Accumulation of potentially toxic elements in Chinese tea (Camellia sinensis): Towards source apportionment and health risk assessment

Efficient and simultaneous immobilization of fluoride and lead in water and tea garden soil by bayberry tannin foam loaded zirconium

Nowadays, human activities intensified the combined pollution of fluoride and lead in acidic tea garden soil. The key to eliminating this combined pollution is to immobilize pollutants simultaneously, thus preventing their migration from tea garden soil to tea trees. In this paper, the natural product bayberry tannin was employed as raw material to fabricate functional materials (TF-Zr) for simultaneous adsorption of fluorine (F) and lead (Pb) in water and soil by the reactivity of tannin with Pb2+ and the affinity of Zr with F. SEM-Mapping, EDS, FT-IR, XPS were utilized to probe the immobilization mechanisms. The results showed that TF-Zr could simultaneously and efficiently adsorb F- and Pb2+ from water with the adsorption capacity of 5.02 mg/g (Pb) and 4.55 mg/g (F). The adsorption processes were both in accordance with the proposed secondary kinetic adsorption model. Besides, the presence of F- promoted the adsorption of Pb2+ by TF-Zr. The materials were applied into tea garden soil to explore its effect on the variation of F and Pb forms in the soil. It was found that the proportion of water-soluble fluorine, exchangeable fluorine and exchangeable lead in the tea garden soil decreased significantly, while the proportion of residual fluorine and lead increased evidently, illustrating TF-Zr possessed eximious fixation effect on the highly reactive fluorine and lead in the soil and facilitated their conversion to the more stable residue state. Therefore, TF-Zr can be used for the efficient and simultaneous immobilization of fluorine and lead in water and tea garden soil.

Metal tolerance protein CsMTP4 has dual functions in maintaining zinc homeostasis in tea plant

Plants have evolved a series of zinc (Zn) homeostasis mechanisms to cope with the fluctuating Zn in the environment. How Zn is taken up, translocated and tolerate by tea plant remains unknown. In this study, on the basis of RNA-Sequencing, we isolated a plasma membrane-localized Metal Tolerance Protein (MTP) family member CsMTP4 from Zn-deficient tea plant roots and investigated its role in regulation of Zn homeostasis in tea plant. Heterologous expression of CsMTP4 specifically enhanced the tolerance of transgenic yeast to Zn excess. Moreover, overexpression of CsMTP4 in tea plant hairy roots stimulated Zn uptake under Zn deficiency. In addition, CsMTP4 promoted the growth of transgenic Arabidopsis plants by translocating Zn from roots to shoots under Zn deficiency and conferred the tolerance to Zn excess by enhancing the efflux of Zn from root cells. Transcriptome analysis of the CsMTP4 transgenic Arabidopsis found that the expression of Zn metabolism-related genes were differentially regulated compared with wild-type plants when exposed to Zn deficiency and excess conditions. This study provides a mechanistic understanding of Zn uptake and translocation in plants and a new strategy to improve phytoremediation efficiency.

Exploring tea and herbal infusions consumption patterns and behaviours: The case of Portuguese consumers

Consumption of tea and herbal infusions (THIs) have a long history in traditional medicine and cultural practices. The health-promoting benefits attributed to THIs are considered influential factors in consumer choices. However, there is limited data on consumer choices and attitudes that might interfere with the positive effects associated with THIs consumption. The aim of this study was to investigate the consumption pattern and behavior of THIs consumers in Portugal, assessing the influence of socio-demographic factors on the selection of THIs products and consumer practices related to these beverages. An online survey was conducted, and from the collected data, 720 responses met the aim of the study and were further analyzed. Most of the respondents were female, 74.4%, belonging to the 40-60 age group (40.6%) and were medium consumers of THIs (47.2%). Green tea was the most consumed type among participants, and its consumption was associated not only with age but also with the pattern of THIs consumption. Despite that, participants preferred herbal infusions, with citronella, chamomile, and lemon verbena being the most consumed types. For certain types of herbal infusions, consumption was associated with age, while other types were preferred by moderate or heavy consumers. Most participants purchased THIs in supermarkets, registered trademark and brand stores, in the form of THIs bag. Light consumers use only bag, while medium/heavy consumers indicated the use of other forms. Almost half of the respondents admitted to not reading the information on product labels before consumption and using THIs after the expiry date, while only one-third of them declared paying attention to the label instructions. This study revealed the impact of socio-demographic factors as age on the consumption patterns and preferences of THIs of consumers. Of concern is the neglect of label usage among Portuguese consumers. This emphasizes the urgency of implementing interventions to guide proper label use and promote good consumption practices to ensure the quality of THIs products.

A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms

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.