The Impact of Harvest Season on Oolong Tea Aroma Profile and Quality

Abstract

The impact of seasonality on the aroma quality of tea has been documented in various tea types, but not specifically in oolong tea. This study is the first to explore the complex relationships between seasonality, volatile compounds, and aroma quality in oolong tea. Using Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS)-based untargeted metabolomics, we analyzed 266 samples of Tieguanyin oolong tea. The data identified linalool, linalool oxides (trans-linalool oxide (furanoid) and trans-linalool oxide (pyranoid)), and their metabolites (diendiol I; hotrienol) as key seasonal discriminants. Four out of the top ten key differential compounds for distinguishing aroma scores were metabolites from fatty acid degradation, namely trans-3-hexenyl butyrate, trans-2-hexenyl hexanoate, hexyl hexanoate, and hexyl 2-methyl butyrate. Approximately one-fifth of the seasonal discriminant volatile compounds were significant in influencing aroma quality. Overall, the impact of seasonality on the aroma quality of finished Tieguanyin oolong tea is marginal. These findings enhance our understanding of the interplay between seasonal variations, volatile composition, and aroma quality in oolong tea.

Keywords: HS-SPME-GC-MS; aroma quality; oolong tea; season; untargeted metabolomics.

Figure 1

The aromatic profiles (A) of different seasons of light-scented Tieguanyin tea; OPLS-DA score plot (B) of finished light-scented Tieguanyin tea made from spring-harvested (n = 73 × 3) and autumn harvested (n = 60 × 3) leaves; heat map (C) of normalized accumulation levels of 64 differential metabolites between spring- and autumn-harvested light-scented Tieguanyin using OPLS-DA with VIP > 1.5, p < 0.05.

Figure 2

Machine learning model performance for seasonal classification of light-scented Tieguanyin tea using 133 samples and 181 volatile features. The confusion matrix (A) and the classification report (B) of the validation set samples; the receiver operating characteristic (ROC) curve (C) and the precision–recall (PR) curves (D) of GB model; Shapley additive explanations (SHAP) plot (E) for the gradient boosting model. The bar graph shows the input variables’ relative importance. The figure plots every sample in the analysis as a point. The y-axis lists the input variables. The x-axis is a metric of the SHAP value associated with each variable and sample within the dataset (i.e., points plotted for each case based on the impact on prediction). The points plotted on the far left have a greater impact on X prediction, and points plotted on the right have a greater impact on Y prediction. The normalized value of observation is color-based (red = higher values; blue = lower values).

Figure 3

The aromatic profiles (A), OPLS-DA score plot (B), and odors related to the key differentiating metabolites (C) of high-quality (top 30 in aroma score) and low-quality (bottom 30 in aroma score) grades of Tieguanyin tea; heat map (D) of normalized accumulation levels of 33 differential metabolites between high- and low-quality Tieguanyin tea using OPLS-DA with VIP > 1, p < 0.05.

Figure 4

Histogram of the sensory scores (A) for the light-scented Tieguanyin tea collected from different seasons. The dotted line represents the average score of the season; correlation network of sensory-related metabolites (B) for light-scented Tieguanyin (|r| > 0.7 and p < 0.05) on VIP > 1 metabolites identified in OPLS-DA analysis. The purple ring indicates the compounds are important in both sensory and seasonality differentiation.