Scented green tea (Camellia sinensis) is a type of reprocessed green tea produced by scenting with flowers. To investigate the differences in the volatiles of scented green tea processed with four different flowers (Jasminum sambac, Osmanthus fragrans, Michelia alba, and Rosa rugosa), gas chromatography–ion mobility spectrometry (GC–IMS) and gas chromatography–mass spectrometry (GC–MS) were employed to detect and identify the volatile compounds in the four types of scented teas. GC–IMS and GC–MS identified 108 and 101 volatile compounds, respectively. The key characteristic volatile compounds, namely indole, linalool, β-myrcene, benzyl acetate, and ethyl benzoate (jasmine tea); cedrol, (E)-β-ionone, γ-decalactone, and dihydro-β-ionol (osmanthus tea); geraniol, phenylethyl alcohol, jasmone, methyl jasmonate, hexadecanoic acid, 4-ethyl-benzaldehyde, 2-methylbutyl hexanoate, and indole (michelia tea); and 3,5-dimethoxytoluene, (E)-β-ionone, and 2-methylbutyl hexanoate (rose tea), were identified through chemometric analysis combined with relative odor activity values (ROAVs) and sensory evaluation. This study provides new insights into the formation of aroma molecular fingerprints during green tea scenting with flowers, providing theoretical guidance for infusing distinct aroma characteristics into green tea during scented tea processing.

Pyrolysis experiments of pine nut shells (PNSs) were carried out in a screw reactor at 300–700°C with approximately 98% energy yields. The analysis results showed that higher temperatures result in elevated heating values (HVs) of products but reduced biochar and bio-oil yields. The HVs of pyrolytic gas were remarkably influenced by proportions of CH₄ and H₂. Bio-oil of PNSs consisted primarily of phenols, acids, aldehydes, ketones, furans, and aromatic hydrocarbons. Five hundred degree Celsius was considered as optimal temperature for PNS pyrolysis, and biochar was the primary product with a yield of 37.11% and better Cu²⁺, Ni²⁺, and Zn²⁺ adsorption properties.

Premium green tea is a high-value agricultural product significantly influenced by its geographical origin, making it susceptible to food fraud. This study utilized nuclear magnetic resonance (NMR) spectroscopy to perform chemical fingerprint analysis on 78 Longjing tea (LJT) samples from both protected designation of origin (PDO) regions (Zhejiang) and non-PDO regions (Sichuan, Guangxi, and Guizhou) in China. Unsupervised algorithms and heatmaps were employed for the visual analysis of the data from PDO and non-PDO teas while exploring the feasibility of linear and nonlinear machine-learning algorithms in discriminating the origin of LJT. The findings revealed that the nonlinear model random forest (92.2%), exhibited superior performance compared to the linear model linear discriminant analysis (85.6%). The random forest model identified 15 key marker metabolites for the geographical origin of LJT, such as kaempferol glycoside, glutamine, and ECG. The results support the conclusion that the integration of NMR with machine-learning classification serves as an effective tool for the quality assessment and origin identification of LJT.