Machine learning models to revealing the impacts of ibuprofen on Fistulifera pelliculosa and Mesolimbus marine algae

This study intelligently analyzes the effects of ibuprofen (IBU) on the metabolism of Fistulifera pelliculosa and Mesolimbus marine algae, through the integration of machine learning (ML) and conventional analytical methods. Malondialdehyde (MDA) and Superoxide dismutase (SOD) had the highest level (0.282 nmol/L and 2.461 U/ML, respectively) for Mesolimbus under exposure to 100 mg/L IBU. The lowest OD686, Chl-a, and fucoxanthin values were 0.221, 264.89, and 9.531 mg/L respectively, at 100 mg/L IBU. The ML-integrated approach revealed a smart graphical pattern of OD686, Chl-a, and sugar, protein, lipid & fucoxanthin metabolites, highlighting IBU's impact on algae. Moreover, K-means cluster analysis grading the 0-0.1 < 1-10 < 50-200 mg/L groups of IBU influence on algal growth activity. Support vector machine (SVM) confirm > 5.0 mg/L IBU influenced the growth biomass activity, the accuracy and recall are both 100 %, and the decision boundary determines the threshold 4.8 mg/L concentration of IBU affecting Mesolimbus, which is faster and more precise compared to conventional techniques. The principal component analysis (PCA) results indicate that IBU primarily affects fucoxanthin metabolic products in the algae to inhibit algal growth, reduce chlorophyll content, and exacerbate oxidative stress in the algae, leading to increased levels of MDA and SOD. The hierarchical cluster analysis (HCA) and K-means models further confirm that IBU greater than 5.0 mg/L have a significant impact on fucoxanthin in both Fistulifera pelliculosa & Mesolimbus algae, and the effect intensified with increasing IBU concentration. ML models mitigate the conventional analyses challenges to enhancing the IBU impact assessment for environmental monitoring decision making tools.

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