Exploration of a Method of Distinguishing Different Nongxiang Tieguanyin Tea Grades Based on Aroma Determined by GC-MS Combined with Chemometrics

Analysis of volatiles from the thermal decomposition of Amadori rearrangement products in the cysteine-glucose Maillard reaction and density functional theory study

The Amadori rearrangement products are an important flavor precursor in the Maillard reaction. Its thermal decomposition products usually contribute good flavors in foods. Therefore, investigating the thermal breakdown of Amadori products is significant for understanding the flavor forming mechanism in the Maillard reaction. In this study, volatiles from thermal decomposition of Amadori products in cysteine and glucose Maillard reaction was investigated by a thermal desorption cryo-trapping system combined with gas chromatography-mass spectrometry (GC-MS). A total of 60 volatiles were detected and identified. Meanwhile, the forming mechanism of 2-methylthiophene, a major decomposition product, was also investigated by using density functional theory. Seventeen reactions, 12 transition states, energy barrier and rate constant of each reaction were finally obtained. Results reveal that it is more likely for Amadori products of cysteine and glucose to undergo decomposition under neutral or weakly alkaline conditions.

Comprehensive applications of metabolomics on tea science and technology: Opportunities, hurdles, and perspectives

With the development of metabolomics analytical techniques, relevant studies have increased in recent decades. The procedures of metabolomics analysis mainly include sample preparation, data acquisition and pre-processing, multivariate statistical analysis, as well as maker compounds' identification. In the present review, we summarized the published articles of tea metabolomics regarding different analytical tools, such as mass spectrometry, nuclear magnetic resonance, ultraviolet-visible spectrometry, and Fourier transform infrared spectrometry. The metabolite variation of fresh tea leaves with different treatments, such as biotic/abiotic stress, horticultural measures, and nutritional supplies was reviewed. Furthermore, the changes of chemical composition of processed tea samples under different processing technologies were also profiled. Since the identification of critical or marker metabolites is a complicated task, we also discussed the procedure of metabolite identification to clarify the importance of omics data analysis. The present review provides a workflow diagram for tea metabolomics research and also the perspectives of related studies in the future.

Identification of key volatile and odor-active compounds in 10 main fragrance types of Fenghuang Dancong tea using HS-SPME/GC-MS combined with multivariate analysis

Fenghuang Dancong tea (FHDC), a famous oolong tea originating from Guangdong Province in China, is known for its rich and unique fragrance. Nevertheless, the identification of the key aroma compounds with the difference fragrance types of FHDC remains uncertain. In order to characteristic the volatile components in different fragrance types of FHDC, 10 well-known fragrance types of FHDC and Tieguanyin (TGY) as a control were analyzed by headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography mass spectrometry (GC-MS). Results indicated that 172 volatile compounds were identified as common volatile compounds among all the tea samples. A total of 16 compounds were identified as key compounds that could be used to distinguish between FHDC and TGY. Among the 10 FHDC fragrance types, indole, hotrienol, benzyl nitrile, and jasmine lactone were found to be the most abundant compounds. Despite the presence of certain similarities in aroma components, each type exhibits unique fragrance characteristics as a result of variation in compound composition content and proportion. Furthermore, using statistical and odor activity value analysis, 20 aroma-active compounds were recognized as potential characteristic markers accountable for the diverse fragrance types of FHDC. This research enhances our comprehension of the various fragrance types of FHDC and provides reference values for their rapid identification in the market.

HS−SPME/GC−MS Reveals the Season Effects on Volatile Compounds of Green Tea in High−Latitude Region

This study investigates the volatile compounds of green tea produced with different leaves from spring, summer, and autumn in high−latitude region. A total of 95 volatile compounds were identified by gas chromatography–mass spectrometry (GC–MS). Spring, summer and autumn green tea contained 68, 72 and 82 volatile compounds, respectively. Principal component analysis (PCA), partial least squares−discrimination analysis (PLS−DA), and hierarchical cluster analysis (HCA) classified the samples and showed the difference. And 32 key characteristic components were screened out based on variable importance in the projection (VIP) values higher than 1.0. The characteristic volatile compounds of spring green tea including 18 components, such as geranylacetone, phenethyl alcohol, geraniol, β−ionone, jasmone, 1−octen−3−ol and longifolene. 13 components such as 2−methylfuran, indole, 1−octanol, D−limonene and ethanethiol were the key compounds in summer green tea. And 2,4,6−trimethylstyrene was the major differential volatile compounds in autumn green tea. The results increase our knowledge of green tea in different seasons and provide a theoretical basis for production control of green tea.

Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics

Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS−DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS−DA. MSC_VIP_PLS−DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.

Variations in Fatty Acids Affected Their Derivative Volatiles during Tieguanyin Tea Processing

Fatty acids (FAs) are important precursors of oolong tea volatile substances, and their famous derivatives have been shown to be the key aroma components. However, the relationship between fatty acids and their derivatives during oolong tea production remains unclear. In this study, fresh Tieguanyin leaves were manufactured into oolong tea and green tea (control), and fatty acids and fatty acid-derived volatiles (FADV) were extracted from processed samples by the sulfuric acid–methanol method and solvent-assisted flavor evaporation (SAFE), respectively. The results showed that unsaturated fatty acids were more abundant than saturated fatty acids in fresh leaves and decreased significantly during tea making. Relative to that in green tea, fatty acids showed larger variations in oolong tea, especially at the green-making stage. Unlike fatty acids, the FADV content first increased and then decreased. During oolong tea manufacture, FADV contents were significantly and negatively correlated with total fatty acids; during the green-making stage, methyl jasmonate (MeJA) content was significantly and negatively correlated with abundant fatty acids except steric acid. Our data suggest that the aroma quality of oolong tea can be improved by manipulating fatty acid transformation.

Comparative Analysis of Volatile Compounds in Tieguanyin with Different Types Based on HS-SPME-GC-MS

Tieguanyin (TGY) is one kind of oolong tea that is widely appreciated for its aroma and taste. To study the difference of volatile compounds among different types of TGY and other oolong teas, solid-phase microextraction−gas chromatography−mass spectrometry and chemometrics analysis were conducted in this experiment. Based on variable importance in projection > 1 and aroma character impact > 1, the contents of heptanal (1.60−2.79 μg/L), (E,E)-2,4-heptadienal (34.15−70.68 μg/L), (E)-2-octenal (1.57−2.94 μg/L), indole (48.44−122.21 μg/L), and (E)-nerolidol (32.64−96.63 μg/L) in TGY were higher than in other varieties. With the increase in tea fermentation, the total content of volatile compounds decreased slightly, mainly losing floral compounds. Heavily fermented tea contained a higher content of monoterpenoids, whereas low-fermentation tea contained higher contents of sesquiterpenes and indole, which could well distinguish the degree of TGY fermentation. Besides, the volatiles analysis of different grades of TGY showed that the special-grade tea contained more aroma compounds, mainly alcohols (28%). (E,E)-2,4-Heptadienal, (E)-2-octenal, benzeneacetaldehyde, and (E)-nerolidol were the key volatile compounds to distinguish different grades of TGY. The results obtained in this study could help enrich the theoretical basis of aroma substances in TGY.

Effects of the Baking Process on the Chemical Composition, Sensory Quality, and Bioactivity of Tieguanyin Oolong Tea

Tieguanyin oolong tea (TOT), a semi-oxidized tea originating from Anxi county in China, is categorized into jade TOT, medium-baked TOT, and deep-baked TOT, based on different baking processes. To study the effects of baking, chemical analysis, sensory evaluation, and bioactivity assessments of the three TOTs were conducted. The results indicated that the baking process promoted the formation of colored macromolecules (e.g., theabrownins), which affected the color of tea infusion. Free amino acids underwent the Maillard reaction and generated specific Maillard reaction products, such as 5-hydroxymethylfurfural and furfural, which modified the taste and aroma. Floral and fresh volatiles were remarkably reduced, while multiple new volatiles were produced, forming a typically baked aroma. The antioxidant activity and antibacterial activity were reduced after baking, which might be associated with the decrease of monomeric catechins. These results provide a scientific basis for understanding the changes caused by the baking process.

