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To explore the effect of room temperature storage on the flavor quality and biochemical composition of flower and fruit scented black tea, this study compared the sensory flavor quality of flower and fruit scented black tea produced in the years 2019, 2020, 2021 and 2022 and stored at room temperature, and it detected volatile and non-volatile compounds in the tea by headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and ultra-performance liquid chromatography coupled to orbitrap high resolution mass spectrometry (UPLC-Orbitrap-MS). The results showed that the sensory quality of flower and fruit scented black tea was significantly affected by storage at room temperature, and the tea lost its floral and fruity aroma characteristics after three years of storage at room temperature; its quality characteristic was mature sour taste. The tea samples were divided into four groups of age by principal component analysis (PCA) and hierarchical cluster analysis (HCA). Totally 15 volatile differential compounds such as linalool, cis-β-ocimene, and hexanoic acid (VIP > 1 and P 2 and P < 0.05) were selected by partial least squares discriminant analysis (PLS-DA) and one-way analysis of variance (ANOVA). The content of volatile compounds such as linalool, hexanal, cis-β-ocimene, and 2-pentylfuran decreased with increasing storage time, while the contents of hexanoic acid, dihydroactinidolide 1-ethyl-2-formyl-1H-pyrrole, and β-ionone increased. The contents of non-volatile compounds such as most amino acids, nucleotides, sugars, tea polyphenols tended to decrease with increasing storage time, while the contents of organic acids and lipids showed an increasing trend. The results obtained from this study can provide a scientific basis for elucidating the quality changes of flower and fruit scented black tea during storage and help guide its rational storage and scientific consumption.

This study investigated the changes in the aroma of jasmine tea during storage. Solid-phase micro-extraction (SPME)–gas chromatography (GC)-mass spectrometry (MS) and stir bar sorptive extraction (SBSE)-GC-MS were combined to detect all volatile compounds. GC-olfactometry (GC-O), odor activity value (OAV), and p-value were employed to analyze and identify the key aroma compounds in six jasmine tea samples stored for different durations. Nine key aroma compounds were discovered, namely (Z)-3-hexen-1-yl acetate, methyl anthranilate, methyl salicylate, trans-β-ionone, linalool, geraniol, (Z)-4-heptenal, benzoic acid methyl ester, and benzoic acid ethyl ester. The importance of these compounds was confirmed through the aroma addition experiment. Correlation analysis showed that (Z)-4-heptenal might be the main reason for the increase in the stale aroma of jasmine tea. Through sensory evaluation and specific experimental analysis, it can be concluded that jasmine tea had the best aroma after 3 years of storage, and too long a storage time may cause the overall aroma of the tea to weaken and produce an undesirable odor. The findings can provide a reference for the change in aroma during the storage of jasmine tea and provide the best storage time (3 years) in terms of jasmine tea aroma. [ABSTRACT FROM AUTHOR]

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea. [ABSTRACT FROM AUTHOR]