Integrated Machine Learning and Statistical Interpretation of Malachite Green Removal by SDBS-Modified Ostrich Bone Waste: A Comparative Random Forest, Bayesian Regression, and LMG Analysis.

Mehdi Bahrami, Mohammad Javad Amiri, Zahra Rahsepar Chargi, Barbara Lednicka, Mohammad Gheibi

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Article in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Mehdi BahramiDepartment of Water Engineering, Faculty of Agriculture, Fasa University, Fasa 74616-86131, Iran.ORCID 0000-0002-9935-7899

Mohammad Javad AmiriDepartment of Water Engineering, Faculty of Agriculture, Fasa University, Fasa 74616-86131, Iran.ORCID 0000-0002-2633-0572

Zahra Rahsepar ChargiDepartment of Water Engineering, Faculty of Agriculture, Fasa University, Fasa 74616-86131, Iran.

Barbara LednickaDepartment of Physics, Faculty of Marine Engineering, Gdynia Maritime University, 81-225 Gdynia, Poland.ORCID 0000-0002-2718-8598

Mohammad GheibiInstitute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Studentská 1402/2, 461 17 Liberec, Czech Republic.ORCID 0000-0003-1987-5790

Funding

Gdynia Maritime University WM/2026/PZ/06

6 · The paper itself

Abstract

The development of sustainable adsorbents and reliable predictive tools for dye-contaminated wastewater treatment remains an important challenge in environmental engineering. In this study, the adsorption of malachite green (MG) dye onto sodium dodecyl benzene sulfonate (SDBS)-modified ostrich bone waste was investigated using an integrated modeling framework combining Lindeman-Merenda-Gold (LMG) analysis, Bayesian Regression (BR), and Random Forest (RF) modeling. 52 experimental conditions (each measured in triplicate) were analyzed to evaluate the influence of pH, contact time, initial dye concentration, temperature, adsorbent dosage, and SDBS dosage on dye removal efficiency. The three modeling approaches consistently identified pH and contact time as the two most influential operational variables, whereas the relative importance of initial dye concentration and adsorbent dosage differed among the methods. BR quantified the direction and uncertainty of predictor effects, yielding posterior coefficients of 0.650 (95% CrI: 0.365-0.935) for pH, 0.467 (95% CrI: 0.263-0.672) for contact time, and -0.517 (95% CrI: -0.759 to -0.272) for initial dye concentration. Under internal validation, RF yielded an OOB R

Indexed as

BenzenesulfonatesBone and BonesMachine LearningRosaniline DyesStruthioniformesWater Pollutants, ChemicalAdsorptionAnimalsBayes TheoremHydrogen-Ion ConcentrationRandom ForestWastewaterWater PurificationBenzenesulfonatesdodecylbenzenesulfonic acidmalachite greenRosaniline DyesWastewaterWater Pollutants, ChemicaladsorptionBayesian regressionLindeman–Merenda–Gold analysismachine learningmalachite greenostrich bone wasteRandom ForestSDBS modificationwastewater treatment

Identifiers

PMID42883249

DOI10.3390/molecules31193484

PMCPMC13666174

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.