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

• 基础研究 • 上一篇    下一篇

基于感官品质、质构特征及理化成分分析的中国南瓜果实感官综合评价预测模型

赵思颖,李璐,刘小茜,赵钢军,吴海滨,罗剑宁,龚浩,郑晓明,李俊星   

  1. (1.广东省农业科学院蔬菜研究所,广东 广州 510640;2.广东省蔬菜新技术研究重点实验室,广东 广州 510640;3.广东省农业科学院蚕业与农产品加工研究所,广东 广州 510610)
  • 出版日期:2022-12-15 发布日期:2022-12-28
  • 基金资助:
    广东省重点领域研发计划项目(2020B020220003); 广东省科技计划项目(2021A1515011187;2019A050520002;2017B030314111); 广东省农业科学院农业优势产业学科团队建设项目(202103TD); 财政部和农业农村部:国家现代农业产业技术体系项目(CARS-23-G-50); 省级现代农业产业技术体系建设果菜产业技术体系创新团队(2019KJ110); 广州市科学研究计划重点项目(201904020012)

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

摘要: 本实验以20 份不同类型中国南瓜种质资源的果实为研究对象,分别测定其感官品质指标、质构特征指标和理化成分指标,并进行差异性分析、相关性分析及逐步回归分析,以建立一套数据化、能够综合评价中国南瓜果实感官品质的方法,为探究感官品质关键因子和优良品质新品种选育提供理论依据。差异性分析结果表明,不同南瓜材料的感官品质指标、质构特征指标和理化成分含量均存在不同程度的差异。相关性分析结果表明,南瓜果实的柔软度、黏糯度、甜度越高,口感越微润,综合口感越好;此外,质构特征指标中弹性、内聚性越高,黏附性越低,南瓜果实的综合口感越好;理化成分中糖、果胶和总淀粉含量越高,含水量越低,综合口感越好。通过逐步回归分析建立了Y=-1.547+0.072×果糖含量+0.052×可溶性果胶含量-0.053×直链淀粉含量-0.022×黏附性+21.278×粗纤维含量(Y为综合口感预测得分)的感官综合评价预测模型,该模型预测口感效果较好,综上,以质构特征和理化成分含量作为客观评价指标可以较好地弥补感官分析主观性较强的劣势。

关键词: 中国南瓜;感官分析;质构特征;理化成分;相关性分析;回归模型

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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