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Optimized Svr With Nature-inspired Algorithms For Environmental Modelling Of Mycotoxins In Food Virtual-water Samples.

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Optimized Svr With Nature-inspired Algorithms For Environmental Modelling Of Mycotoxins In Food Virtual-water Samples.

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Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water

Abdullahi G Usman^1,2, Sagiru Mati^3,4, Hanita Daud⁵

¹Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, 99138, Nicosia, Turkish Republic of Northern Cyprus. abdullahigusman@gmail.com.

Scientific Reports

|May 13, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a machine learning model combining Support Vector Regression with Harris Hawks Optimization (SVR-HHO) for accurate mycotoxin prediction in food. This advanced method enhances food safety and regulatory compliance.

Keywords:

Food virtual-water samples Harris-Hawks Optimization Machine learning Mycotoxins Retention time

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Area of Science:

Analytical Chemistry
Computational Chemistry
Food Science

Background:

Accurate mycotoxin determination is vital for food safety and preventing toxic effects in humans and animals.
Existing methods for mycotoxin analysis can be time-consuming and require complex sample preparation.

Purpose of the Study:

To develop and validate a predictive model for chromatographic retention time of food mycotoxins.
To enhance the accuracy and efficiency of mycotoxin identification using machine learning and metaheuristic optimization.

Main Methods:

Utilized a Support Vector Regression (SVR) model optimized with Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO).
Trained and validated SVR-HHO and SVR-PSO models using a dataset from secondary sources.
Assessed model performance using mean square error, correlation coefficient, and Nash-Sutcliffe efficiency.

Main Results:

The SVR-HHO model demonstrated superior performance, outperforming existing methods by 4-7% in both training and testing phases.
Metaheuristic optimization effectively improved parameter adjustment, avoided local minima, and enhanced model generalization.
The SVR-HHO framework showed high accuracy and resilience in predicting mycotoxin retention times, managing complex nonlinear interactions.

Conclusions:

Combining machine learning (SVR) with metaheuristics (HHO) offers a powerful tool for accurate mycotoxin forecasting.
The SVR-HHO model provides a robust framework for commercial quality assurance, regulatory testing, and food safety programs.
This approach improves food safety by enabling accurate mycotoxin identification and reducing health risks for consumers and animals.