Discrimination and polyphenol compositions of green teas with seasonal variations based on UPLC-QTOF/MS combined with chemometrics

A comparative metabolomics analysis of phytochemcials and antioxidant activity between broccoli floret and by-products (leaves and stalks)

Leaves and stalks, which account for about 45% and 25% of broccoli biomass, respectively, are usually discarded during broccoli production, leading to the waste of green resources. In this study, the phytochemical composition and antioxidant capacity of broccoli florets and their by-products (leaves and stalks) were comprehensively analyzed. The metabolomics identified several unique metabolites (e.g., scopoletin, Harpagoside, and sinalbin) in the leaves and stalks compared to florets. Notably, the leaves were found to be a rich source of flavonoids and coumarins, with superior antioxidant capacity. The random forest model and correlation analysis indicated that flavonoids, coumarin, and indole compounds were the important factors contributing to the antioxidant activity. Moreover, the stalks contained higher levels of carbohydrates and exhibited better antioxidant enzyme activity. Together, these results provided valuable data to support the comprehensive utilization of broccoli waste, the development of new products, and the expansion of the broccoli industry chain.

Exploring the Influence of Origin, Harvest Time, and Cultivation Method on Antioxidant Capacity and Bioactive Compounds of Matcha Teas

Matcha, or powdered green tea, has been gaining popularity and is no longer consumed only in the form of infusions, finding new uses in gastronomy and the food industry. The range of teas available on the food market has expanded considerably; hence, the aim of this study was to determine, for the first time, the antioxidant capacity and contents of antioxidant compounds in various Matcha teas available on the Polish market, taking into account the country of origin, time of harvest, and conventional vs. organic cultivation. Eleven green-tea powders were used in the analyses performed using spectrophotometric methods (Trolox equivalent antioxidant capacity, Ferric-Ion-Reducing Antioxidant Power, Total Polyphenol Content, Total Flavonoid Content, Vitamin C Content) and HPLC methods (polyphenolic acids, flavonoids, and caffeine). Antioxidant capacity ranged from 7.26 to 9.54 mM Trolox equivalent/L while reducing power ranged from 1845.45 to 2266.12 Fe(II)/L. Total phenolic content amounted to 820.73-1017.83 mg gallic acid equivalent/L, and total flavonoid content was 864.71-1034.40 mg rutin equivalent /L. A high vitamin C content was found, ranging from 38.92 to 70.15 mg/100 mL. Additionally, a high content of caffeine that ranged between 823.23 and 7313.22 mg/L was noted. Moreover, a high content of polyphenolic compounds, including epicatechin gallate, myricetin, gallic acid, and 4-hydroxybenzoic acid, was found. The phytochemical composition and antioxidant properties depended on the harvest time, type of cultivation, and country of origin. Therefore, Matcha tea infusions have been shown to be a valuable source of antioxidants that can be used in the daily diet.

The Impact of Harvesting Mechanization on Oolong Tea Quality

Mechanization is the inevitable future of tea harvesting, but its impact on tea chemistry and quality remains uncertain. Our study examines untargeted metabolomic data from 185 oolong tea products (Tieguanyin) made from leaves harvested by hand or machine based on UPLC-QToF-MS analysis. The data revealed a minimum 50% loss for over half of the chemicals in the machine-harvested group, including catechins, theaflavin, gallic acid, chlorogenic acid, and kaempferol-3-gluocside. Integrating sensory evaluation, OPLS-DA identified the six most important metabolites as significant contributors to sensory decline caused by harvesting mechanization. Furthermore, our research validates the possibility of using DD-SIMCA modelling with untargeted metabolomic data for distinguishing handpicked from machine-harvested tea products. The model was able to achieve 93% accuracy. This study provides crucial insights into the chemical and sensory shifts during mechanization, along with tools to manage and monitor these changes.

Exploring Influence of Production Area and Harvest Time on Specialized Metabolite Content of Glycyrrhiza glabra Leaves and Evaluation of Antioxidant and Anti-Aging Properties

Calabrian Glycyrrhiza glabra is one of the most appreciated licorice varieties worldwide, and its leaves are emerging as a valuable source of bioactive compounds. Nevertheless, this biomass is usually discarded, and its valorization could contribute to boost the economic value of the licorice production chain. In this study, the effects of production area and harvest time on the specialized metabolite content of G. glabra leaves (GGL) and also the antioxidant and anti-aging properties are evaluated to explore the potential of this untapped resource and to select the most optimal harvesting practices. GGL exhibited high levels of specialized metabolites (4-30 g/100 g of dry leaf) and the most abundant ones are pinocembrin, prenylated flavanones (licoflavanone and glabranin), and prenylated dihydrostilbenes. Their levels and antioxidant capacity in extracts are influenced by both production area and harvest time, showing a decisive role on specialized metabolites accumulation. Interestingly, GGL extracts strongly attenuate the toxicity of α-synuclein, the intracellular reactive oxygen species (ROS) content, and cellular senescence on Saccharomyces cerevisiae expressing human α-synuclein model, showing great potential to prevent aging and age-related disorders. These results provide insights into the phytochemical dynamics of GGL, identifying the best harvesting site and period to obtain bioactive-rich sources with potential uses in the food, nutraceutical, and pharmaceutical sectors.

