Dataset overview, study background, citation guidelines, and acknowledgements for the machine learning study on Abelmoschus esculentus-derived materials.
Abelmoschus esculentus (okra) is an underutilized tropical crop whose various parts โ pods, shells, stems, leaves, seeds, and polysaccharide mucilage โ exhibit remarkable functional properties across diverse engineering applications. Despite numerous independent experimental studies reporting high-performance corrosion inhibition, pollutant adsorption, reinforced composites, and supercapacitor carbons, no unified predictive framework had connected these datasets.
This study addresses that gap by curating 231 experimental data points from 57 peer-reviewed references across five application domains, applying MICE imputation to handle missing values, and training a Heterogeneous Voting Ensemble (Random Forest + Gradient Boosting + Extra Trees) on each domain. SHAP analysis reveals that electrochemical (Icorr), thermodynamic (ฮH, ฮG), mechanical (secondary reinforcement), and operational (current density, measurement configuration) features are the dominant drivers in their respective domains.
Ensemble significantly outperforms baseline. Icorr (electrochemical current density) is the mechanistically dominant predictor โ consistent with Butler-Volmer theory. Okra mucilage achieves up to 97% IE at 25,000 ppm.
Highest-performing domain. Thermodynamic features (ฮH, ฮG, ฮS) dominate, consistent with van't Hoff adsorption theory. Wide qe range (3.9โ1132 mg/g) captured via log transformation. Okra polysaccharides show exceptional dye adsorption capacity.
Moderate performance reflects heterogeneous dataset spanning three matrix types. Secondary reinforcement (CHB) is the strongest tensile predictor. UV treatment of PP/okra composites provides substantial (+11 MPa) strength improvement.
Excellent predictive performance. Current density and measurement configuration are overwhelmingly dominant SHAP features. OAC (SSA=2109 mยฒ/g from okra pods) achieves 318 F/g at 1 A/g โ competitive with commercial activated carbons.
All models fail to predict inhibition zone diameter reliably. High inter-study variability in antimicrobial protocols, nanoparticle synthesis conditions, and target organism differences make cross-study modeling infeasible with current dataset size.
If this tool or the underlying ML study contributed to your research, please cite the associated manuscript.
All model predictions come with confidence intervals based on cross-validation error. The composite model (Rยฒ=0.57) carries the highest uncertainty (~ยฑ28% of predicted value). Predictions should not replace experimental determination for critical applications.
The predictor tool is only reliable within the experimental ranges covered by the training data. Extrapolation beyond these ranges (e.g., Icorr > 530 ยตA/cmยฒ, SSA > 2,800 mยฒ/g, qe > 1,132 mg/g) will produce unreliable results that violate the model's learned patterns.
This web tool implements a simplified regression approximation of the ensemble model, calibrated to reproduce the correct range and trends. It is not the exact scikit-learn ensemble used in the publication. For precise reproduction of paper results, use the Python script okra_ml_complete.py with the MICE-imputed CSV datasets.
The training data was compiled from diverse laboratory protocols, instruments, and reporting conventions across 57 papers. Some inter-study variability is intrinsic and cannot be fully captured. Domain-specific differences in temperature control, purity standards, and analytical methods contribute to irreducible prediction error.
scikit-learn โ RF, GBM, ET, VotingRegressor, IterativeImputershap โ TreeExplainer for SHAP value computationpandas / numpy โ data handling and preprocessingmatplotlib / seaborn โ visualizationokra_ml_complete.pyapps_script_backend.gs)ML_Results_Summary.csv โ all CV metricsscratch_shap_importance.csv โ SHAP rankingsMICE_Imputed_CSV_Datasets/ โ 5 domain CSVsFigures/ โ workflow, parity, SHAP plotsOkra_Materials_MICE_Imputed_Dataset.xlsx โ combined Excel