Investigation of the geographical provenance of the beer available in South Korea using multielements and isotopes

Discrimination of geographical origin of hop (Humulus lupulus L.) using geochemical elements combined with statistical analysis

Beer is a popular alcoholic beverage worldwide, traditionally made from water, barley and hop (Humulus lupulus L.) strobili. The strobili contain lupulin glands whose components (mostly bitter acids and polyphenols) confer unique and locally different flavours to beer types. It is therefore relevant for brewers and consumers to precisely know the geographical origin of hop plants used for high-quality beer. Hop plants belonging to the variety Hallertau Perle, grown in two locations, Cavalese and Imèr, of the Trentino Region (Italy) were analysed to establish a direct relationship between the chemical elements detected in soil and in plant parts. Chemical elements were determined by X-ray fluorescence and inductively coupled plasma mass spectrometry in soil, leaf and strobili samples from Cavalese and Imèr. The data from the two areas were compared by a nonparametric test (Mann-Whitney) and multivariate statistics (principal component analysis and partial least squares discriminant analysis). The geochemical characterization and the statistical analyses showed different concentrations of major and trace elements in soil and plant parts from the two areas. A reliable correlation could be established between some elements in soil and strobili samples, that is Nb, Fe, Rb and Zr for Cavalese and Mg, Ni, Zn and Zr for Imèr. These elements could therefore be used as geochemical fingerprints to identify the geographical origin of strobili from the two study areas, an approach useful to verify the origin of hop plants for the production of high-quality beer.

Elemental profile and oxygen isotope ratio (δ18O) for verifying the geographical origin of Chinese wines

The elemental profile and oxygen isotope ratio (δ¹⁸O) of 188 wine samples collected from the Changji, Mile, and Changli regions in China were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma optical emission spectroscopy (ICP-OES) and isotope ratio mass spectrometry (IRMS), respectively. By combining the data of δ¹⁸O and the concentration data of 52 elements, the analysis of variance (ANOVA) technique was firstly applied to obtain the important descriptors for the discrimination of the three geographical origins. Ca, Al, Mg, B, Fe, K, Rb, Mn, Na, P, Co, Ga, As, Sr, and δ¹⁸O were identified as the key explanatory factors. In the second step, the key elements were employed as input variables for the subsequent partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) analyses. Then, cross validation and random data splitting (training set: test set = 70:30, %) were performed to avoid the over-fitting problem. The average correct classification rates of the PLS-DA and SVM models for the training set were both 98%, while for the test set, these values were 95%, 97%, respectively. Thus, it was suggested that the combination of oxygen isotope ratio (δ¹⁸O) and elemental profile with multi-step multivariate analysis is a promising approach for the verification of the considered three geographical origins of Chinese wines.