Main text: The CCQM Isotope Ratio Working Group (IRWG) determined that an additional key comparison of carbon isotope delta measurements was required to capture the progress of this field. Vials containing 0.25 mg of vanillin were prepared at NRC, and evaluated for bottle-to-bottle homogeneity prior to distribution to the eight participating institutes. Participants were able to choose any suitable method and reference materials for carbon isotope delta measurements, and report a carbon isotope delta value and the associated uncertainty, and analysis details. To determine the key comparison reference value (KCRV) and its associated uncertainty, the NRC in collaboration with the NIST, developed a multivariate Bayesian random laboratory effects model, which also incorporates the uncertainty due to bottle-to-bottle homogeneity and any correlations between the reported results that arise due to the use of common reference materials. The KCRV for this study was determined to be -25.833 ‰ relative to the VPDB, with associated uncertainty of 0.028 ‰, and expanded uncertainty of 0.056 ‰ (k=2). All the results reported by the participants in this study were considered equivalent to the KCRV. To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/. The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Developing a robust and reliable geographical origin discrimination model, unaffected by grape varieties, wine types, or the scale and distance of the wine‐growing area, is essential for the sustainable growth of China’s wine industry. A total of 967 wine samples were collected from 10 original regions in China, and 27 elemental profiles and two stable isotope ratios were determined by ICP‐MS and IRMS, respectively. PCA was used to evaluate the contributions of 29 regional markers in classifying the geographical regions of Chinese wine. Six chemometric methods were employed, including PCA, PLS‐DA, RF, ANN, SVM, and R‐Part, to verify the regions and subregions of Chinese wine. This study successfully proposed advanced simultaneous classification models for original regions and subregions of Chinese wine using multielements and stable ratios combined with random forest (100% accuracy) for the first time.