Statistical optimisation for the formulation of edible bird nest-based instant soup using Response Surface Methodology (RSM) / Mohamad Haziq Aiman Ismail … [et al.]

Scientifically, edible bird nest (EBN) contains protein, carbohydrate, fat, and bioactive compounds that can boost the human immune system, strengthen bones, and improve skin complexion. The inclusion of EBN as the main ingredient in instant soup mix can provide an accomplished nutritional meal with...

Full description

Saved in:
Bibliographic Details
Main Authors: Ismail, Mohamad Haziq Aiman, Osman, Mohamed Syazwan, Khairudin, Khairunnisa, Zainon, Mohammad Faizal, Mohd Eusoff, Abdul Fattah, Zubli, Quzaimer
Format: Article
Language:English
Published: Universiti Teknologi MARA Shah Alam 2022
Online Access:https://ir.uitm.edu.my/id/eprint/70369/1/70369.pdf
https://ir.uitm.edu.my/id/eprint/70369/
https://mjcetfkk.uitm.edu.my/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Scientifically, edible bird nest (EBN) contains protein, carbohydrate, fat, and bioactive compounds that can boost the human immune system, strengthen bones, and improve skin complexion. The inclusion of EBN as the main ingredient in instant soup mix can provide an accomplished nutritional meal with protein, carbohydrates, fat, vitamin etc. The formulation of the main ingredients of EBN-based instant soup is statistically optimised using Response Surface Methodology (RSM) technique, with three main ingredients (i.e., EBN wt%, mushroom powder wt%, and skimmed milk wt%) as input factors meanwhile the antioxidant activity (%) of EBN-instant soup solution as the response. The antioxidant activity (%) is analysed using a standard DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) antioxidant assay. The optimised instant soup mix formulation contains 40 wt% EBN, 20 wt% mushroom powder, and 20 wt% skimmed milk with antioxidant activity of 85.35%. The response fit a linear model with a coefficient of determination (R2, 0.9734) and a standard deviation of 0.2. The model is significant with a p-value of < 0.0001which is below 0.0500. The model has been validated successfully with a maximum error of 5.32%.