Optimization of enzymatic hydrolysis parameters for sapodilla fruit ( Manikara achras L.) juice extraction

Abstract

Cold pressing of sapodilla fruit pulp for juice extraction is generally difficult and yields an inferior quality of juice due to fruit tissue rigidity and its granular pulpy nature which necessitates the enzyme combination for tissue hydrolysis. A central composite design was used to optimize the sapodilla tissue hydrolysis conditions like pectinase concentration (T1 0.1%–0.2%), cellulase concentration (T2 0.05%–0.1%), incubation temperature (T3 40–50°C), and incubation period (T4 90–150 min). The responses studied were juice yield, viscosity, total soluble solid, L‐value, clarity, total phenol content, and overall acceptability. Regression analysis fitted to a second‐order quadratic model for each response was a significant (p < .05) function of hydrolysis. The optimized level of pectinase concentration, cellulase concentration, incubation temperature, and period was 0.125%, 0.063%, 42.50°C, and 112.20 min, respectively. The validity of the model was confirmed by the closeness of experimental values (juice yield 61.51 ± 0.50%, viscosity 2.88 ± 0.02 cps, TSS 18.66 ± 0.06 °Brix, L‐value 35.94 ± 0.52, clarity (abs) 1.91 ± 0.03, TPC 1.588 ± 0.02 mg GAE/100 ml, and OA 7.23 ± 0.25) with predicted values. Practical applications Various benefits of enzymatic hydrolysis of sapodilla tissue for juice extraction have been found over conventional methods (cold and hot press). It overcomes the challenges of extractability and pressability. It also improves the functional (total phenolic content), physical (soluble solid, color, clarity, and viscosity), and sensory characteristics (overall acceptability) of juice. Higher juice yield and total phenolic content suggest that enzymatic hydrolysis of sapodilla could promote the juice industry for the production of quality juice and healthy fruit‐based drinks too. The reduced processing time in enzymatic hydrolysis as compared with that in traditional method and high TSS in extracted sapodilla juice will further offer the advantages of a reduced burden on cane sugar for sweetening purpose.

Optimization of Convective Tray-Drying Process Parameters for Green Banana Slices Using Response Surface Methodology and Its Characterization

“…Furthermore, the DSC and TGA analysis revealed green banana four's maximum gelatinization, dehydration, and decomposition temperature and these might be due to the compositional changes in banana (maximum starch and dietary fber content). Te green banana four obtained in this study can be useful for numerous applications in food and nonfood processing industries such as to develop high temperature stable gel, thickening, encapsulating agent, and binder in tablet formulation [21,26]…”

Development and characterization of physical stability, glycemic index, flow behaviors, and antioxidant activity of finger millet‐based beverage

“…The yield % of beverages was estimated using the method described by Singh et al. (2022). The volume of finger millet‐based beverage obtained after each experiment was measured and divided by the volume of water added (80 mL) and weight of grain (20 g), and the yield was calculated using the following equation: $$$$\begin{eqnarray} &&{\mathrm{Yield}}\left( {\mathrm{\% }} \right) =\nonumber\\ && {\mathrm{total}}\,{\mathrm{volume}}\,{\mathrm{of}}\,{\mathrm{obtained}}\,{\mathrm{milk/total}}\,{\mathrm{weight}}\,{\mathrm{of}}\,{\mathrm{grain}} \nonumber\\ &&+ {\mathrm{water}} \times {\mathrm{100}}\end{eqnarray}$$$$…”

“…(1) where Y 1 is the expected response, β 0 is an offset term, β 1 and β 2 are regression coefficients for linear effects, and β 11 and β 22 are quadratic effects. In this model, X 1 and X 2 are independent variables (Singh et al, 2022). For fitting the mathematical models, regression analysis, and model variance, optimization parameters and analysis of variance (ANOVA) were employed to determine the significant dif-ference among linear, quadratic, and interaction terms of independent components.…”

“…The yield % of beverages was estimated using the method described by Singh et al (2022). The volume of finger millet-based beverage obtained after each experiment was measured and divided by the volume of water added (80 mL) and weight of grain (20 g), and the yield was calculated using the following equation:…”

“…The instrument's lens was wiped and cleaned before taking readings. The viscosity of beverages was analyzed by a Brookfield viscometer (Model DV-II+Pro, Brookfield Engineering Laboratory) at 80 rpm and 25 • C. The beverages were analyzed for pH using the digital pH meter (Singh et al, 2022).…”