食品科学 ›› 2020, Vol. 41 ›› Issue (20): 227-233.doi: 10.7506/spkx1002-6630-20190916-195

• 成分分析 • 上一篇    下一篇

多种甜味剂的电子舌味觉评价

黄嘉丽,黄宝华,左珊珊,郭成龙,周金林,卢宇靖   

  1. (1.广东工业大学轻工化工学院,广东 广州 510006;2.广东金骏康生物技术有限公司,广东 佛山 528225;3.梅州金柚康健康科技有限公司,广东 梅州 514021;4.广东工业大学生物医药学院,广东 广州 510006)
  • 出版日期:2020-10-25 发布日期:2020-10-23
  • 基金资助:
    广东省科技计划资助项目(2016A020209009);广东省农业科技特派员精准扶贫乡村振兴入库项目(19ZK0086;19ZK0091); 广东省“扬帆计划”引进创新创业团队专项资助项目(2015YT02S006); 广东省农业厅农业科技创新项目(2018LM2175);广东工业大学横向合作项目(16HK0102)

Taste Evaluation of Various Sweeteners by Electronic Tongue

HUANG Jiali, HUANG Baohua, ZUO Shanshan, GUO Chenglong, ZHOU Jinlin, LU Yujing   

  1. (1. School of Chemical Engineering and Light Industry, Guangdong University of Technology, Guangzhou 510006, China; 2. Golden Health Biotechnology Co. Ltd., Foshan 528225, China; 3. Golden Pomelo Biotechnology Co. Ltd., Meizhou 514021, China;4. School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, China)
  • Online:2020-10-25 Published:2020-10-23

摘要: 为客观评价甜味剂的甜度和甜味特征,以感官评价为依据,考察电子舌评价蔗糖、甜菊糖、甘草甜素、罗汉果提取物、新橙皮苷二氢查尔酮(neohesperidin dihydrochalcone,NHDC)、柚苷二氢查尔酮(naringin dihydrochalcone,NDHC)、甜蜜素、三氯蔗糖、糖精钠和安赛蜜10 种甜味剂的可行性。结果表明,电子舌的甜味传感器GL1能有效检测其中的蔗糖、NHDC、NDHC、糖精钠、安赛蜜和甜蜜素6 种甜味剂的甜味信息,其中甜蜜素的甜味响应为负值。基于电子舌检测值采用主成分分析及聚类分析可对上述6 种甜味剂进行较好分类。以蔗糖甜度为参照,NHDC、NDHC、糖精钠、安赛蜜的甜度可用电子舌测得的甜味值换算成相对甜度表示。采用最小二乘法建立甜味响应为正值的5 种甜味剂电子舌测定值和感官评价结果之间的定量预测模型。结果表明,5 种甜味剂的感官评价结果和电子舌测定值之间均具有良好的相关性,蔗糖、NHDC和安赛蜜呈现良好的线性关系。5 种甜味剂甜味预测集的Rp均大于0.96,均方根误差小于0.9,显示所建立的预测模型具有较高的精度和良好的预测效果。

关键词: 甜味剂;电子舌;感官评价;甜度;预测模型

Abstract: In order to objectively evaluate the sweetness and characteristics of sweeteners, we investigated the feasibility of electronic tongue (ET) to evaluate the taste of 10 sweeteners, including sucrose, stevia, glycyrrhizin, mangosteen extract, neohesperidin dihydrochalcone (NHDC), naringin dihydrochalcone (NDHC), cyclamate, sucralose, sodium saccharin and acesulfame by comparison with sensory evaluation. The results showed that the sweetness sensor GL1 could effectively detect the sweetness of sucrose, NHDC, NDHC, sodium saccharin, acesulfame and cyclamate, and the response to cyclamate was negative. Based on the ET data, the 6 sweeteners were clearly classified by principal component analysis (PCA) and cluster analysis. Relative to sucrose, the sweetness of the other 4 sweeteners was expressed. A quantitative prediction model between the results of ET detection and sensory evaluation was established for each of the five sweeteners with positive sweetness response by least square method. The results showed a good correlation for the five sweeteners and good linear relationships for sucrose, NHDC and acesulfame. The correlation coefficient of prediction set (Rp) for each of the sweeteners was greater than 0.96, and the root mean square error was less than 0.9, indicating that the prediction model has high accuracy and good prediction performance.

Key words: sweeteners; electronic tongue; sensory evaluation; sweetness; prediction model

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