GC-IMS and olfactometry analysis on the tea aroma of Yingde black teas harvested in different seasons

This study aims to investigate the influence of different harvesting seasons on the aroma of black tea and the trend in the tea aroma variation. A total of 68 volatile substances was identified by gas chromatography coupled with ion-mobility spectrometry (GC-IMS), and 20 characteristic aroma-active compounds were quantitatively analyzed by gas chromatography-olfactometry coupled with aroma extract dilution analysis (GC-O AEDA) and odor activity value (OAV) analysis. These aroma-active compounds are mainly linalool, β-damascenone, and benzeneacetaldehyde. Both methods confirmed that the aroma of tea changes with the harvesting seasons, showing a downward trend followed by an upward trend. Besides, black teas harvested in different seasons have their characteristic volatile compounds and metabolism precursors. The degradation of glycosides, carotenes, and amino acids are the most important degradation pathways for the formation of tea aroma. The PLSR results of GC-O-AEDA, OAV, and DSA data agree with each other, showing that five aroma attributes of the autumn tea have strong correlations. The autumn tea has the richest aroma, followed by the spring tea and the summer tea.

Predicting the grades of Astragali radix using mass spectrometry-based metabolomics and machine learning

Astragali radix (AR, the dried root of Astragalus) is a popular herbal remedy in both China and the United States. The commercially available AR is commonly classified into premium graded (PG) and ungraded (UG) ones only according to the appearance. To uncover novel sensitive and specific markers for AR grading, we took the integrated mass spectrometry-based untargeted and targeted metabolomics approaches to characterize chemical features of PG and UG samples in a discovery set (n=16 batches). A series of five differential compounds were screened out by univariate statistical analysis, including arginine, calycosin, ononin, formononetin, and astragaloside Ⅳ, most of which were observed to be accumulated in PG samples except for astragaloside Ⅳ. Then, we performed machine learning on the quantification data of five compounds and constructed a logistic regression prediction model. Finally, the external validation in an independent validation set of AR (n=20 batches) verified that the five compounds, as well as the model, had strong capability to distinguish the two grades of AR, with the prediction accuracy > 90%. Our findings present a panel of meaningful candidate markers that would significantly catalyze the innovation in AR grading.

An integrated strategy for the establishment of a protoberberine alkaloid profile: Exploration of the differences in composition between Tinosporae radix and Fibraurea caulis

INTRODUCTION

Accurate species and content identification of major active components in herbals are the guarantee of the safety and effectiveness for medical and commodity purposes.

OBJECTIVES

In this study, an integrated strategy used to establish the protoberberine alkaloid profile was applied to explore the differences in composition between the pieces of Tinosporae radix and Fibraurea caulis, both of which had morphological similarities.

MATERIALS AND METHODS

First, an in-house library including possible protoberberine alkaloids based on different substituents was predicted by systematic literature survey. Meanwhile, diagnostic fragments of protoberberine alkaloids were investigated using the corresponding standards. Second, ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) was used to obtain multidimensional mass spectral data. Then, the identifications were confirmed by targeted filter of the acquired data based on the library.

RESULTS

As a result, 10 protoberberine alkaloid molecules including 46 isomers were identified or characterised. The qualitative distribution and relative content of protoberberine alkaloids revealed the fundamental difference between Tinosporae radix and Fibraurea caulis. 25 alkaloids were present in both herbals, while five compounds were detected only in Tinosporae radix. Furthermore, the contents of four alkaloids in Tinosporae radix were significantly higher than those in its adulterant, Fibraurea caulis.

CONCLUSION

The five unique ingredients in Tinosporae radix can be used as a better indicator for distinguishing the pieces of Tinosporae radix and Fibraurea caulis. The protoberberine alkaloid profile established in this study can be applied to quality evaluation of the two herbals or other herbals containing major protoberberine alkaloids.

Comparison of Volatiles in Different Jasmine Tea Grade Samples Using Electronic Nose and Automatic Thermal Desorption-Gas Chromatography-Mass Spectrometry Followed by Multivariate Statistical Analysis

Chinese jasmine tea is a type of flower-scented tea, which is produced by mixing green tea with the Jasminum sambac flower repeatedly. Both the total amount and composition of volatiles absorbed from the Jasminum sambac flower are mostly responsible for its sensory quality grade. This study aims to compare volatile organic compound (VOC) differences in authoritative jasmine tea grade samples. Automatic thermal desorption-gas-chromatography-mass spectrometry (ATD-GC-MS) and electronic nose (E-nose), followed by multivariate data analysis is conducted. Consequently, specific VOCs with a positive or negative correlation to the grades are screened out. Partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) show a satisfactory discriminant effect on rank. It is intriguing to find that the E-nose is good at distinguishing the grade difference caused by VOC concentrations but is deficient in identifying essential aromas that attribute to the unique characteristics of excellent grade jasmine tea.