| Literature DB >> 33409909 |
Jian Zhang1,2, Ruidong Yang3, Yuncong C Li4, Xinran Ni1.
Abstract
The combination of mineral multi-elements with chemometrics can effectively trace the geographical origin of tea (Camellia sinensis). However, the role of soil mineral multi-elements in discriminating the origin of tea was unknown. This study aimed to further validate whether the geographical origin of tea can be authenticated based on mineral multi-elements, the concentrations of which in tea leaves were significantly correlated with those in soil. Eighty-seven tea leaves samples and paired soils from Meitan and Fenggang (MTFG), Anshun, and Leishan in China were sampled, and 24 mineral elements were measured. The data were processed using one-way analysis of variance (ANOVA), Pearson correlation analysis, principal component analysis (PCA), and stepwise linear discriminant analysis (SLDA). Results indicated that tea and soil samples from different origins differed significantly (p < 0.05) in terms of most mineral multi-elemental concentrations. Conversely, the intra-regional differences of different cultivars of the same origin were relatively minor. Seventeen mineral elements in tea leaves were significantly correlated with those in soils. The SLDA model, based on the 17 aforementioned elements, produced a 98.85% accurate classification rate. In addition, the origin was also identified satisfactorily with 94.25% accuracy when considering the cultivar effect. In conclusion, the tea plant cultivars unaffected the accuracy of the discrimination rate. The geographical origin of tea could be authenticated based on the mineral multi-elements with significant correlation between tea leaves and soils. Soil mineral multi-elements played an important role in identifying the geographical origin of tea.Entities:
Keywords: Cultivated soils; Mineral multi-elements; Pearson correlation analysis; Stepwise linear discriminant analysis; Tea leaves
Year: 2021 PMID: 33409909 DOI: 10.1007/s12011-020-02527-8
Source DB: PubMed Journal: Biol Trace Elem Res ISSN: 0163-4984 Impact factor: 3.738