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).

中文翻译：

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ATR-FT-MIR在追踪马耳他群岛生产的蜂蜜的地理起源中的应用。

马耳他蜂蜜作为当地独家美食产品的生产，销售和销售已有数年之久。但是，到目前为止，还没有研究评估这种本地食品的个性。由于该食品的价格和潜在的有益特性，表征蜂蜜的来源和花香来源的参数和特性的评估已成为全世界各种研究的主题。使用21种马耳他蜂蜜样品和49种从国外（西西里岛，希腊，瑞典）收集的蜂蜜样品，研究了分析全衰减中红外（ATR-FT-MIR）光谱法对本地蜂蜜与外国蜂蜜进行区分和分析的模型。 ，意大利，法国，爱沙尼亚等地域混合的样本）。通过结合光谱技术，开发了光谱变换，变量选择和偏最小二乘判别分析（PLS-DA），成功分类了本地和非本地蜂蜜出处的化学计量模型。这些模型的结果还得到其他分类和模式识别技术的证实，例如线性判别分析（LDA），支持向量机（SVM）和前馈人工神经网络（FF-ANN）。