Blue light extensively regulates multiple physiological processes and secondary metabolism of plants. Although blue light quantity (fluence rate) is important for plant life, few studies have focused on the effects of different blue light intensity on plant secondary metabolism regulation, including tea plants. Here, we performed transcriptomic and metabolomic analyses of young tea shoots (one bud and two leaves) under three levels of supplemental blue light, including low-intensity blue light (LBL, 50 μmol m^–2 s^–1), medium-intensity blue light (MBL, 100 μmol m^–2 s^–1), and high-intensity blue light (HBL, 200 μmol m^–2 s^–1). The total number of differentially expressed genes (DEGs) in LBL, MBL and HBL was 1, 7 and 1097, respectively, indicating that high-intensity blue light comprehensively affects the transcription of tea plants. These DEGs were primarily annotated to the pathways of photosynthesis, lipid metabolism and flavonoid synthesis. In addition, the most abundant transcription factor (TF) families in DEGs were bHLH and MYB, which have been shown to be widely involved in the regulation of plant flavonoids. The significantly changed metabolites that we detected contained 15 lipids and 6 flavonoid components. Further weighted gene co-expression network analysis (WGCNA) indicated that CsMYB (TEA001045) may be a hub gene for the regulation of lipid and flavonoid metabolism by blue light. Our results may help to establish a foundation for future research investigating the regulation of woody plants by blue light.

Chinese jasmine tea is a type of flower-scented tea, which is produced by mixing green tea with the Jasminum sambac flower repeatedly. Both the total amount and composition of volatiles absorbed from the Jasminum sambac flower are mostly responsible for its sensory quality grade. This study aims to compare volatile organic compound (VOC) differences in authoritative jasmine tea grade samples. Automatic thermal desorption-gas-chromatography-mass spectrometry (ATD-GC-MS) and electronic nose (E-nose), followed by multivariate data analysis is conducted. Consequently, specific VOCs with a positive or negative correlation to the grades are screened out. Partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) show a satisfactory discriminant effect on rank. It is intriguing to find that the E-nose is good at distinguishing the grade difference caused by VOC concentrations but is deficient in identifying essential aromas that attribute to the unique characteristics of excellent grade jasmine tea.