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Portable Sensor Array for On-Site Detection and Discrimination of Pesticides and Herbicides Using Multivariate
Ranbir1, Gagandeep Singh2, Harupjit Singh2
1Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
Analytical Chemistry
|September 19, 2023
Summary
A novel azodye-based sensor array detects and distinguishes pesticides and herbicides in food and soil. Machine learning algorithms enable sensitive and selective identification, crucial for environmental and public health protection.
Area of Science:
- Analytical Chemistry
- Environmental Science
- Food Safety
Background:
- Pesticide and herbicide residues in food and soil pose significant environmental and public health risks.
- Current detection methods may lack the sensitivity or selectivity required for complex sample matrices.
- Need for rapid, reliable analytical tools to monitor agricultural chemical contamination.
Purpose of the Study:
- To develop an azodye-based chromogenic sensor array for detecting and discriminating pesticides and herbicides.
- To utilize machine learning for processing sensor array data and enabling automated analysis.
- To assess the sensor array's performance in qualitative and quantitative determination of target analytes in food and soil samples.
Main Methods:
- Fabrication of an azodye-based chromogenic sensor array incorporating various metal ions.
- Utilizing multivariate analysis techniques: Hierarchical Clustering Analysis (HCA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Partial Least Square Regression (PLSR).
- Testing the sensor array with pesticide (chlorpyrifos) and herbicide (glyphosate) standards and mixtures.
Main Results:
- The sensor array exhibited distinct photophysical responses to various pesticides and herbicides.
- Machine learning algorithms successfully processed sensor data for selective and sensitive analyte identification.
- High linear correlation (R²: 0.89–0.96) and sensitive detection limits (5.3–11.8 ppm) were achieved for quantitative analysis.
- Successful discrimination of a binary mixture of chlorpyrifos and glyphosate was demonstrated.
Conclusions:
- The developed azodye-based sensor array coupled with machine learning offers a promising approach for pesticide and herbicide monitoring.
- This method provides a sensitive, selective, and automated platform for detecting and discriminating agrochemical residues in environmental and food samples.
- The technology has significant implications for safeguarding environmental quality and public health.
