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 ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.

Plant phenotyping relevance

新鮮茶葉の代謝物を非破壊推定する分光センシングと機械学習モデルの開発・評価が研究の中心であり、植物形質の取得手法に該当する。

Code and data availability

The paper describes hyperspectral reflectance data (~200 tea leaves) and 15 metabolite traits used to build regression models, but no public deposit of the phenotype/spectral datasets, images, analysis code, or trained models is stated. Supplementary materials contain only result tables/figures, not the underlying data

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