中国农业科学 ›› 2019, Vol. 52 ›› Issue (17): 3034-3048.doi: 10.3864/j.issn.0578-1752.2019.17.011

• 食品科学与工程 • 上一篇    下一篇

超声耦合不同酸度柠檬酸脱苦溶液对苦杏仁品质特性的影响

史芳芳,张清安()   

  1. 陕西师范大学食品工程与营养科学学院,西安 710119
  • 收稿日期:2018-12-29 接受日期:2019-07-19 出版日期:2019-09-01 发布日期:2019-09-10
  • 通讯作者: 张清安
  • 作者简介:史芳芳,E-mail:13772427380@163.com。
  • 基金资助:
    国家自然科学基金青年基金(31101324);陕西省重点研发计划(2017NY-167);陕西省重点研发计划(2018ZDXM-NY-086);西安市科技局高校院所人才服务企业工程项目(2017071CG/RC034SXSF003)

Effects of Different Citric Acid Solutions on the Quality of Apricot Kernels During Debitterizing Mediated by Ultrasound Irradiation

SHI FangFang,ZHANG QingAn()   

  1. College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi’an 710119
  • Received:2018-12-29 Accepted:2019-07-19 Online:2019-09-01 Published:2019-09-10
  • Contact: QingAn ZHANG

摘要:

【目的】探究超声耦合不同pH柠檬酸溶液脱苦对苦杏仁颜色等品质特性的影响;利用相关性分析明确各指标间的关系,以简化脱苦酸溶液对苦杏仁品质的评价指标;运用多元数据处理对不同酸度脱苦溶液进行分类,为科学合理选择脱苦溶液从而减少苦杏仁营养及感官品质损失提供理论依据。【方法】以苦杏仁为研究对象,首先利用高效液相色谱仪、分光光度计、质构仪等仪器对苦杏仁的质构、β-葡萄糖苷酶活性、苦杏仁苷和水分含量等进行测定,并评定脱苦后杏仁的感官特性。同时,测定脱苦溶液中总酚、蛋白质、还原糖、可溶性固形物的含量,并采用单因素方差分析、相关性分析对测定结果进行显著性和相关性分析。利用主成分分析(PCA)、聚类分析(CA)等多元数据处理方法,对6种不同酸度脱苦液中相关数据进行综合分析。【结果】与未脱苦杏仁相比,6种不同酸度脱苦溶液处理后,脱苦杏仁的颜色、硬度、脆性、咀嚼性、回复性、胶着性及感官评价结果均存在显著差异,且脱苦杏仁中水分含量增多,β-葡萄糖苷酶活性变化显著。当脱苦柠檬酸溶液pH为5时,苦杏仁脱苦所需时间最短,仅需90 min,且苦杏仁中各营养物质损失较少、口感也较好。通过相关性分析可知,各指标间具有一定的相关性。PCA、CA的分析结果一致,即二者均可将6种脱苦溶液分为3大类,且同一大类中各脱苦溶液之间的相关理化指标差异不显著。【结论】综合分析,pH为5的柠檬酸溶液可以作为超声快速脱除苦杏仁苦味的较优脱苦溶液,这样既可以加速苦杏仁脱苦,又能减少苦杏仁中营养物质的流失,最大程度保持苦杏仁固有的口感特性,可为苦杏仁的产业化快速脱苦提供有力支撑。

关键词: 苦杏仁, 脱苦溶液, 质构, 感官评定, 主成分分析, 聚类分析

Abstract:

【Objective】 In this paper, the effects of different pH values coupled with ultrasound irradiation on the color, texture and some physicochemical properties of the apricot kernels during debitterizing were investigated. Correlation analysis of all measured variables was conducted to simplify the evaluating indicators of the quality of debitterizing apricot kernels with different solvents. In addition, multivariate data analysis was applied to categorize the debitterizing solvents and provide the theoretical base for the selection of debitterizing solvents in the debitterizing processing of apricot kernels. 【Method】 Determinations of the amygdalin, water content and beta-glucosidase activity of the apricot kernels, and the contents of total phenols, proteins, reducing sugars and soluble solids in the debitterizing solutions were conducted by the high performance liquid chromatography, spectrophotometer and abbe refractometer, etc, respectively. In addition, the texture and organoleptic properties of the debitterized apricot kernels were also investigated by the texture analyzer and sensory evaluation test, respectively. Finally, principal component analysis (PCA) and cluster analysis (CA) were applied to classify the six kinds of debitterizing solvents. 【Result】 Compared with the untreated sample, the color, hardness, fracturability, chewiness, resilience, gumminess and sensory evaluation of the debitterized apricot kernels were significantly different after being debitterized by the six kinds of debitterizing solvents. In the meantime, the moisture content and the activity of beta-glucosidase of the apricot kernels increased significantly. Based on the comprehensive analysis of the physicochemical indicators, the debitterizing time was the shortest (only 90 min), and the loss of the nutrients in the apricot kernels was less than the other treated samples, when the pH of the debitterizing solution was at the value of 5. Correlation analysis showed a certain correlation among the indicators. The results of PCA and CA provided the same classification, and the six kinds of debitterizing solvents could be classified into three categories. Moreover, the effects of the different solutions on the apricot kernels in the same category were not significant. 【Conclusion】 The citric acid solution with a pH of 5 could be used as the optimal solution to remove the bitterness of apricot kernels, which not only accelerated the bitterness of apricot kernels, but also reduced the loss of nutrients of apricot kernels. All these results could provide the support for the industrial rapid debitterizing of apricot kernels.

Key words: apricot kernel, debitterizing solvents, texture, sensory evaluation, principal component analysis, cluster analysis