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A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues.

Ștefan-Mihai Petrea1, Mioara Costache2, Dragoș Cristea3

  • 1Department of Foood Science, Food Engineering, Biotechnology and Aquaculture, Faculty of Food Science and Engineering, University "Dunărea de Jos" of Galați, 800008 Galați, Romania.

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|October 17, 2020
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Summary

Machine learning models effectively estimate heavy metal concentrations in turbot (Psetta maxima maeotica) tissues. This approach aids in assessing aquatic pollution and ensuring food safety.

Keywords:
heavy metalsmachine learningprediction modelsrandom forestturbot

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Marine Biology

Background:

  • Metals pose significant environmental hazards due to bioaccumulation and persistence.
  • Demersal fish, like turbot (Psetta maxima maeotica), serve as crucial bioindicators for aquatic heavy metal pollution.
  • Accurate monitoring of heavy metals in fish tissues is vital for food safety and environmental health assessments.

Purpose of the Study:

  • To develop and evaluate machine learning models for estimating heavy metal concentrations in turbot muscle and liver.
  • To compare the efficacy of multiple linear regression (MLR) and random forest (RF) algorithms in predicting metal levels.
  • To assess the suitability of these models for environmental pollution and food safety studies.

Main Methods:

  • Utilized a machine learning approach incorporating multiple linear regression (MLR) and random forest (RF) algorithms.
  • Data sourced from scientific literature, covering 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn).
  • Models were trained and validated using heavy metal concentrations from turbot muscle and liver tissues.

Main Results:

  • Significant MLR models were identified for Ca, Fe, Mg, Na (muscle) and K, Cu, Zn, Na (liver).
  • RF models achieved over 70% prediction accuracy for As, Cd, Cu, K, Mg, Zn (muscle) and As, Ca, Cd, Mg, Fe (liver).
  • Both MLR and RF models demonstrated suitability for predicting heavy metal concentrations in turbot tissues.

Conclusions:

  • Machine learning, specifically MLR and RF, offers a robust method for estimating heavy metal levels in turbot.
  • These predictive models can enhance the efficiency and knowledge base for heavy metal food safety and pollution studies.
  • The study supports the use of turbot as bioindicators and highlights the utility of advanced computational methods in environmental monitoring.