FOOD SCIENCE ›› 2022, Vol. 43 ›› Issue (23): 63-71.doi: 10.7506/spkx1002-6630-20211011-105

• Basic Research • Previous Articles     Next Articles

Predictive Model for Comprehensive Quality Evaluation of Pumpkin (Cucurbita moschata) Fruit Based on Sensory Analysis, Texture Characteristics and Physicochemical Components

ZHAO Siying, LI Lu, LIU Xiaoxi, ZHAO Gangjun, WU Haibin, LUO Jianning, GONG Hao, ZHENG Xiaoming, LI Junxing   

  1. (1. Vegetable Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; 2. Guangdong Key Laboratory for New Technology Research of Vegetables, Guangzhou 510640, China; 3. Sericultural & Agri-food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, China)
  • Online:2022-12-15 Published:2022-12-28

Abstract: In this study, a set of methods for comprehensive quality evaluation of pumpkin (Cucurbita moschata) fruit was established in order to provide a theoretic basis for exploring the key sensory quality attributes and breeding new cultivars with excellent fruit quality. Twenty pumpkin fruit samples of different cultivars were used, and their sensory attributes, texture parameters, and physicochemical indicators were measured. The obtained data were subjected to difference analysis, correlation analysis and stepwise regression analysis. The difference analysis showed that sensory attributes, texture parameters and physicochemical indicators were different between these pumpkin samples. The correlation analysis indicated that a higher comprehensive taste score was observed for pumpkin fruit with higher softness, stickiness and sweetness and moister mouthfeel. In addition, higher elasticity and cohesiveness and lower adhesiveness resulted in better overall taste of pumpkin fruit. Higher contents of sugar, pectin and total starch and lower water content contributed to better overall taste. Through stepwise regression analysis, a comprehensive sensory evaluation prediction model was established as follows: Y = ?1.547 + 0.072 × fructose content + 0.052 × soluble pectin content ? 0.053 × amylose content ? 0.022 × adhesiveness + 21.278 × crude fiber content (Y is the predicted value of overall taste score), and this model had good predictive performance. Use of texture parameters and physicochemical indicators as objective measures can better make up for the disadvantage of the subjectivity of sensory evaluation.

Key words: Cucurbita moschata; sensory analysis; texture characteristics; physicochemical composition; correlation analysis; regression model

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