Prediction of Cu(II) biosorption performances on wild mushrooms Lactarius piperatus using Artificial Neural Networks (ANN) model

• Published in: Canadian journal of chemical engineering 2017-04, Vol.95 (4), p.615-622
• Main Authors: Indolean, Cerasella, Măicăneanu, Andrada, Cristea, Vasile‐Mircea
• Format: Article
• Language: English
• Subjects: Adsorption; artificial neural network; Artificial neural networks; Beads; Beryllium; Biodegradability; Biomass; biosorption; Copper; Cu(II); Efficiency; functional foods; Lactarius; Lactarius piperatus; Learning theory; Mathematical models; Mushrooms; Neural networks; Predictions
• ISSN: 0008-4034; 1939-019X
• DOI: 10.1002/cjce.22703

This work investigates the possible usage of edible mushrooms as support for metabolic quantities of copper. Biosorption potential of natural and biodegradable matrix formed from wild Lactarius piperatus mushrooms, in suspension (LP) and alginate immobilized based beads (LPAB), was explored. The effects of biomass quantity, Cu(II) concentration, and temperature were assessed. LPAB showed better adsorption capacity (7.67 mg/g) by comparison to LP biosorbent (6.43 mg/g). Also, biosorption efficiencies up to 76 and 99 % for LP and LPAB (for the same quantity of biomass, 2 g), respectively, were obtained. Furthermore, a multilayer feed forward Artificial Neural Network (ANN) model was developed in order to predict the biosorption efficiency. The trained ANNs, for LP and LAPB biosorbents, showed good correlation (R = 0.998) between the predicted and experimental biosorption efficiency, associated to reduced mean relative errors and demonstrated that the ANN has a good generalization potential. 1–2 g of Lactarius piperatus mushroom, as powder or in alginate‐based beads containing Cu(II), could be used as a dietary supplement in order to supply the daily copper demand of the organism. Cu(II) Biosorption on L. Piperatus Biomass ‐ ANN Model.