A recent paper published describes the development of low-cost E-nose:

Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality.

Claudia Gonzalez Viejo; Sigfredo Fuentes; Amruta Godbole; Bryce Widdicombe and Ranjith R Unnithan.

CLICK HERE to see the paper published in Sensors and Actuators B Chemical

A new paper published by the Digital Agriculture, Food and Wine research group describes the development of a low-cost E-nose based on nine gas sensors and integrated temperature and relative humidity sensors.

This E-nose has been tested in a smoke contamination trial in Adelaide to detect smoke-related compounds in grapes and wines using Machine Learning and Artificial Intelligence.

This E-nose can be integrated with IoT and a computer application (app) to monitor in real-time smoke contamination and risk of smoke taint considering weather variables, phenological stage of grapevines, the susceptibility of different cultivars and intensity of smoke contamination. Information collected by the system will be processed using machine learning and AI algorithms to produce decision-making tools for winegrowers to assess the risk of contamination and levels of contaminants in berries and final wine.

Contact: A. Prof. Sigfredo Fuentes; The University of Melbourne. E-mail: sfuentes@unimelb.edu.au