Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.

There is increasing interest in the production and consumption of tea (Camellia sinensis L.) processed from purple–leaved cultivar due to their high anthocyanin content and health benefits. However, how and why seasonal changes affect anthocyanin accumulation in young tea leaves still remains obscured. In this study, anthocyanin and abscisic acid (ABA) contents in young leaves of Zifuxing 1 (ZFX1), a cultivar with new shoots turning to purple in Wuyi Mountain, a key tea production region in China, were monitored over four seasons. Young leaves produced in September were highly purplish, which was accompanied with higher anthocyanin and ABA contents. Among the environmental factors, the light intensity in particular was closely correlated with anthocyanin and ABA contents. A shade experiment also indicated that anthocyanin content significantly decreased after 168 h growth under 75% shade, but ABA treatment under the shade conditions sustained anthocyanin content. To confirm the involvement of ABA in the modulation of anthocyanin accumulation, anthocyanin, carotenoids, chlorophyll, ABA, jasmonic acid (JA), and salicylic acid (SA) in the young leaves of four cultivars, including ZFX1, Zijuan (ZJ), wherein leaves are completely purple, Rougui (RG) and Fudingdabaicha (FDDB) wherein leaves are green, were analyzed, and antioxidant activities of the leaf extracts were tested. Results showed that ABA, not other tested hormones, was significantly correlated with anthocyanin accumulation in the purple–leaved cultivars. Cultivars with higher anthocyanin contents exhibited higher antioxidant activities. Subsequently, ZFX1 plants were grown under full sun and treated with ABA and fluridone (Flu), an ABA inhibitor. ABA treatment elevated anthocyanin level but decreased chlorophyll contents. The reverse was true to those treated with Flu. To pursue a better understanding of ABA involvement in anthocyanin accumulation, RNA–Seq was used to analyze transcript differences among ABA– or Flu–treated and untreated ZFX1 plants. Results indicated that the differentially expressed genes in ABA or Flu treatment were mainly ABA signal sensing and metabolism–related genes, anthocyanin accumulation-related genes, light–responsive genes, and key regulatory MYB transcription factors. Taking all the results into account, a model for anthocyanin accumulation in ZFX1 cultivar was proposed: high light intensity caused reactive oxygen stress, which triggered the biosynthesis of ABA; ABA interactions with transcription factors, such as MYB-enhanced anthocyanin biosynthesis limited chlorophyll and carotenoid accumulation; and transport of anthocyanin to vacuoles resulting in the young leaves of ZFX1 with purplish coloration. Further research is warranted to test this model.

Panyong Congou black tea (PCT) is one of the most representative and historically famous Congou black teas in China and has been gaining more and more attention for its beneficial health properties. Currently, four grades of PCT are available, based on the raw leaf materials and consumer palatability. The chemical profiles distinguishing different grades of PCT are yet to be defined, nor has the relationship with grade been evaluated. In the present study, chemometric analysis showed that epigallocatechin (EGC), catechin (C), polyphenols, gallic acid (GA), and free amino acids are grade related bio-markers of PCT. These compounds are associated with the sweet and mellow aftertaste of PCT. A total of 34 volatile components were identified, of which the three component types with the highest relative percentages were alcohols (51.34–52.51%), ketones (27.31–30.28%), and aldehydes (12.70–13.18%). Additionally, our results revealed that sweet floral and fruity aromas were positively correlated with six volatile organic compounds (VOCs), 1-pentanol, propyl hexanoate, linalool, cyclohexanone, hexanal, and 2,5-dimethylpyrazine. Clear discrimination was achieved using orthogonal projections to latent structures discriminant analysis (OPLS-DA). The findings provide vital information on the characteristic flavor of each grade of PCT.