2nd Asia Pacific International Conference on Industrial Engineering and Operations Management

Improving Mixing Process Quality for Sweet Soybean Sauce Using Full Factorial Design of Experiment

Adnan Hassan & Noorilyana Abu Bakar
Publisher: IEOM Society International
0 Paper Citations
Track: Quality Control

Soybean sauce is a common condiment in Asian cooking. It is made from fermented soybeans combined with other ingredients. The manufacturing of soybean sauce involves fermentation, extraction, sterilization, homogenization, bottling, labeling, and packaging. Several researchers have reported investigations related to soybean production. Our review reveals that there has been limited literature on the optimization of mixing parameters for soybean sauce using fractional factorial experimental design. There are variations of soybean sauces available in the market where the quality is dependent on the method and duration of fermentation, and the ratios of fermented soy, water, sugar, caramel, salt, and other addition to the ingredients. The mixing ratios among the ingredients are critical to ensure that the resulted Brix value conforms to specification. This study was based on a case study company located in Johor Malaysia where it experienced high variability in the Brix value in sweet soybean source production. Prior to this investigation, a trial and error approach was used to balance among the ingredients. The quantity of sugar was increased if it was too salty, and additional soy water was added if it was too sweet until the standard specification was achieved. The objective of this study is to reduce the mixing process variability by investigating its optimum parameter setting. To simplify the experiments, two stages of 2k full factorial design of experiments were conducted. The first stage involved mixing ingredients of sugar, caramel, and monosodium glutamate (MSG), and the second stage involved mixing ingredients of sugar, salt, water, and acetic acid. The responses (Brix readings) were statistically analyzed using ANOVA and the factors and interaction effects. The mathematical regression models were formulated to predict the Brix readings. The optimum parameter setting proposed in this paper should be used to replace the traditional trial-and-error approach in the mixing process.  

Published in: 2nd Asia Pacific International Conference on Industrial Engineering and Operations Management, Surakarta, Indonesia

Publisher: IEOM Society International
Date of Conference: September 13-16, 2021

ISBN: 978-1-7923-6129-6
ISSN/E-ISSN: 2169-8767