Classifying honeys from the Soria Province of Spain via multivariate analysis
Classifying honeys from the Soria Province of Spain via multivariate analysis
复制标题
通过多变量分析对西班牙索里亚省的蜂蜜进行分类
DOI：
10.1007/s00216-005-3161-0
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发表时间：
2005
影响因子：
4.3
通讯作者：
M. Gómez
中科院分区：
文献类型：
--
作者：
M. J. N. Nalda;J. L. B. Yagüe;J. Calva;M. Gómez
通过对7种植物来源的73种蜂蜜[ling （Calluna vulgarisL.）、heather （Ericasp.）、rosemary （Rosmarinus officinalisL.）、thyymus vulgarisL.)、honeydew （Quercussp.）、spike lavender （Lavandula latifoliaM.）和french lavender (Lavandula stoechasL.)]的金属含量数据和其他常见的理化参数进行判别分析，对它们进行了分类。采用原子发射光谱（AES）和电感耦合等离子体原子发射光谱（ICP-AES）分别对K、Na和Mg、Ca、Al、Fe、Mn、Zn、B、Cu、Co、Cr、Ni、Cd和Pb等15种矿物进行了鉴定和定量。此外，根据国际蜂蜜委员会的统一方法分析了8个理化参数：灰分含量、水分、不溶性物质、还原糖、表观蔗糖、淀粉酶活性、游离酸度和羟甲基糠醛。用化学计量学对所分析的蜂蜜进行了表征和区分。方差分析显示，除了表观蔗糖、HMF、Fe和Zn外，所有变量的平均含量在蜂蜜之间存在显著差异。主成分分析被用作描述工具，在两个维度上可视化数据结构，发现变量和蜂蜜类型之间的关系。同样，判别分析与各种方法（逐步、向前和向后）一起用于选择具有最高判别能力的变量，这使我们能够对本工作中考虑的所有植物起源进行分类，在交叉验证后实现接近90%的全局成功率。
A total of 73 different honeys from seven botanical origins [ling (Calluna vulgarisL.), heather (Ericasp.), rosemary (Rosmarinus officinalisL.), thyme (Thymus vulgarisL.), honeydew (Quercussp.), spike lavender (Lavandula latifoliaM.) and french lavender (Lavandula stoechasL.)] have been classified by applying discriminant analysis to their metal content data and other common physicochemical parameters. Fifteen minerals were identified and quantified using atomic emission spectroscopy (AES) for K and Na, and inductively coupled plasma atomic emission spectrometry (ICP-AES) for Mg, Ca, Al, Fe, Mn, Zn, B, Cu, Co, Cr, Ni, Cd and Pb. Moreover, eight physicochemical parameters were analysed following the Harmonised Methods of the International Honey Commision: ash content, moisture, insoluble matter, reducing sugars, apparent sucrose, diastase activity, free acidity and hydroxymethylfurfural. The honeys analysed were characterised and distinguished using chemometrics. ANOVA highlighted significant differences between the honeys in terms of the mean contents of all variables except apparent sucrose, HMF, Fe and Zn. Principal component analysis was used as a descriptive tool to visualise the data structure in two dimensions, finding relationships between variables and types of honey. Likewise, discriminant analysis, together with various methods (stepwise, forward and backward), was used to select the variables with the highest discriminating power, which allowed us to classify all of the botanical origins considered in this work, achieving a global success rate close to 90% following cross-validation.