Study of assessment of green tea’ grades in GC‐MS determination of aromatic components based on principal component analysis (PCA)

Abstract

Aroma compounds are one of the most important components in tea, as well as an important indicator for its quality. In this paper, using HS‐SPME/GC‐MS method, we established the fingerprint for aromatic components in green tea produced in Guizhou Province, China. In this fingerprint, there are nine volatile aromatic components which have significant correlation with the quality scores of green teas’ aroma (p < .05). They contain ethers and aldehydes (dimethyl sulfide and phenylacetaldehyde) of aromatic compounds, and alcohols and esters (1‐penten‐3‐ol, linalool, phenethyl alcohol, geraniol, and Methyl salicylate) of aliphatic compounds, and pyrrole compounds of heterocyclic compounds (3‐ethyl‐1‐pyrrole and 1‐ethyl‐2‐carbaldehyde‐1‐pyrrole). The Regression Model has been established for predicting evaluation scores of green teas’ aroma. A good result was obtained via verification, indicating that GC‐MS is a feasible way to analyze key volatile aromatic components in green tea, which also can be used to predict quality scores aroma and different grades. Practical applications The aromatic components of tea are considered as the important indicator for evaluating the quality of green tea. We can use GC‐MS and other technologies to determine the representative aroma substances in green tea that can evaluate the quality of green tea. In this way, we can use the instrumental analysis method of the aromatic components to assist the traditional sensory evaluation, which make the green tea quality evaluation results more scientific.

Combined metabolic analyses for the biosynthesis pathway of l-threonine in Escherichia coli

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“…The current methods of producing Thr mainly include hydrolysis of animal proteins and microbial fermentation. Compared with the former, the microbial fermentation method has the advantages of low cost and low pollution (Li et al, 2021;). Among the Thr-producing strains, E. coli is the main host,…”

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

“…Therefore, we used the seven above-mentioned variable screening algorithms to extract the characteristic wavelengths. After variable screening, PCA was conducted to perform quadratic dimensional reduction compression on the selected data and to optimize the model’s accuracy [ 17 ]. The characteristic wavelengths screened for different endoplasmic components are shown in Table 1 .…”

“…The input of the principal components of the models was determined using minimal root-mean-square error cross-validation (RMSECV). The evaluation indices included root-mean-square error of prediction (RMSEP), correlation coefficient of calibration set (Rc), correlation coefficient of predication set (Rp), and relative percentage deviation (RPD) [ 17 ]. Usually, the closer the RMSECV and RMSEP values, the better the generalization performance of a model.…”