UPLC-Q-TOF/MS-based metabolomic analysis reveals the effects of asomate on the citrus fruit

The regulation of the sugar-acid ratio is of great significance to the improvement of citrus fruit quality. The citric acid level in fruit is influenced by many factors. Among them, cultivar selection and production practices are the most important strategies under the grower's control. In recent years, an arsenic-containing preparation called "Tianmisu", with the main ingredient of asomate, has occasionally been reported to be used in citrus cultivation to improve the sweetness of fruits. In order to reveal the effects of the pesticide on citrus fruits, 'Harumi' tangor was treated with "Tianmisu", and the impact of this pesticide on fruit quality and metabolites was investigated through UPLC-Q-TOF/MS-based metabolomic analysis. Compared with the control, the concentration of titratable acidity, in particular citric acid, in the pulp of 'Harumi' tangor treated with the pesticide, was significantly reduced by 60.5%. The differences in metabolites between the pesticide-treated samples and the control were illustrated by Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). The PLS-DA analysis demonstrated a clear discrimination, with R²Y and Q² values of 0.982 and 0.933 in the positive mode and 0.984 and 0.900 in the negative mode, respectively. A total of 155 compounds were identified, and 63 characteristic components were screened out from the pesticide-treated samples compared to the control. Aside from the upregulation observed for a few metabolites, the majority of the compounds, including citric acid and various lipids, were down-regulated in the treated citrus fruits compared to the control. This study can serve as a basis for understanding the regulatory mechanism of organic acids in citrus and will be helpful in developing different strategies to improve citrus quality.

Surface-enhanced Raman scattering integrating with machine learning for green tea storage time identification

The rapid and accurate identification of complex samples still remains a great challenge, especially for those with similar compositions. In this work, we report an integration strategy consisting of surface-enhanced Raman scattering (SERS) and machine learning to discriminate complex and similar analytes, in this case green tea products with different storage times. Surface-functionalized Ag nanoparticles (NPs) were used as a SERS substrate to reveal the changes in the sensory components of green tea with variable storage time. Principal components analysis (PCA)-based support vector machine (SVM) classification was used to extract the key spectral features and identify green tea with different storage times. The results showed that such an integration strategy achieved high predictive accuracy on time tag discrimination for green tea. The multiclass SVM classifier successfully recognized green tea with different storage times at a prediction accuracy of 95.9%, sensitivity of 96.6%, and specificity of 98.8%. Therefore, this work illustrates that the SERS-based PCA-SVM platform might be a facile and reliable tool for the identification of complex matrices with subtle differentiations.

Effects of Tea Plant Varieties with High- and Low-Nutrient Efficiency on Nutrients in Degraded Soil

Tea plants are widely planted in tropical and subtropical regions globally, especially in southern China. The high leaching and strong soil acidity in these areas, in addition to human factors (e.g., tea picking and inappropriate fertilization methods) aggravate the lack of nutrients in tea garden soil. Therefore, improving degraded tea-growing soil is urgently required. Although the influence of biological factors (e.g., tea plant variety) on soil nutrients has been explored in the existing literature, there are few studies on the inhibition of soil nutrient degradation using different tea plant varieties. In this study, two tea plant varieties with different nutrient efficiencies (high-nutrient-efficiency variety: Longjing43 (LJ43); low-nutrient-efficiency variety: Liyou002 (LY002)) were studied. Under a one-side fertilization mode of two rows and two plants, the tea plant growth status, soil pH, and available nutrients in the soil profiles were analyzed, aiming to reveal the improvement of degraded soil using different tea varieties. The results showed that (1) differences in the phenotypic features of growth (such as dry tea yield, chlorophyll, leaf nitrogen (N), phosphorus (P), and potassium (K) content) between the fertilization belts in LJ43 (LJ43-near and LJ43-far) were lower than those in LY002. (2) RDA results showed that the crucial soil nutrient factors which determine the features of tea plants included available P, slowly available K, and available K. Moreover, acidification was more serious near the fertilization belt. The pH of the soil near LJ43 was higher than that near LY002, indicating an improvement in soil acidification. (3) Soil nutrient heterogeneity between fertilization belts in LJ43 (LJ43-near and LJ43-far) was lower than in LY002. In conclusion, the long-term one-side fertilization mode of two rows and two plants usually causes spatial heterogeneities in soil nutrients and aggravates soil acidification. However, LJ43 can reduce the nutrient heterogeneities and soil acidification, which is probably due to the preferential development of secondary roots. These results are helpful in understanding the influence of tea plant variety on improving soil nutrients and provide a relevant scientific reference for breeding high-quality tea varieties, improving the state of degraded soil and maintaining soil health.

