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

J Food Drug Anal. 2018 Jul;26(3):1033-1044. doi: 10.1016/j.jfda.2017.12.009. Epub 2018 Jan 18.

Abstract

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.

Keywords: Elemental profile; Geographical origin; Isotope ratio (δ(18)O); Multivariate analysis; Oxygen.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Discriminant Analysis
  • Geography
  • Mass Spectrometry
  • Oxygen Isotopes / analysis*
  • Trace Elements / chemistry
  • Wine / analysis*

Substances

  • Oxygen Isotopes
  • Trace Elements

Grants and funding

This research was supported by the International S&T Cooperation Program of China (Grant No. 2015DFA31720), the National Science and Technology Project of the Ministry of Science and Technology in the 13th Five-Year Plan Period (Grant No. 2016YFF0203903), the National Natural Science Foundation of China (Grant Nos. 31601553 and 31601580), the National Standardization Management Committee of China (Grant No. 20101051-T-607), the 2016 China and Beijing Nova Plan of Science and Technology (Grant No. Z161100004916122), the Key Program for International S&T Cooperation Projects of China (2016YFE0113200), EU-China-Safe Horizon 2020 Project (Grant No. 727864). The authors are also thankful to the China Scholarship Council (CSC) for providing financial support for first author’s study as a joint PhD student in Germany.