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Graphical abstract

Coconut copra is a potential biosorbent for removal of humic substances from peat swamp runoff. In this paper, response surface methodology was applied to evaluate the optimum conditions for removal of humic substances from peat swamp runoff using modified coconut copra. Batch adsorption experiments were conducted according to central composite design. Results show that the quadratic model is best fitted for predicting the removal efficiency with regression coefficients closer to 1 and a lower root mean square error. Dosage is found to have significant influence on the removal efficiency with p \ 0.05. Response surface models further identified the optimum dosage, contact time and temperature at 4.56 g modified coconut copra per 100 mL peat swamp runoff, 42.9 min and 56.8°C/329°K, respectively attaining the maximum removal efficiency of 88.19 %. The predicted removal efficiency was confirmed experimentally under the modelled optimum conditions; the removal efficiency attained (86.54 %) was in good agreement with the predicted value.

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Multiple self‐organizing maps (SOMs) were applied to classify soil samples according to their geographic origins. The soil physical and chemical parameters, including textures, pH, and chemical nutrients, were analyzed and used for establishing the chemometric models. To determine the optimum size and arrangement of the maps, we adapted a growing self‐organizing map algorithm. To evaluate the reliability of the models, we calculated statistic indices based on the majority vote including percentage predictive ability, percentage model stability, and percentage correctly classified using a bootstrap methodology. For means of comparison, we also used linear discriminant analysis, quadratic discriminant analysis, partial least squares‐discriminant analysis, soft independent modeling of class analogy, counter propagation network, supervised Kohonen network, and k‐nearest neighbors. In comparison to a single SOM, multiple SOMs clearly provided better classification results. The extension of multiple SOMs also led to the best discrimination of the soil origins.