Polyphenols are one of the most important metabolites in tea due to their unique biological activities and health benefits,arousing great attention of researchers to investigate biochemical mechanisms of polyphenols d...Polyphenols are one of the most important metabolites in tea due to their unique biological activities and health benefits,arousing great attention of researchers to investigate biochemical mechanisms of polyphenols during tea plant growth,development and tea processing.Although omics has been used as a major analytical platform for tea polyphenol research with some proven merits,a single-omics strategy remains a considerable challenge due to the complexity of biological system and functional processes of tea in each stage of tea production.Recent advances in multi-omics approaches and data analysis have enabled mining and mapping of enormous number of datasets at different biological scales from genotypes to phenotypes of living organisms.These new technologies combining genomics,metagenomics,transcriptomics,proteomics and/or metabolomics can pave a new avenue to address fundamental questions regarding polyphenol formation and changes in tea plants and products.Here,we review recent progresses in single-and multi-omics approaches that have been used in the field of tea polyphenol studies.The perspectives on future research and applications for improvement of tea polyphenols as well as current challenges of multi-omics studies for tea polyphenols are also discussed.展开更多
In recent years, the popular multifractal detrended fluctuation analysis （MF-DFA） is extended to two-dimensional （2D） version, which has been applied in some field of image processing. In this paper, based on the ...In recent years, the popular multifractal detrended fluctuation analysis （MF-DFA） is extended to two-dimensional （2D） version, which has been applied in some field of image processing. In this paper, based on the 2D MF-DFA, a novel multifractal estimation method for images, which we called the local multifractal detrended fluctuation analysis （LMF-DFA）, is proposed to recognize and distinguish 20 types of tea breeds. A set of new multifractal descriptors, namely the local multifractal fluctuation exponents is defined to portray the local scaling properties of a surface. After collecting 10 tea leaves for each breed and photographing them to standard images, the LMF-DFA method is used to extract characteristic parameters for the images. Our analysis finds that there are significant differences among the different tea breeds＇ characteristic parameters by analysis of variance. Both the proposed LMF-DFA exponents and another classic parameter, namely the exponent based on capacity measure method have been used as features to distinguish the 20 tea breeds. The comparison results illustrate that the LMF-DFA estimation can differentiate the tea breeds more effectively and provide more satisfactory accuracy.展开更多
文摘Polyphenols are one of the most important metabolites in tea due to their unique biological activities and health benefits,arousing great attention of researchers to investigate biochemical mechanisms of polyphenols during tea plant growth,development and tea processing.Although omics has been used as a major analytical platform for tea polyphenol research with some proven merits,a single-omics strategy remains a considerable challenge due to the complexity of biological system and functional processes of tea in each stage of tea production.Recent advances in multi-omics approaches and data analysis have enabled mining and mapping of enormous number of datasets at different biological scales from genotypes to phenotypes of living organisms.These new technologies combining genomics,metagenomics,transcriptomics,proteomics and/or metabolomics can pave a new avenue to address fundamental questions regarding polyphenol formation and changes in tea plants and products.Here,we review recent progresses in single-and multi-omics approaches that have been used in the field of tea polyphenol studies.The perspectives on future research and applications for improvement of tea polyphenols as well as current challenges of multi-omics studies for tea polyphenols are also discussed.
文摘In recent years, the popular multifractal detrended fluctuation analysis （MF-DFA） is extended to two-dimensional （2D） version, which has been applied in some field of image processing. In this paper, based on the 2D MF-DFA, a novel multifractal estimation method for images, which we called the local multifractal detrended fluctuation analysis （LMF-DFA）, is proposed to recognize and distinguish 20 types of tea breeds. A set of new multifractal descriptors, namely the local multifractal fluctuation exponents is defined to portray the local scaling properties of a surface. After collecting 10 tea leaves for each breed and photographing them to standard images, the LMF-DFA method is used to extract characteristic parameters for the images. Our analysis finds that there are significant differences among the different tea breeds＇ characteristic parameters by analysis of variance. Both the proposed LMF-DFA exponents and another classic parameter, namely the exponent based on capacity measure method have been used as features to distinguish the 20 tea breeds. The comparison results illustrate that the LMF-DFA estimation can differentiate the tea breeds more effectively and provide more satisfactory accuracy.
