Machine Learning-Based Prediction of Polycyclic Aromatic Hydrocarbon (PAH) Levels in Smoked Fish Using WEKA: Evaluation of Smoking Parameters and Model Performance

İrem Kılınç; Hayal Boyacıoğlu; Berna Kılınç

doi:10.3390/blsf2026056001

Machine Learning-Based Prediction of Polycyclic Aromatic Hydrocarbon (PAH) Levels in Smoked Fish Using WEKA: Evaluation of Smoking Parameters and Model Performance

2026 Open access

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Authors: İrem Kılınç, Hayal Boyacıoğlu, Berna Kılınç

DOI: 10.3390/blsf2026056001

Cited by: 0

References: 8

Abstract

This study investigates the predictive modeling of total Polycyclic Aromatic Hydrocarbon (PAH) concentrations in smoked fish products based on various smoking parameters using machine learning techniques in the Waikato Environment for Knowledge Analysis (WEKA) software environment.

Fields of Study & Subjects

Data Analysis with R Air Quality Monitoring and Forecasting Smoking Behavior and Cessation

Publication details

Pages: 1–1
Publication date: 5 January 2026
Language: EN

References 8