Conti Marcelo Enrique, Stripeikis Jorge, Campanella Luigi, Cucina Domenico, Tudino Mabel Beatriz
SPES Development Studies Research Centre, Università di Roma La Sapienza, Rome, Italy.
Chem Cent J. 2007 Jun 7;1:14. doi: 10.1186/1752-153X-1-14.
The characterization of three types of Marche (Italy) honeys (Acacia, Multifloral, Honeydew) was carried out on the basis of the their quality parameters (pH, sugar content, humidity) and mineral content (Na, K, Ca, Mg, Cu, Fe, and Mn). Pattern recognition methods such as principal components analysis (PCA) and linear discriminant analysis (LDA) were performed in order to classify honey samples whose botanical origins were different, and identify the most discriminant parameters. Lastly, using ANOVA and correlations for all parameters, significant differences between diverse types of honey were examined.
Most of the samples' water content showed good maturity (98%) whilst pH values were in the range 3.50 - 4.21 confirming the good quality of the honeys analysed. Potassium was quantitatively the most relevant mineral (mean = 643 ppm), accounting for 79% of the total mineral content. The Ca, Na and Mg contents account for 14, 3 and 3% of the total mineral content respectively, while other minerals (Cu, Mn, Fe) were present at very low levels. PCA explained 75% or more of the variance with the first two PC variables. The variables with higher discrimination power according to the multivariate statistical procedure were Mg and pH. On the other hand, all samples of acacia and honeydew, and more than 90% of samples of multifloral type have been correctly classified using the LDA. ANOVA shows significant differences between diverse floral origins for all variables except sugar, moisture and Fe.
In general, the analytical results obtained for the Marche honeys indicate the products' high quality. The determination of physicochemical parameters and mineral content in combination with modern statistical techniques can be a useful tool for honey classification.
基于意大利马尔凯地区三种蜂蜜（刺槐蜜、多花蜂蜜、甘露蜜）的质量参数（pH值、糖分含量、湿度）和矿物质含量（钠、钾、钙、镁、铜、铁和锰）进行了特征分析。运用主成分分析（PCA）和线性判别分析（LDA）等模式识别方法对植物来源不同的蜂蜜样本进行分类，并确定最具判别力的参数。最后，通过对所有参数进行方差分析（ANOVA）和相关性分析，研究了不同类型蜂蜜之间的显著差异。
大多数样本的水分含量显示成熟度良好（98%），而pH值在3.50 - 4.21范围内，证实了所分析蜂蜜的良好品质。钾是定量上最相关的矿物质（平均值 = 643 ppm），占总矿物质含量的79%。钙、钠和镁的含量分别占总矿物质含量的14%、3%和3%，而其他矿物质（铜、锰、铁）的含量非常低。前两个主成分变量解释了75%或更多的方差。根据多元统计程序，具有较高判别力的变量是镁和pH值。另一方面，使用线性判别分析对所有刺槐蜜和甘露蜜样本以及90%以上的多花蜂蜜样本进行了正确分类。方差分析表明，除了糖分、水分和铁之外，所有变量在不同花卉来源之间存在显著差异。
总体而言，马尔凯蜂蜜的分析结果表明这些产品质量很高。结合现代统计技术测定理化参数和矿物质含量可成为蜂蜜分类的有用工具。
