Pattern-recognizing-assisted detection of mildewed wheat by Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array

Area of Science:

• Analytical Chemistry
• Materials Science
• Food Science

Background:

• Accurate and timely detection of crop diseases like mildew is crucial for food safety and quality control.
• Traditional methods for assessing crop mildew can be time-consuming and destructive.
• Developing rapid, non-destructive methods for mildew assessment is an ongoing challenge in agricultural science.

Purpose of the Study:

• To design and validate a paper-based colorimetric sensor array for discriminating wheat with varying mildew rates.
• To establish correlations between volatile organic compounds emitted by mildewed wheat and sensor responses.
• To enable rapid, visual, and non-destructive evaluation of wheat quality based on mildew levels.

Main Methods:

• A Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array was developed.
• The sensor array captured volatile gases from wheat samples with different mildew rates.
• RGB values from the array were correlated with mildew rates, and pattern recognition (LDA) was applied.

Main Results:

• Specific array points showed strong correlations between RGB value changes (ΔG, ΔR) and mildew rates (R² up to 0.9816).
• Linear Discriminant Analysis (LDA) achieved 100% correct discrimination of wheat samples based on mildew levels.
• The sensor array effectively visualized odors associated with different mildew rates.

Conclusions:

• The Dyes/Dyes-Cu-MOF paper-based sensor array is a viable tool for rapid, visual, and non-destructive detection of wheat mildew.
• This technology offers a promising approach for real-time food safety and quality monitoring.
• Odor visualization through colorimetric sensing provides a new avenue for agricultural diagnostics.