Work

M2.3.4 - 3D-Printed µ-GC Integrated with an E-Nose System for Enhanced Plant Health Prediction

Year: 2025

Type: conference-paper

Abstract: This work integrates a low-cost 3D-printed µ-GC with an e-nose system f or improved plant health prediction.Metal oxide sensors are placed bef ore and af ter the µ-GC column and show that ethanol elutes f irst, f ollowed by trans-2-hexenal with a 1 s and linalool with a 2.5 s delays; demonstrating the column's ability to separate out these pest plant biomarkers.Features are extracted f rom the sensor time-dependent responses and analysed using partial least squares (PLS) method.They accurately predict the VOC type and concentration.This simple e-nose system may of f er a cost-ef f ective solution f or real-time plant health monitoring with enhanced prediction capabilities of harmf ul pests.

Source: Lectures

DOI: 10.5162/eurosensors2025/m2.3.4

Authors: U. Yaqoob, D. Limbani, S. Esfahani, M. Cole, J. W. Gardner

Institution: University of Warwick

Language: English

Topic: Advanced Chemical Sensor Technologies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: diamond

Funders: HORIZON EUROPE Framework Programme, Research Executive Agency