Rapid Identification of Different Grades of Huangshan Maofeng Tea Using Ultraviolet Spectrum and Color Difference

A sensor array based on a nanozyme with polyphenol oxidase activity for the identification of tea polyphenols and Chinese green tea

Green tea is popular among consumers because of its high nutritional value and unique flavor. There is often a strong correlation among the type of tea, its quality level and the price. Therefore, the rapid identification of tea types and the judgment of tea quality grades are particularly important. In this work, a novel sensor array based on nanozyme with polyphenol oxidase (PPO) activity is proposed for the identification of tea polyphenols (TPs) and Chinese green tea. The absorption spectra changes of the nanozyme and its substrate in the presence of different TPs were first investigated. The feature spectra were scientifically selected using genetic algorithm (GA), and then a sensor array with 15 sensing units (5 wavelengths × 3 time) was constructed. Combined with the support vector machine (SVM) discriminative model, the discriminative rate of this sensor array was 100% for different concentrations of typical TPs in Chinese green tea with a detection limit of 5 μM. In addition, the identification of different concentrations of the same tea polyphenols and mixed tea polyphenols have also been achieved. Based on the above study, we further developed a facile and efficient new method for the category differentiation and adulteration identification of green tea, and the accuracy of this array was 96.88% and 100% for eight types of green teas and different adulteration ratios of Biluochun, respectively. This work has significance for the rapid discrimination of green tea brands and adulteration.

Comparative Analysis of Hot and Cold Brews from Single-Estate Teas (Camellia sinensis) Grown across Europe: An Emerging Specialty Product

Tea is grown around the world under extremely diverse geographic and climatic conditions, namely, in China, India, the Far East and Africa. However, recently, growing tea also appears to be feasible in many regions of Europe, from where high-quality, chemical-free, organic, single-estate teas have been obtained. Hence, the aim of this study was to characterize the health-promoting properties in terms of the antioxidant capacity of traditional hot brews as well as cold brews of black, green and white teas produced across the European territory using a panel of antioxidant assays. Total polyphenol/flavonoid contents and metal chelating activity were also determined. For differentiating the characteristics of the different tea brews, ultraviolet-visible (UV-Vis) spectroscopy and ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry were employed. Overall, our findings demonstrate for the first time that teas grown in Europe are good quality teas that are endowed with levels of health-promoting polyphenols and flavonoids and that have an antioxidant capacity similar to those grown in other parts of the world. This research is a vital contribution to the characterization of European teas, providing essential and important information for both European tea growers and consumers, and could be of guidance and support for the selection of teas grown in the old continent, along with having the best brewing conditions for maximizing the health benefits of tea.

Exploring the Quality and Application Potential of the Remaining Tea Stems after the Postharvest Tea Leaves: The Example of Lu'an Guapian Tea (Camellia sinensis L.)

Lu’an Guapian tea is produced through the processing of only leaves, with the stems and buds discarded, but stems constitute a large proportion of the tea harvest. To test the usability of tea stems, we compared the physicochemical properties of tea leaves and stems from the same growth period as well as the taste of their infusions. The leaves contained higher concentrations of polyphenols and caffeine and had a stronger taste. The tea stems contained higher concentrations of free amino acids and soluble sugars and were richer in umami and sweet flavors. In addition, more tender tea stems had higher concentrations of polyphenols, caffeine, and free amino acids, and their infusions had more refreshing and sweeter tastes. Furthermore, crude fiber content increased as stem tenderness decreased. In summary, tea stems are rich in phytochemical components and flavor, and these properties increased with tenderness. This provides a theoretical basis for the high-value utilization of tea stems.

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.

Process Optimization for Supercritical Carbon Dioxide Extraction of Origanum vulgare L. Essential Oil Based on the Yield, Carvacrol, and Thymol Contents

Background

Origanum vulgare L. essential oil (OEO) is widely known for its good biological activity, but different extraction methods with significant implications on the yield of OEO and the content of the thymol and carvacrol. As an efficient method for extracting essential oils (EO), the supercritical carbon dioxide extraction (SC-CO2) can improve the yield of EOs while protecting their main active components from loss.

Objective

In this study, the process optimization of SC-CO2 of OEO was carried out. The effects of extraction pressure, temperature, time, and modifier concentration on the composite score of OEO extraction process were investigated.

Method

Response surface analysis was performed using a Box-Behnken design with three levels and four independent variables. Steam distillation (SD) and lipophilic solvents (n-hexane) extraction (LSE) were compared with SC-CO2 for OEO yields. OEOs extracted by the three methods were qualitatively and semi-quantitatively analyzed by gas chromatography quadrupole-time-of-flight mass spectrometry and gas chromatography-flame ionization detector.

