Formosa Jean Paul, Lia Frederick, Mifsud David, Farrugia Claude
Department of Chemistry, University of Malta, Msida, 2080 MSD, Malta;
Department of Rural Sciences and Food Systems, University of Malta, Msida, 2080 MSD, Malta.
Foods. 2020 Jun 1;9(6):710. doi: 10.3390/foods9060710.
Maltese honey has been produced, marketed, and sold as an exclusive local gourmet food product for countless years. Yet, thus far, no study has evaluated the individuality of this local food product. The evaluation of the parameters and properties which characterise the provenance and floral source of honey have been the subject of various studies worldwide, owing to the price and potential beneficial properties of this food product. Models analysing the potential of attenuated total reflection mid-infrared (ATR-FT-MIR) spectroscopy in discriminating and classifying local honey from that of foreign origin were investigated using 21 Maltese honey samples and 49 honey samples collected from abroad (Sicily, Greece, Sweden, Italy, France, Estonia and other samples of mixed geographical origin). Through a combination of spectroscopic techniques, spectral transformations, variable selection and partial least squares discriminant analysis (PLS-DA), chemometric models which successfully classified the provenance of local and non-local honey were developed. The results of these models were also corroborated with other classification and pattern recognition techniques, such as linear discriminate analysis (LDA), support vector machines (SVM) and feed-forward artificial neural networks (FF-ANN).
马耳他蜂蜜作为一种独特的本地美食产品，已经生产、销售了无数年。然而，迄今为止，尚未有研究对这种本地食品的独特性进行评估。由于蜂蜜这种食品的价格及其潜在的有益特性，对表征蜂蜜来源和花蜜来源的参数及特性进行评估已成为全球各种研究的主题。利用21个马耳他蜂蜜样本和49个从国外采集的蜂蜜样本（西西里岛、希腊、瑞典、意大利、法国、爱沙尼亚以及其他混合地理来源的样本），研究了分析衰减全反射中红外（ATR-FT-MIR）光谱在区分和分类本地蜂蜜与外国蜂蜜方面潜力的模型。通过光谱技术、光谱变换、变量选择和偏最小二乘判别分析（PLS-DA）的组合，开发出了成功对本地和非本地蜂蜜来源进行分类的化学计量学模型。这些模型的结果还通过其他分类和模式识别技术得到了证实，如线性判别分析（LDA）、支持向量机（SVM）和前馈人工神经网络（FF-ANN）。
