Characterization of three different classes of non-fermented teas using untargeted metabolomics

Non-volatile metabolite and in vitro bioactivity differences in green, white, and black teas

Camellia sinensis var. assamica cultivars 'Zijuan' (ZJ, characterized by high anthocyanin content) and 'Mengku large-leaf' (LL, with high content of catechins) are widely consumed in China. Therefore, when processed into green, white, and black teas, differences in composition and biological activities should be detectable. The aim of this work was to explore these potential differences. To achieve that, in vitro bioactivity assays and metabolomics combined with correlation and ridge analyses were applied. Metabolomics revealed that the concentrations of theasinensins, anthocyanins, and amino acids in ZJ teas were higher than those in LL teas. Compared with green and white teas, black teas had higher concentrations of Amadori rearrangement products and theaflavins. Bioactivity assays showed ZJ teas had stronger bioactivity than LL teas. Catechins, procyanidins, and flavone glycosides were identified as key contributors to bioactivity differences rather than anthocyanins. These results suggested that ZJ was more suitable for making functional tea beverages.

Comprehensive investigation on the flavor difference in five types of tea from JMD (Camellia sinensis 'Jinmudan')

BACKGROUND

Jinmudan (JMD) is a high-aroma variety widely cultivated in China. The current study primarily focuses on the key volatile metabolites in JMD black and oolong teas, and investigates the impact of processing technologies on the aroma quality of JMD tea. However, few studies have explored the suitability of JMD for producing a certain type of tea or the characteristic quality differences among various JMD teas using multivariate statistical analysis methods.

RESULTS

The principal volatile metabolites contributing to the floral quality of JMD tea are linalool, geraniol, indole and phenethyl alcohol. In JMD black tea (BT), the key volatile metabolites include methyl salicylate, geraniol, (E)-β-ocimene and phenethyl alcohol. In JMD oolong tea (OT), the key volatile metabolites include indole, linalyl valerate and phenethyl alcohol. In JMD yellow tea (YT), the key volatile metabolites include methyl salicylate, geraniol and terpinolene. In JMD white tea (WT), the key volatile metabolites include methyl salicylate, geraniol and terpinolene. In JMD green tea (GT), the key volatile metabolites include (E)-β-ocimene, indole and geraniol. Comparative analysis and KEGG pathway enrichment analysis revealed that flavonoid biosynthesis is the primary metabolic pathway responsible for the taste differences among various tea types. GT exhibited higher levels of phloretin, dihydromyricetin and galangin. The contents of vitexin, tricetin in YT were relatively higher. The contents of aromadendrin and naringenin in BT were higher, while OT contained higher levels of kaempferol. Additionally, WT showed higher contents of 3-O-acetylpinobanksin and 3,5,7-pinobanksin.

CONCLUSION

This study explained the reasons for the quality differences of different JMD tea and provided a reliable theoretical basis for the adaptability of JMD tea. © 2024 Society of Chemical Industry.

Effects of processing technology on tea quality analyzed using high-resolution mass spectrometry-based metabolomics

Fixation is a crucial step in green tea processing that can impact quality. In this study, we explored the differences in the chemical components of steamed and fried green teas made from the same batch of fresh tea leaves using different fixing methods. Results showed that concentrations of sucrose and free amino acids were significantly higher in steamed green tea. Abundances of 12 compounds including purine nucleoside, pyrimidine nucleoside derivatives, and catechins were higher in fried green tea, while 34 compounds such as amino acids and their derivatives, benzofurans and flavonoids were higher in steamed green tea. Thus, steaming retained more compounds associated with sweet and fresh tastes, such as free amino acids, while frying produced more compounds with bitter tastes, such as catechin. This might explain why steamed green tea is mellower than fried tea.

Metabolomics investigation of the chemical variations in white teas with different producing areas and storage durations

In this study, we employed nontargeted metabolomics and quantitative analysis to explore the variations in metabolites among white teas from different production areas and with varying storage durations. A total of 83 compounds exhibited differential levels between Zhenghe and Fuding white tea, 89 between Zhenghe and Jinggu, and 75 between Fuding and Jinggu white tea. Concerning the storage of white tea, the concentrations of flavanols, dimeric catechins, and amino acids decreased over time, while N-ethyl-2-pyrrolidone-substituted flavanols (EPSFs), caffeine, adenosine monophosphate (AMP), and adenosine increased. Galloylated flavanols showed a higher propensity to form EPSFs with theanine compared to nongalloylated flavanols during storage. Theanine and epigallocatechin gallate were more inclined to generate S-configuration EPSFs during storage in Fuding and Jinggu white tea samples, while R-configuration EPSFs were more readily formed in Zhenghe white tea samples. This study offers a comprehensive understanding of the changes in metabolites during the storage of white tea.

