Computational modelling approach for the optimization of apple juice clarification using immobilized pectinase and xylanase enzymes

Shady S Hassan,Gwilym A Williams,Amit K Jaiswal

doi:10.1016/j.crfs.2020.09.003

Shady S Hassan, Gwilym A Williams + Show 1 more

Open Access

https://doi.org/10.1016/j.crfs.2020.09.003

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Abstract

Apple juice is typically marketed as a clear juice, and hence enzymatic treatments are common practices in juice industry. However, enzymatic treatments have been shown to face some challenges when process efficiency, and cost effectiveness are concerned. Therefore, it is necessary to optimize the enzymatic treatment process to maximize efficiency, and reuse enzymes to minimize the overall cost via immobilization. In this context, the present work features the immobilization of pectinase and xylanase from M. hiemalis on genipin-activated alginate beads, with subsequent evaluation of its efficacy in apple juice clarification. A central composite rotatable design (CCRD), coupled with artificial neural network (ANN) for modeling and optimization was used to design the experiments. Deploying a coupling time up to 120 min, and an agitation rate of 213 rpm (pectinase) - 250 rpm (xylanase), a maximum fractional enzyme activity recovered was observed to be about 81–83% for both enzymes. Optimum enzyme loading and genipin concentration were found to be 50 U/ml and 12% (w/v), respectively. The immobilized enzyme preparations were suitable for up to 5 repeated process cycles, losing about 45% (pectinase) - 49% (xylanase) of their initial activity during this time. The maximum clarity of apple juice (%T660, 84%) was achieved at 100 min when pectinase (50 U/ml of juice) and xylanase (20 U/ml of juice) were used in combination at 57 °C. The immobilized enzymes are of industrial relevance in terms of biocompatibility, recoverability, and operational-storage stability.

Highlights

Unlike animal counterparts, plant cells are surrounded by an extracellular matrix known as the cell wall which comprises polysaccharide and protein polymers
Pectinase and xylanase enzymes have been commonly applied to clarification in the apple juice industry (Garg et al, 2016; Nagar et al, 2012; Sharma and PatelSugandha, 2017)
Trained artificial neural network (ANN) model has been successfully employed to optimize apple juice clarification by ultrafiltration using Bayesian regularization algorithm (Go€kmenAçar et al, 2009), suggesting that incorporation into the present study could be beneficial, The results of the algorithms were compared by minimized root mean squared error (RMSE) and maximized coefficient of determination (R2)

Summary

Introduction

Plant cells are surrounded by an extracellular matrix known as the cell wall which comprises polysaccharide and protein polymers. The main advantage of the covalent approach is the strength of enzyme binding to a solid phase, theoretically minimizing in-process enzyme leachate from the carrier (Mohy Eldin et al, 2011). Natural polymers such as alginate beads have received considerable attention due to their potential applications in the food and pharmaceutical. An artificial neural network was employed to achieve the maximum recovery of enzyme fractional activity (%) of pectinase and xylanase, as well as maximum apple juice clarification using the immobilized enzymes. Trained ANN model has been successfully employed to optimize apple juice clarification by ultrafiltration using Bayesian regularization algorithm (Go€kmenAçar et al, 2009), suggesting that incorporation into the present study could be beneficial, The results of the algorithms were compared by minimized root mean squared error (RMSE) and maximized coefficient of determination (R2)

Enzymes

Enzyme immobilization

Enzymatic treatment of apple juice

Conclusion