Results

The results showed that extraction pressure was the most significant factor affecting the OEO yield, thymol, and carvacrol content. In the optimal conditions (pressure: 217 bar, temperature: 54°C, time: 2 h, modifier concentration: 14%), the yield of OEO reached up to 1.136%, and the contents of thymol and carvacrol reached 53.172  and 41.785 mg/g, respectively.

Conclusions

SC-CO2 was the best extraction method compared to the other two methods. Under the optimal conditions, yield and the content of main components can be effectively improved. It can provide a theoretical basis for the industrial extraction of OEO.

Highlights

Taking the comprehensive score as the index, the interaction between the four independent variables in the supercritical fluid extraction process was evaluated by the response surface method. The effects of extraction parameters on the yield of EOs and the contents of thymol and carvacrol were comprehensively investigated.

The Physiology of Postharvest Tea (Camellia sinensis) Leaves, According to Metabolic Phenotypes and Gene Expression Analysis

Proper postharvest storage preserves horticultural products, including tea, until they can be processed. However, few studies have focused on the physiology of ripening and senescence during postharvest storage, which affects the flavor and quality of tea. In this study, physiological and biochemical indexes of the leaves of tea cultivar ‘Yinghong 9′ preserved at a low temperature and high relative humidity (15–18 °C and 85–95%, PTL) were compared to those of leaves stored at ambient conditions (24 ± 2 °C and relative humidity of 65% ± 5%, UTL). Water content, chromatism, chlorophyll fluorescence, and key metabolites (caffeine, theanine, and catechins) were analyzed over a period of 24 h, and volatilized compounds were determined after 24 h. In addition, the expression of key biosynthesis genes for catechin, caffeine, theanine, and terpene were quantified. The results showed that water content, chromatism, and chlorophyll fluorescence of preserved leaves were more similar to fresh tea leaves than unpreserved tea leaves. After 24 h, the content of aroma volatiles and caffeine significantly increased, while theanine decreased in both groups. Multiple catechin monomers showed distinct changes within 24 h, and EGCG was significantly higher in preserved tea. The expression levels of CsFAS and CsTSI were consistent with the content of farnesene and theanine, respectively, but TCS1 and TCS2 expression did not correlate with caffeine content. Principal component analysis considered results from multiple indexes and suggested that the freshness of PTL was superior to that of UTL. Taken together, preservation conditions in postharvest storage caused a series of physiological and metabolic variations of tea leaves, which were different from those of unpreserved tea leaves. Comprehensive evaluation showed that the preservation conditions used in this study were effective at maintaining the freshness of tea leaves for 2–6 h. This study illustrates the metabolic changes that occur in postharvest tea leaves, which will provide a foundation for improvements to postharvest practices for tea leaves.

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.

The qualitative and quantitative assessment of xiaochaihu granules based on e-eye, e-nose, e-tongue and chemometrics

Xiaochaihu granules (XCHG), a famous Chinese patent medicine with high sales, have more than 100 approved number by China Food and Drug Administration (CFDA). Therefore, it is important to evaluate the quality of XCHG from different pharmaceutical companies. The data fusion of electronic eye (e-eye), electronic nose (e-nose) and electronic tongue (e-tongue) combined with chemometrics were applied for qualitative identification and quantitative prediction of XCHG quality. Firstly, main chemical constituents, such as saikosaponin b2, baicalin and glycyrrhizin were quantified with ultra-high-performance liquid chromatography (UHPLC). Secondly, the characteristic features of odor, color, and taste of XCHG were measured by e-nose, e-eye and e-tongue, and the Pearson correlation between constituents and e-signals was analyzed. Thirdly, partial least squares discrimination analysis (PLS-DA) of e-eye, e-nose and e-tongue were classified by the hierarchical clustering analysis (HCA) results of the main constituents of XCHG separately. Finally, partial least-squares regression (PLSR) was used to build the prediction model between components and data fusion of e-eye, e-nose and e-tongue. The results showed that saikosaponin b2, baicalin and glycyrrhizin were the three main components in XCHG samples. in which saikosaponin b2 ranged from 0.280 to 2.186 mg (relative standard deviation (RSD), 62.10 %), baicalin range from 25.883 mg to 49.108 mg (RSD, 16.64 %), and glycyrrhizin ranged from 0.897 mg to 6.052 mg (RSD, 40.32 %) of 31 batches of XCHG in each bag. Pearson correlation results showed that the main constituents were related to the core e-signals of XCHG, such as Eab, bitterness and R2 (odor sensitive to nitrogen oxide). Data fusion of e-eye, e-nose and e-tongue with main constitutes of XCHG using the PLSR model showed that the root mean square error (RMSE) values were 0.320 and 0.090 for saikosaponin b2 and licoricesaponin G2 (P < 0.000). The saikosaponin b2 and licoricesaponin G2 contents in XCHG could be predicted with integrated data of e-nose, e-eye, and e-tongue using the PLSR model.

A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.