Fabrication and optimization of a conducting polymer sensor array using stored grain model volatiles

Area of Science:

• Agricultural Science
• Materials Science
• Analytical Chemistry

Background:

• Stored grain quality degrades due to physical, chemical, and biological factors, primarily insects and fungi.
• Effective intervention requires continuous monitoring and differentiation of spoilage sources to minimize losses.
• Existing sensor technologies offer potential for novel applications in agricultural monitoring.

Purpose of the Study:

• To develop a straightforward, cost-effective sensor array for monitoring stored grain.
• To fabricate and optimize a carbon black (CB) polymer sensor array for detecting insect and fungal volatiles.
• To differentiate between key spoilage-related compound classes in stored grain.

Main Methods:

• Fabrication and optimization of a carbon black (CB) polymer sensor array.
• Utilized statistical analyses including PCA and LDA for polymer selection.
• Tested sensor array performance with model volatiles associated with insect (benzene derivatives) and fungal (aliphatic hydrocarbon derivatives) spoilage.
• Minimized sensitivity to environmental factors like humidity.

Main Results:

• The developed CB polymer sensor array successfully identified and separated model volatiles associated with stored grain spoilage.
• Statistical methods effectively selected polymers for optimal discrimination of compound classes (quinones, alcohols).
• The sensor array demonstrated satisfactory performance under ambient conditions.

Conclusions:

• A novel CB polymer sensor array shows promise for monitoring stored grain quality.
• This technology can differentiate between insect and fungal spoilage indicators.
• The sensor array offers a potential solution for cost-effective and efficient stored grain monitoring.