No abstract

Pu-erh tea processed from the sun-dried green tea leaves can be divided into ancient tea (AT) and terrace tea (TT) according to the source of raw material. However, their similar appearance makes AT present low market identification, resulting in a disruption in the tea market rules of fair trade. Therefore, this study analyzed the classification by principal component analysis/hierarchical clustering analysis and conducted the discriminant model through stepwise Fisher discriminant analysis and decision tree analysis based on the contents of water extract, phenolic components, caffeine, and amino acids, aiming to investigate whether phytochemicals coupled with chemometric analyses distinguish AT and TT. Results showed that there were good separations between AT and TT, which was caused by 16 components with significant (p < 0.05) differences. The discriminant model of AT and TT was established based on six discriminant variables including water extract, (+)-catechin, (−)-epicatechin, (−)-epigallocatechin, theacrine, and theanine. Among them, water extract comprised multiple soluble solids, representing the thickness of tea infusion. The model had good generalization capability with 100% of performance indexes according to scores of the training set and model set. In conclusion, phytochemicals coupled with chemometrics analyses are a good approach for the identification of different raw materials.

Methods currently used for the quantitative determination of total lipids and fatty acid composition in plant require solvent extraction. This study examined the solvent extraction of purple medic seed oil. Results varied with different extraction conditions. The effects of temperature, extraction time, sample size and solvent type on the extraction efficiency and oil quality were studied. The amount of extract collected was highest when ether absolute was used as a solvent for solvent extraction.