Smart E-Tongue Based on Polypyrrole Sensor Array as Tool for Rapid Analysis of Coffees from Different Varieties

• Published in: Foods Vol. 13; no. 22; p. 3586
• Main Authors: Almario, Alvaro Arrieta, Calabokis, Oriana Palma, Barrera, Eisa Arrieta
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.11.2024; MDPI
• Subjects: Acids; Arrays; Beverage industry; Chemical analysis; Chemical sensors; Cluster analysis; Coffee; Coffee industry; Electrochemistry; Electrodes; Electronic tongues; Food industry; Food quality; Integrated circuits; Neural networks; Pattern recognition; polypyrrole; Polypyrroles; Portable equipment; Potassium; Principal components analysis; Quality assessment; Quality control; Reagents; sensor array; Sensor arrays; Sensors; Sensory evaluation; Sensory properties; smart e-tongue; Smartphones; Sodium; Software; Voltammetry; United States; United States > US; polypyrrole; smart e-tongue; coffee; sensor array
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods13223586

Due to the lucrative coffee market, this product is often subject to adulteration, as inferior or non-coffee materials or varieties are mixed in, negatively affecting its quality. Traditional sensory evaluations by expert tasters and chemical analysis methods, although effective, are time-consuming, costly, and require skilled personnel. The aim of this work was to evaluate the capacity of a smart electronic tongue (e-tongue) based on a polypyrrole sensor array as a tool for the rapid analysis of coffees elaborated from beans of different varieties. The smart e-tongue device was developed with a polypyrrole-based voltammetric sensor array and portable multi-potentiostat operated via smartphone. The sensor array comprised seven electrodes, each doped with distinct counterions to enhance cross-selectivity. The smart e-tongue was tested on five Arabica coffee varieties (Typica, Bourbon, Maragogype, Tabi, and Caturra). The resulting voltammetric signals were analyzed using principal component analysis assisted by neural networks (PCNN) and cluster analysis (CA), enabling clear discrimination among the coffee samples. The results demonstrate that the polypyrrole sensors can generate distinct electrochemical patterns, serving as “fingerprints” for each coffee variety. This study highlights the potential of polypyrrole-based smart e-tongues as a rapid, cost-effective, and portable alternative for coffee quality assessment and adulteration detection, with broader applications in the food and beverage industry.