Results 91 to 100 of about 6,879 (152)

Digital Sensing for Comprehensive Tea Quality Evaluation: From Dry Tea to Tea Infusion and Infused Leaves. [PDF]

Compr Rev Food Sci Food Saf
Zheng M, Tian L, Mei W, Li X, Li D, Jie W, Chen X, Chen Q, Chen Q.   +8 more
europepmc   +1 more source

Active learning for data-efficient segmentation of tea buds and leaves. [PDF]

Front Plant Sci
Wang Z, Liao W, Li Y, Xiong Z, Wang F, Hu K, You H, Xiao Q.   +7 more
europepmc   +1 more source

Influence of Light Quality During Spreading on the Chemical and Aromatic Profile of Summer Green Tea. [PDF]

Int J Mol Sci
Xia X, Lin L, Xu F, Liu Y, Zhao Y, Xu W.
europepmc   +1 more source

Study on the Functional, Physiological, and Sensory Properties of Coffee Leaf Dark Tea Processed Using the Pile Fermentation Method of Pu-Erh Tea. [PDF]

Foods
Wang Y, Zhang K, Xiong G, Du P, Dong J, Li J, Liu X, Chen Z.   +7 more
europepmc   +1 more source

Phenolic Acids and Their Derivatives in Tea: Chemical Diversity, Processing Changes, Sensory Contributions, and Health Implications-A Comprehensive Review. [PDF]

Compr Rev Food Sci Food Saf
Jiang C, Ke JP, Wang Z, Lai G, Liu X, Han Z, Zhang L, Ho CT, Granato D.   +8 more
europepmc   +1 more source

Impact of processing techniques on chemical compositions and sensory attributes across five types of Yunnan large-leaf teas. [PDF]

Food Chem X
Dang X, Li Y, Lv Z, Lu F, Li W, Zhang Y, Shi X, Yi G, Duan H, Hou Y.   +9 more
europepmc   +1 more source

High-efficiency callus induction from intact Camellia sinensis leaves via controlled incisions and its regulatory mechanisms. [PDF]

BMC Plant Biol
Lili T, Juan L, Jian An H.
europepmc   +1 more source

Coleaf, an image recognition-driven approach facilitates the genome-wide association study with tea leaf morphology. [PDF]

Plant Methods
Jiang M, Chen S, Kong W, Wang Y, Qu S, Liao Z, García P, Noor-Ul-Áin, Zhao Q, Tang H, Zhang X.   +10 more
europepmc   +1 more source

Design and Experimental Analysis of a Multi-Scale Fresh Tea Leaf Rotary Drum Screen Based on the DEM-RSM Method. [PDF]

Food Sci Nutr
Zhang X, Wang R, Zhou Z.
europepmc   +1 more source

TEA-LWNet: a lightweight multispectral feature enhancement network based on RGB-NIR fusion for detection of tea leaf diseases and pests. [PDF]

Front Plant Sci
Wang Z, Wang F, Lin H.
europepmc   +1 more source