Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves.

ABSTRACT: Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the

SUBMITTER: Yamashita H 

PROVIDER: S-EPMC7892543 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature