Developing food sensory test system with preference test (Hedonic and Hedonic quality) wheat bread case study

Abstract

Bread is a food source of carbohydrates that are often consumed by the community. Various types of bread were produced to meet consumer's curiosity, one of which is wheat bread. Manufacturers must be able to produce quality wheat bread and liked by consumers. Increasing the quality of bread will certainly have an impact on sales to be generated. One of the efforts in improving the quality of wheat bread is by doing the Hedonic test and Hedonic Quality test. This study aims to develop a system capable of providing an assessment of wheat bread. This study develops machine learning system with supervised learning algorithm, then using the results of the initial Organoleptic test as Knowledge-Based (KB). This test involved detection, recognition, discrimination, scaling and ability to express likes or dislikes (hedonic quality), using expert judgment. Hedonic quality is used as a variable for assessing wheat bread products with 4 variables, which include flavor, taste, appearance, and texture. While the hedonic test using two classes: likes or dislikes. This KB used as Naive Bayes Classifier algorithm initial knowledge, The test results using 10 fold shown average accuracy 98.8%, while the final goal of the development of this system will create a system capable of providing an assessment of a wheat bread product.