Comprehensive applications of metabolomics on tea science and technology: Opportunities, hurdles, and perspectives

With the development of metabolomics analytical techniques, relevant studies have increased in recent decades. The procedures of metabolomics analysis mainly include sample preparation, data acquisition and pre-processing, multivariate statistical analysis, as well as maker compounds' identification. In the present review, we summarized the published articles of tea metabolomics regarding different analytical tools, such as mass spectrometry, nuclear magnetic resonance, ultraviolet-visible spectrometry, and Fourier transform infrared spectrometry. The metabolite variation of fresh tea leaves with different treatments, such as biotic/abiotic stress, horticultural measures, and nutritional supplies was reviewed. Furthermore, the changes of chemical composition of processed tea samples under different processing technologies were also profiled. Since the identification of critical or marker metabolites is a complicated task, we also discussed the procedure of metabolite identification to clarify the importance of omics data analysis. The present review provides a workflow diagram for tea metabolomics research and also the perspectives of related studies in the future.

Yellow tea: more than turning green leaves to yellow

Yellow tea (YT), a slightly-fermented tea originated from Ming Dynasty with distinctive "Three yellows," mild-sweet smell, and mellow taste attributed to the unique yellowing process. Based on current literature and our previous work, we aim to comprehensively illustrate the key processing procedures, characteristic chemical compounds, health benefits and applications, as well as the interlocking relationships among them. Yellowing is the most vital procedure anchored on the organoleptic quality, characteristic chemical components, and bioactivities of YT, which is influenced by temperature, moisture content, duration, and ventilation conditions. Pheophorbides, carotenoids, thearubigins and theabrownins are the major pigments contributing to the "three yellows" appearance. Alcohols, such as terpinol and nerol, are attributed to the refreshing and sweet aroma of bud and small-leaf YT, while heterocyclics and aromatics forming during roasting result in the crispy rice-like large-leaf YT. Hygrothermal effects and enzymatic reactions during yellowing result in the decline of astringent substances. Meanwhile, multiple bioactive compounds such as catechins, ellagitannins, and vitexin, endow YT with antioxidant, anti-metabolic syndrome, anti-cancer, gut microbiota regulation, and organ injury protection effects. Future studies focusing on the standard yellowing process technology, quality evaluation system, and functional factors and mechanisms, possible orientations, and perspectives are guaranteed.

Identification and Analysis of Metabolites That Contribute to the Formation of Distinctive Flavour Components of Laoxianghuang

In addition to volatile compounds, metabolites also have a great effect on the flavour of food. Fresh finger citron cannot be eaten directly because of its spicy and bitter taste, so it is made into a preserved fruit product known as Laoxianghuang (LXH). To investigate the metabolites that have an effect on the flavour of LXH, untargeted metabolomics was performed using an ultrahigh-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS), and the metabolites of the Laoxianghuang samples from different locations in the Chaoshan area were compared and analysed. A total of 756 metabolites were identified and distinct differences were revealed among the different Laoxianghuang samples. A total of 33 differential metabolites with the most significant changes were screened through further multivariate analytical steps, and each group of samples had unique metabolites. For instance, pomolic acid had the highest content in the JG sample, while L-glycyl-L-isoleucine was rich in the QS sample. Moreover, flavonoid metabolites made the greatest contribution to the unique flavour of Laoxianghuang. The metabolic pathways involved are the biosynthetic pathways of flavonoids, isoflavonoids, flavones, and flavonols. This study can provide some creative information for distinguishing the quality differences of Laoxianghuang from the perspective of metabolites and offer preliminary theoretical support to characterise the formation of flavour substances in Laoxianghuang.

Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm

In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R²) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R² values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.

HPLC-DAD fingerprints combined with chemometric techniques for the authentication of plucking seasons of Laoshan green tea

Laoshan green teas plucked in summer and autumn were measured by high performance liquid chromatography-diode array detector (HPLC-DAD). After baseline correction, the fingerprints data were resolved by multivariate curve resolution-alternating least squares (MCR-ALS) and a total of 57 components were acquired. Relative concentrations of these components were afterwards applied to distinguish plucking seasons using principal component analysis (PCA), support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA). For both SVM and PLS-DA models, the total recognition rates of training set, cross-validation and testing set were 100%, 91.3% and 100%, respectively. Besides, three variable selection methods were employed to determine characteristic components for the authentication of summer and autumn teas. Results showed that PLS-DA model based on three characteristic components selected by VIP possesses identical predictive ability as the original model. This study demonstrated that our proposed strategy is competent for the authentication of plucking seasons of Laoshan green tea.

Recent advances in the application of metabolomics for food safety control and food quality analyses

As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.