Discrimination of the harvesting season of green tea by alcohol/salt-based aqueous two-phase systems combined with chemometric analysis

The flavor and aroma quality of green tea are closely related to the harvest season. The aim of this study was to identify the harvesting season of green tea by alcohol/salt-based aqueous two-phase system (ATPS) combined with chemometric analysis. In this paper, the single factor experiments (SFM) and response surface methodology (RSM) optimization were designed to investigate and select the optimal ATPS. A total of 180 green tea samples were studied in this work, including 86 spring tea and 94 autumn tea. After the active components in green tea samples were extracted by the optimal ethanol/(NH₄)₂SO₄ ATPS, the qualitative and quantitative analysis was realized based on HPLC-DAD combined with alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) algorithm, with satisfactory spiked recoveries (86.00 %-112.45 %). The quantitative results obtained from ATLD-MCR model were subjected to chemometric pattern recognition analysis. The constructed partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models showed better results than the principal component analysis (PCA) model, and the R²X_cum values (>0.835) and R²Y_cum (>0.937) were close to 1, the Q²_cum values were greater than 0.75 (>0.933), and the differences between R²Y_cum and Q²_cum were not larger than 0.2, indicating excellent cross-validation prediction performance of the models. Furthermore, the classification results based on the hierarchical clustering analysis (HCA) were consistent with the PCA, PLS-DA and OPLS-DA results, establishing a good correlation between tea active components and the harvesting seasons of green tea. Overall, the combination of ATPS and chemometric methods is accurate, sensitive, fast and reliable for the qualitative and quantitative determination of tea active components, providing guidance for the quality control of green tea.

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.

Cellulose-Based Light-Management Films with Improved Properties Directly Fabricated from Green Tea

Tea polyphenols are a phenolic bioactive compound extracted from tea leaves and have been widely used as additives to prepare functional materials used in packaging, adsorption and energy fields. Nevertheless, tea polyphenols should be extracted first from the leaves before use, leading to energy consumption and the waste of tea. Therefore, completely and directly utilizing the tea leaf to fabricate novel composite materials is more attractive and meaningful. Herein, semi-transparent green-tea-based all-biomass light-management films with improved strength, a tunable haze (60–80%) and UV-shielding properties (24.23% for UVA and 4.45% for UVB) were directly manufactured from green tea by adding high-degree polymerization wood pulps to form entanglement networks. Additionally, the green-tea-based composite films can be produced on a large scale by adding green tea solution units to the existing continuous production process of pure cellulose films. Thus, a facile and feasible approach was proposed to realize the valorization of green tea by preparing green-tea-based all-biomass light-management films that have great prospects in flexible devices and energy-efficient buildings.

Metabolomic analysis reveals the quality characteristics of Yi Gong tea leaves at different harvesting periods

To obtain a theoretical reference for understanding the changes in metabolites of Yigong tea leaves during different harvesting periods and to determine the optimal harvesting period, we performed a metabolome comparison using UPLC-Q-Exactive MS on Yigong tea leaves from different harvesting periods. The results indicated that a total of 41 metabolites were significantly altered during the growth of Yi Gong tea leaves. These involved 7 amino acids and their derivatives, 16 flavonols and flavonol glycosides, 4 organic acids, 3 catechins, 3 carbohydrates, 7 fatty acid esters, 1 terpene, and 3 substances from others. In particular, the levels of arginine and glutamine were higher in early-harvested tea leaves than in late-harvested tea leaves; the levels of flavonoids and flavonols were higher in late-harvested tea leaves. Metabolic pathway analysis revealed that the caffeine metabolism and the flavonoid biosynthesis perform key roles in Yigong tea leaves from different harvesting periods. PRACTICAL APPLICATIONS: At present, the application of metabolomics in tea research is focused on the study of pesticide residues, processing processes, environmental stresses, and regional differences. This study is to focus on the effect of the tea harvesting period on tea quality through metabolomics. Through metabolomics, we can better determine the optimal tea harvesting period, and this study can improve the quality of this tea product and may be able to bring some favourable favorable contributions contribution to the local tea marketing in the future.

Paper-based colorimetric sensor array for the rapid and on-site discrimination of green tea samples based on the flavonoid composition

Green tea is a worldwide appreciated food product with Chinese production estimated to reach over 3m tons in 2027 and with many valuable health effects. The development of analytical methods to discriminate among green tea samples is induced by economic benefits and to avoid deliberate origin mislabeling and adulteration. In this study, we present a paper-based colorimetric sensor array comprised of six ordinary reagents tailored for the discrimination of green tea extracts of different brands according to differences in the composition of flavonoids. The colorimetric array was rationally designed based on indicators that differentially react with a variety of flavonoids via specific functional groups. 4 μL of each reagent was impregnated onto the paper surface followed by the addition of the green tea extract. After 1 minute, digital images were acquired using a smartphone and the color changes were employed to build differential maps with a unique fingerprint for each green tea sample. Moreover, principal component analysis (PCA) and hierarchical component analysis (HCA) were employed to successfully discriminate among the samples, enabling the origin and adulteration identification of the samples. Therefore, this study provides a simple, effective, low-cost, and portable method for quick discrimination and quality control of green tea samples.