Consistent aroma characteristics are important for tea products. However, understanding the formation of tea aroma flavor and correspondingly proposing applicable protocols to control tea quality and consistency remain major challenges. Oolong tea is one of the most popular teas with a distinct flavor. Generally, oolong tea is processed with the leaves of tea trees belonging to different subspecies and grown in significantly different regions. In this study, Yunnan and Fujian oolong teas, green tea, black tea, and Pu-erh tea were collected from major tea estates across China. Their sensory evaluation, main water-soluble and volatile compounds were identified and measured. The sensory evaluation, total polysaccharide, caffeine, and catechin content of Yunnan oolong tea was found to be different from that of Fujian oolong tea, a result suggesting that the kinds of tea leaves used in Yunnan and Fujian oolong teas were naturally different. However, according to their aroma compounds, principal component analysis (PCA) and cluster analysis (CA) of the volatile compounds showed that the two types of oolong teas were similar and cannot be clearly distinguished from each other; they are also different from green, black, and Pu-erh teas, a result indicating that the same oolong tea processing technology applied to different tea leaves results in consistent aroma characteristics. The PCA analysis results also indicated that benzylalcohol, indole, safranal, linalool oxides, β-ionone, and hexadecanoic acid methyl ester highly contributed to the distinct aroma of oolong tea compared with the other three types of teas. This study proved that the use of the same processing technology on two kinds of tea leaves resulted in a highly consistent tea aroma.

Modern instrumental analysis technology can provide various chemical data and information on tea samples. Unfortunately, it remains difficult to extract the useful information. We describe the use of chemical fingerprint similarities, combined with principal component and cluster analyses, to distinguish and recognize Pu-erh green teas, which from two tea mountains, Wuliang and Jingmai, in the Pu-erh district of Yunnan province. The volatile components of all 20 Pu-erh green teas (10 Wuliang and 10 Jingmai teas) were extracted and identified by headspace solid-phase micro extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). Sixty-three volatiles (including alcohols, hydrocarbons, ketones, and aldehydes) were identified in the 20 Pu-erh green teas, and differences in compound compositions between them were also observed. Through fingerprint similarity, combined with principal component and cluster analyses, the 20 Pu-erh green teas were differentiated successfully based on their volatile characteristics. This study demonstrates that the GC-MS combined with chemical fingerprint and unsupervised pattern recognition method is suitable for the investigation of the volatile profiling and evaluating the quality and authenticity of teas related to the different origins.

Soybean oil (SO) and rapeseed oil (RO) are important vegetable oils widely used in food and food preparation. However, the multi-omics profile changes in these edible oils remain unclear. This study investigated the effects of varieties and processing on oil compounds. In total, 1033 widely targeted metabolites, 349 lipid compounds, and 157 volatile organic metabolites were identified in the samples. Chemometric analysis revealed that the number of differential metabolites was 664, 857, 204, and 812 in SO_1stg vs. RO_1stg, SO_3rdg vs. RO_3rdg, SO_3rdg vs. SO_1stg, and RO_3rdg vs. RO_1stg, respectively. Notably, upregulated differential metabolites accounted for 92.73% of the total differential metabolites in the comparison of RO_3rdg vs. RO_1stg, and 79.41% in the comparison of SO_3rdg vs. SO_1stg. This study provides data support and a theoretical basis for constructing regulatory systems in the edible plant oil (EPO) and fat industry.