Development of simple identification models for four main catechins and caffeine in fresh green tea leaf based on visible and near-infrared spectroscopy

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

“…2 ). The CV of EGCG (33.7%), ECG (64.7%), EGC (66.8%), EC (78.8%), caffeine (37.4%) in this study were higher than these (EGCG, 24.2%; ECG, 24.3%; EGC, 34.7%; EC, 14.0%; caffeine, 16.7%) in the previous study 35 . These results indicate that present datasets are suitable for robust regression modelling.…”

“…The NIR-based estimation of some chemical components in ground tea leaves has been established by previous studies 32 – 34 . Few studies have been reported in a non-destructive method for fresh leaves 35 , 36 . Huang et al 35 have reported non-destructive estimation methods for four main catechins and caffeine in fresh green leaves based on VIS–NIR spectra (400–2498 nm) and partial least squares (PLS) model.…”

“…Few studies have been reported in a non-destructive method for fresh leaves 35 , 36 . Huang et al 35 have reported non-destructive estimation methods for four main catechins and caffeine in fresh green leaves based on VIS–NIR spectra (400–2498 nm) and partial least squares (PLS) model. However, the outcomes of this study were limited by fewer tea quality-related metabolites and the sample status from leaf positions and fewer tea quality-related metabolites, which cannot achieve robust results in actual agricultural management.…”

Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis‐NIR spectroscopy

“…Y. Huang et al. (2020) used Vis‐NIR spectroscopy combined with chemometrics as a rapid, simple, and nondestructive approach for the prediction of four main catechin and caffeine contents of tea leaves in three varieties and six leaf positions and built simple detection models for these constituents. MLR models based on characteristic wavelengths had also good prediction performance with greater than 0.893.…”

“…The distribution of all the samples under different tea processing steps in the first three principal component space Lee, Hwang, et al (2014) 2,142 C-H stretching plus C=C stretching Lee, Hwang, et al (2014) 2,486 CH 2 Lee, Hwang, et al (2014) ECG 1,442 C-H stretching and C-H deformation Bian et al 20131,906 O-H stretching first overtone Lee, Hwang, et al (2014) 1,946 O-H stretching and HOH transformation Mark and Workman, (2007) 2,060 N-H asymmetric stretching Lee, Hwang, et al (2014) 2,142 C-H stretching plus C=C stretching Lee, Hwang, et al (2014) 2,486 CH 2 Lee, Hwang, et al (2014) EGC 806 C-H third overtone Osborne (1986) 1,108 C-H stretching second overtone Bian et al (2013) 1,444 C-H stretching and C-H deformation Bian et al (2013) 2,058 N-H asymmetric stretching Lee, Hwang, et al (2014) 2,250 N-H stretching and NH 3 deformation Huang et al 20202,486 CH 2 Lee, Hwang, et al (2014) EGCG 2,060 N-H asymmetric stretching Lee, Hwang, et al (2014) 2,248 N-H stretching and NH 3 deformation Huang et al 20202,486 CH 2 Lee, Hwang, et al (2014) GCG 1,446 C-H stretching and C-H deformation Bian et al (2013) 2,064 N-H asymmetric stretching Lee, Hwang, et al (2014) 2,242 N-H stretching and NH 3 deformation Huang et al (2020) CAF 1,446 C-H stretching and C-H deformation Bian et al (2013) 1,924 O-H stretching first overtone Lee, Hwang, et al (2014) 2,046 N-H asymmetric stretching Lee, Hwang, et al (2014) 2,242 N-H stretching and NH 3 deformation Huang et al 2020GA 806 C-H third overtone Osborne (1986) F I G U R E 7 Distributions of the characteristic wavelengths selected by SPA for ten constituents in tea between the content of tea polyphenols and caffeine and spectral responses. PLS models were established by taking the data of the whole spectral range as independent variables and each constituent as dependent variable.…”