A Flavor blending method based on the linear hypothesis algorithm with FA-TOPSIS: A case study on green tea taste

An intelligence model using a linear hypothesis algorithm was created to calculate the blending proportions of granular green tea sourced from Zhejiang, China. Using quantitative sensory and chemical indicators, two similarity coefficients ( and ) were derived through the Factor Analysis-Technique for Order of Preference by Similarity to Ideal Solution (FA-TOPSIS) to screen appropriate candidate samples and determine the final blending proportions as comprehensive evaluation criteria. ranked the top five appropriate samples for blending, and identified the optimal blending proportions. Blending model solution was obtained using the Monte Carlo method, while integer programming was applied as a model constraint. The blending proportions, as determined by quantitative sensory indicators, resulted in the highest values. The result of quantitative descriptive analysis (QDA) validated that the blended samples closely replicated the target's taste profile. The present study investigates the blending of green tea taste, employing linear hypothesis algorithms with FA-TOPSIS to innovatively establish a digital indicator-based intelligent blending approach. This methodology is also applicable to flavor blending for other tea varieties. • Develop a creative blending method of tea flavor using linear hypothesis algorithm. • Utilize factor analysis to calculate indicator weights in the dataset. • Utilize TOPSIS to confirm blending proportion based on similarity to target sample. • Compare various datasets to find the optimal for blending. • The model blended samples show significant consistency with the target samples.