Development of fuzzy logic algorithm for predicting heavy metal content in poultry product

Area of Science:

• Food Science and Technology
• Computational Toxicology
• Analytical Chemistry

Background:

• Poultry products are vital global protein sources.
• Vulnerability of poultry to toxic metal contamination (copper, cadmium, lead, arsenic).
• Health risks associated with excessive heavy metal intake.

Purpose of the Study:

• To develop a fuzzy logic framework for estimating heavy metal concentrations in poultry products.
• To create a user-friendly system for rapid food safety assessment.
• To provide an accessible alternative to traditional laboratory testing.

Main Methods:

• Utilized the Mamdani fuzzy inference system and triangular membership functions in MATLAB R2021b.
• Constructed three fuzzy logic models with varying rule sets (25, 50, 81 rules).
• Integrated the fuzzy logic model with a graphical user interface (GUI).

Main Results:

• The 50-rule fuzzy logic model demonstrated optimal accuracy and minimized complexity.
• Accurate classification of poultry products as safe or unsafe based on FAO/WHO limits.
• Successful validation against published laboratory data for lead, cadmium, copper, and arsenic.

Conclusions:

• The fuzzy logic model effectively estimates heavy metal levels in poultry products.
• The GUI enables non-expert users to conduct rapid food safety assessments.
• The system offers a cost-effective and user-friendly solution for food safety monitoring and public health protection.