Optimization of the Formulation of Sago Starch Edible Coatings Incorporated with Nano Cellulose Fiber (CNF)

Abstract

This study aimed to produce new edible coatings based on the mixture of sago starch, cellulose nanofiber (CNF), glycerol, and tween-80.The effect of sago starch (5–10 g of starch/100 ml of distilled water), CNF (0.5–20% w/w), glycerol (10–30% w/w), and tween-80 (0.5–10% w/w) based on sago starch concentration on contact angle (CA), water vapor permeability (WVP), oxygen permeability (PO2) and tensile strength (TS) properties of the edible coatings were optimized using factorial experimental design (2k).The result showed that the linear model for all independent variables was significant (<i>P</i><0.05) on all responses (dependent variable).The sago starch concentration depicted a significant (p < 0.001) positive effect on contact angle; CNF showed a statistically significant effect on WVP, PO2, and TS; tween-80 showed a significant effect on all dependent variables, whereas glycerol only affected WVP. The optimum concentrations of sago starch, CNF, glycerol, and tween-80 were predicted to be 5 g/100 ml distilled water, 20% w/w, 10% w/w, and 0.5% w/w based on sago starch, respectively to obtain the minimum contact angle, WVP, PO2, and the maximum TS. The predicted data for the optimized coating formulation were in good agreement with the experimental value. This work revealed that the potential of sago starch/CNF based coating formulation could be effectively produced and successfully applied for coating of food.

Mechanical Insights into Biomaterial Wound Healing Patches and Polyvinyl Acetate Elasticity

“…The wound healing patch samples comprise the base of the pieces from the silicone rubber, water, C27H48O20 (cornstarch), and emulsifier. According to Kasim [11], cornstarch can enhance the performance of biopolymer edible coating. The emulsifier works as a mixing agent to mix the oil-based component with the water-based component, such as silicone rubber with water, referring to the statement from Rahman [12].…”

Real-Time Quantification of Meat Paste Constituents Displaying Nonlinear Blending Behavior Including Salt Using an In-Line NIR MEMS Sensor

“…In this work, the complexity of differently composed meat pastes is modeled using model meat mixtures that were systematically varied according to an experimental design. Design of Experiments (DoE) in combination with Response Surface Methodology (RSM) is a powerful statistical tool, − which enables the identification and quantification of statistically significant recipe components as well as synergistic nonlinear blending behavior of mixture components, while requiring only a limited number of experiments making this approach much more effective and informative than conventional one-factor-at-a-time approaches. ,,, The methodology has thus been used in a wide variety of contexts including food technology. − Using a mixture design wherein salt was included as a component in the reference mixtures, the effect of compositional changes in the meat paste on the spectral characteristics in the NIR region was investigated. For this purpose, various meat pastes were produced based on a balanced mixture design with varying amounts of fat, protein, water, and salt and subsequently were analyzed with a novel process analyzer prototype using a MEMS-based NIR sensor.…”