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Lipids are one of the most important bioactive compounds, affecting the character and quality of tea. However, the contribution of lipids to tea productions is still elusive. Here, we systematically identified the lipid profiles of green, oolong, and black teas in purple-leaf tea (Jinmingzao, JMZ) and green-leaf tea (Huangdan, HD), respectively.The lipids analysis showed regular accumulation in tea products with different manufacturing processes, among which the fatty acids, glycerolipids, glycerophospholipids, and sphingolipids contribute to the quality characteristics of tea products, including typical fatty acyl (FA), monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerols (DGDG), and phosphatidylcholine (PC). Compared tea materials with products, levels of fatty acids were up-regulated, while glycerolipids and glycerophospholipids were down-regulated in tea products. FA 18:3, FA 16:0, MGDG 36:6, DGDG 36:6, PC 34:3, and PC 36:6 were the negative contributors to green tea flavor formation of purple-leaf tea. The pathway analysis of significant lipids in materials and products of purple-leaf tea were enriched linolenic acid metabolism pathway and glycerolipid metabolism.This study provides insights into the lipid metabolism profiles of different tea leaf colors, and found that fatty acids are essential precursors of black tea flavor formation. © 2021 Society of Chemical Industry.

Mutual information (MI) is currently the most popular match metric in handling the registration problem for multi modality images. However, interpolation artifacts impose deteriorating effects to the accuracy and robustness of MI-based methods. This paper analyzes the generation mechanism of the artifacts inherent in linear partial volume interpolation (PVI) and shows that the mutual information resulted from PVI is a convex function within each voxel grid. We conclude that the generation of the artifacts is due to two facts: 1) linear interpolation causes the histogram bin values to change at a synchronized pace; 2) entropy computation function Σxlgx is convex. As a remedy we propose to use non-uniform interpolation functions as the interpolation kernels in estimating the joint histogram. Cubic B-splin and Gaussian interpolators are compared and we demonstrate the improvements via experiments on misalignments between CT/MR brain scans.