食品科学 ›› 2017, Vol. 38 ›› Issue (2): 290-295.doi: 10.7506/spkx1002-6630-201702045

• 安全检测 • 上一篇    下一篇

高效液相色谱-电化学检测指纹图谱鉴别3种单花种蜂蜜花源

贺 琼,何亮亮,康予馨,程 妮,吕新刚,曹 炜   

  1. 1.西北大学化工学院食品科学与工程系,蜂产品应用技术研究中心,陕西 西安 710069; 2.陕西省蜂产品工程技术研究中心,陕西 西安 710065
  • 出版日期:2017-01-25 发布日期:2017-01-16
  • 基金资助:
    国家自然科学基金面上项目(31272510);陕西省社会发展攻关项目(2016SF-425);西北大学研究生创新教育项目(YZZ14043);陕西省大学生创新创业训练计划项目(0687)

Authentication of Three Monofloral Honeys by High Performance Liquid Chromatography with Electrochemical Detection

HE Qiong, HE Liangliang, KANG Yuxin, CHENG Ni, Lü Xingang, CAO Wei   

  1. 1. Department of Food Science and Engineering, School of Chemical Engineering, Applied Technology Research Center of Bee Products, Northwest University, Xi’an 710069, China; 2. Bee Product Research Center of Shaanxi Province, Xi’an 710065, China
  • Online:2017-01-25 Published:2017-01-16

摘要: 基于高效液相色谱-电化学检测器(high performance liquid chromatography-electrochemical detection,HPLC-ECD)技术建立一种新的蜂蜜花源鉴别方法。以采自中国不同地区的3 种单花种蜂蜜为研究对象,构建了3 种单花种蜂蜜的HPLC-ECD指纹图谱,提取HPLC-ECD图谱共有峰面积信息并应用主成分分析和系统聚类分析进行蜂蜜花源分类,并对完全未参与建模的蜂蜜样品进行验证。结果表明,45 个蜂蜜样品(枸杞蜜、荆条蜜、荔枝蜜各15 个),均可通过主成分分析和系统聚类分析按照其花源正确分类,正确率达到100%。该蜂蜜花源鉴别的模型对完全未参与建模的枸杞蜜、荆条蜜和荔枝蜜样品的正确预判率可达到100%、80%和100%。研究表明,HPLCECD指纹图谱技术应用主成分分析和系统聚类分析可以作为一种快速、准确、绿色的判别蜂蜜花源的方法。

关键词: 蜂蜜, 高效液相色谱-电化学检测器, 指纹图谱, 主成分分析, 系统聚类分析

Abstract: A novel method for the identification of the floral origins of honeys was established by high performance liquid chromatography with electrochemical detection (HPLC-ECD). The HPLC-ECD fingerprints of three monofloral honeys collected from different areas of China were established. From the HPLC-ECD fingerprints, the areas of the common peaks were obtained and principal component analysis (PCA) and hierarchical cluster analysis (HCA) were performed to classify 45 honey samples (15 medlar honey samples, 15 vitex honey samples, and 15 litchi honey samples) according to their floral origins. These samples were successfully classified by PCA and HCA with 100% correct classification rates. To evaluate the reliability of the model based on 45 honey samples, some medlar honey, vitex honey and litchi honey samples which were not included in the modeling sample set were validated with correct prediction rates of 100%, 80% and 100%, respectively. The results indicated that HPLC-ECD combined with PCA and HCA may be used as a fast, accurate and environmentally safe method to differentiate honeys according to their floral origins.

Key words: honey, high performance liquid chromatography-electrochemical detection (HPLC-ECD), fingerprint, principal component analysis, hierarchical cluster analysis

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