
networks (ANNs) was applied to identify and quantify common gasoline
adulterants, namely, n-hexane, toluene, and mineral
turpentine, in single- and multiadulterant systems. Measurements were
performed using a glassy carbon electrode with platinum counter and
pseudoreference electrodes. Single-adulterant systems exhibited increasing
Nyquist semicircle diameters in the order n-hexane
< mineral turpentine < toluene, while binary and ternary mixtures
showed nonmonotonic impedance behavior, reflecting concentration-dependent
intermolecular interactions. ANN models trained with the imaginary
impedance component (Z″) demonstrated improved
performance when restricted to the semicircle region of the Nyquist
plots. This approach resulted in no misclassifications in the test
set for adulterant type and enhanced regression performance for individual
adulterants, even in mixed systems (test-set R
2 = 0.846 for n-hexane, 0.965 for toluene,
and 0.742 for mineral turpentine). These results show that focusing
on the semicircle domain concentrates the most informative impedance
features and enables reliable identification and quantification of
gasoline adulteration while also revealing the inherent complexity
of multiadulterant fuel systems.