Abstract
The enzyme-assisted juice extraction from custard apple (Annona squamosa L.) puree was optimized within the domain of 0.3%–5% (w/w) enzyme concentration (EC), 0.5–5 hr incubation period (t), and 30–60°C temperature (T). A multi-objective optimization was carried out using two techniques; response surface methodology (RSM) and artificial neural network with genetic algorithm (ANN-GA). The models showed an overall R2 and mean squared error (MSE) of 0.97 and 2.20 for RSM and 0.96 and 1.86 for ANN, respectively. Sensory evaluation was performed for the two best conditions from both the techniques. The final optimum condition (EC: T: t) of 2.21%: 47°C: 4.47 hr was selected having the maximum overall acceptability of 6.02. The corresponding experimental response was 88% (w/w) yield, 32.8 mg GE/ml total reducing sugar, and 60.9% clarity. The optimally extracted custard apple beverage showed 94.0 µg GAE/ml of phenolic content and 34.7 μg GAEAC/ml of antioxidant capacity. Practical applications Custard apple is an underexplored tropical fruit. Despite having good nutritional values and attractive flavor, its availability as a processed product is very limited, mainly because custard apple is highly perishable. Manufacturing juice or beverage from custard apple can reduce its wastage and promote its utilization. Enzyme-assisted juice extraction is one of the most efficient and environmentally friendly ways to obtain better yield and clarity. Pectinase enzyme was used to enhance juice extraction, and a multi-objective optimization was followed using two techniques; response surface methodology (RSM) and artificial neural network with genetic algorithm (ANN-GA). The best solutions given by RSM and ANN-GA were compared based on sensory evaluation and beverage receiving maximum overall acceptability was finally selected. The current study will undoubtedly help in promoting the utilization of custard apple, its enzyme treatment, reduce wastage, and thereby pave the pathway for similar underexplored fruits.
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