Biotechnology Agroalimentary and Biomedical Analysis Group, Moulay Ismaïl University, Faculty of Sciences, Biology Department, B.P. 11201, Zitoune, Meknes, Morocco; Sensor Electronic & Instrumentation Group, Moulay Ismaïl University, Faculty of Sciences, Physics Department, B.P. 11201, Zitoune, Meknes, Morocco; Université de Lyon, Institut des Sciences Analytiques, UMR 5280, CNRS, Université Lyon 1, ENS Lyon-5, rue de la Doua, F-69100 Villeurbanne, France.
Biotechnology Agroalimentary and Biomedical Analysis Group, Moulay Ismaïl University, Faculty of Sciences, Biology Department, B.P. 11201, Zitoune, Meknes, Morocco; Sensor Electronic & Instrumentation Group, Moulay Ismaïl University, Faculty of Sciences, Physics Department, B.P. 11201, Zitoune, Meknes, Morocco.
Food Chem. 2018 Mar 15;243:36-42. doi: 10.1016/j.foodchem.2017.09.067. Epub 2017 Sep 14.
Moroccan and French honeys from different geographical areas were classified and characterized by applying a voltammetric electronic tongue (VE-tongue) coupled to analytical methods. The studied parameters include color intensity, free lactonic and total acidity, proteins, phenols, hydroxymethylfurfural content (HMF), sucrose, reducing and total sugars. The geographical classification of different honeys was developed through three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs) and hierarchical cluster analysis (HCA). Honey characterization was achieved by partial least squares modeling (PLS). All the PLS models developed were able to accurately estimate the correct values of the parameters analyzed using as input the voltammetric experimental data (i.e. r>0.9). This confirms the potential ability of the VE-tongue for performing a rapid characterization of honeys via PLS in which an uncomplicated, cost-effective sample preparation process that does not require the use of additional chemicals is implemented.
来自不同地理区域的摩洛哥和法国蜂蜜通过应用结合分析方法的伏安电子舌(VE-tongue)进行分类和特征描述。所研究的参数包括颜色强度、游离内酯和总酸度、蛋白质、酚类、羟甲基糠醛含量(HMF)、蔗糖、还原糖和总糖。通过三种模式识别技术:主成分分析(PCA)、支持向量机(SVM)和层次聚类分析(HCA)来实现对不同蜂蜜的地理分类。蜂蜜的特征描述是通过偏最小二乘建模(PLS)来实现的。开发的所有 PLS 模型都能够使用伏安实验数据(即 r>0.9)作为输入准确估计分析参数的正确值。这证实了 VE-tongue 通过 PLS 对蜂蜜进行快速特征描述的潜在能力,其中执行的是一种不复杂、具有成本效益且不需要使用额外化学物质的简单样品制备过程。