Predication of Japanese green tea (Sen-cha) ranking by volatile profiling using gas chromatography mass spectrometry and multivariate analysis

The sensory quality ranking of Japanese green tea (Sen-cha) was evaluated and predicted using volatile profiling and multivariate data analyses. The volatile constituents were extracted from tea infusion using vacuum hydrodistillation and analyzed using GC/MS. A quality of green tea could be discriminated to a high or low grade regarding the volatile profile by partial least squares discriminant analysis (PLS-DA). A quality ranking predictive model was developed from the relationship between subjective attributes (sensory quality ranking) and objective attributes (volatile profile) using partial least squares projections to latent structures together with the preprocessing filtering technique, orthogonal signal correction (OSC). Several volatile compounds highly contributed to model prediction were identified as various odor-active compounds, including geraniol, indole, linalool, cis-jasmone, dihydroactinidiolide, 6-chloroindole, methyl jasmonate, coumarin, trans-geranylacetone, linalool oxides, 5,6-epoxy-β-ionone, phytol, and phenylethyl alcohol. The whole fingerprints of these volatile compounds could be possible markers for the overall quality evaluation of green tea beverage.