Characterization of the aroma-active compounds in Xiaokeng green tea by three pretreatment methods combined with gas chromatography-olfactometry (GC-O).

Chinese Xiaokeng green tea (XKGT) possesses elegant and fascinating aroma characteristics, but its key odorants are still unknown. In this study, 124 volatile compounds in the XKGT infusion were identified by headspace-solid phase microextraction (HS-SPME), stir bar sorptive extraction (SBSE), and solvent extraction-solid phase extraction (SE-SPE) combined with gas chromatography-mass spectrometry (GC-MS). Comparing these three pretreatments, we found HS-SPME was more efficient for headspace compounds while SE-SPE was more efficient for volatiles with higher boiling points. Furthermore, SBSE showed more sensitive to capture ketones then was effective to the application of pretreatment of aroma analysis in green tea. The aroma intensities (AIs) were further identified by gas chromatography-olfactometry (GC-O). According to the AI and relative odor activity value (rOAV), 27 compounds were identified as aroma-active compounds. Quantitative descriptive analysis (QDA) showed that the characteristic aroma attributes of XKGT were chestnut-like, corn-like, fresh, and so on. The results of network analysis showed that (E, Z)-2,6-nonadienal, nonanal, octanal and nerolidol were responsible for the fresh aroma. Similarly, dimethyl sulfide, (E, E)-2,4-heptadienal, (E)-2-octenal and ß-cyclocitral contributed to the corn-like aroma. Furthermore, indole was responsible for the chestnut-like and soybean-like aroma. This study contributes to a better understanding of the molecular mechanism of the aroma characteristics of XKGT.

E-nose and colorimetric sensor array combining homologous data fusion strategy discriminating the roasting degree of large-leaf yellow tea.

Different degrees of roasting result in differences in the quality and flavor of large-leaf yellow tea. The current sensory evaluation and chemical detection methods cannot meet the requirement of online differentiation of LYT roasting degree, so an accurate and comprehensive assessment method needs to be developed urgently. First, the two aroma sensing technologies were compared. Two variable screening methods and three recognition algorithms were employed to build discriminant models. The results showed that the discrimination rate of the colorimetric sensor array (CSA) in the prediction set reached 91.89 %, outperforming that of the E-nose. Subsequently, three fusion strategies were applied to improve the discrimination accuracy. The discrimination rate of the middle fusion strategy resulted in an optimal resolution of 94.59 %. The results obtained from the homologous fusion were able to evaluate the roasting degree comprehensively and accurately, which provides a new method and idea for tea aroma quality.

Predicting the storage time of green tea by myricetin based on surface-enhanced Raman spectroscopy.

The quality of green tea changes rapidly due to the oxidation and degradation of polyphenols during storage. Herein, a simple and fast Surface-enhanced Raman spectroscopy (SERS) strategy was established to predict changes in green tea during storage. Raman spectra of green tea with different storage times (2020-2015) were acquired by SERS with silver nanoparticles. The PCA-SVM model was established based on SERS to quickly predict the storage time of green tea, and the accuracy of the prediction set was 97.22%. The Raman peak at 730 cm-1 caused by myricetin was identified as a characteristic peak, which increased with prolonged storage time and exhibited a linear positive correlation with myricetin concentration. Therefore, SERS provides a convenient method for identifying the concentration of myricetin in green tea, and myricetin can function as an indicator to predict the storage time of green tea.

Improving the efficiency of machine learning in simulating sedimentary heavy metal contamination by coupling preposing feature selection methods.

Sediment cores were collected from Taihu Lake in China. The chronology was determined by radionuclide. Heavy metals and magnetic properties of each core slice were assessed, respectively. The concentrations of most heavy metals in sediments surged at 20 cm from the surface, accompanying the increase in the concentrations of single-domain magnetic particles. This may be resulted from the influence of anthropic activities on the lake's environment after the 1970s. Two feature selection methods, random forest (RF) and maximal information coefficient (MIC), were combined with support vector machine (SVM) model to simulate heavy metals, with the inclusion of selected magnetic and physicochemical parameters. Compared with the modeling results obtained with the full set of parameters, a reasonable simulation performance was obtained with RF and MIC. RF performed better than MIC by increasing the R2 of simulation models for Cd, Cr, Cu, Pb, and Sb. For heavy metals with high ecological risks (As, Cd, Cr, Hg, Pb, Sb), the correlation coefficients for observed and predicted data ranged from 0.73 to 0.97 with only 14-27% of the parameters selected by RF as input variables. The RF-RBF-SVM enabled heavy metal predictions based on the magnetic properties of the lake sediments.

Rapid detection of multiple colorant adulteration in Keemun black tea based on hemp spherical AgNPs-SERS.

This study synthesized stable and sensitive hemp spherical AgNPs as the SERS substrate for the simultaneous and rapid detection of sunset yellow, lemon yellow, carmine and erythrosine adulteration in black tea. With R6G as the probe molecule, the AgNPs were determined to have satisfactory stability over 60 days with an enhancement factor of 108. The effects of three variable screening methods on model performance were compared. Among them, CARS-PLS exhibited superior performance for the quantification of all the four colorants, with prediction set correlation coefficients of 0.95, 0.97, 0.99 and 0.88, respectively. The differentiation of the mixed colorants was also achieved, with recoveries ranging from 91.87 % to 106.5 % with RSD value <1.97 %, demonstrating the high accuracy and precision of the proposed method. The results indicate that AgNPs-based SERS is an effective method and has substantial potential for application in the identification and quantification of colorant in tea.

Heavy metals in submicronic particulate matter (PM1) from a Chinese metropolitan city predicted by machine learning models.

The aim of this study was to establish a method for predicting heavy metal concentrations in PM1 (aerosol particles with an aerodynamic diameter ≤ 1.0 µm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) methods. The annual average PM1 concentration was 26.31 µg/m3 (range: 7.00-73.40 µg/m3). The concentrations of most metals were higher in winter and lower in autumn and summer. Mn and Ni had the highest noncarcinogenic risk, and Cr the highest carcinogenic risk. The hazard index was below safe limit, and the integrated carcinogenic risk was less than precautionary value. There were no obvious differences in the simulation performances of BP-ANN and SVM models. However, in both models many elements had better simulation effects when input variables were atmospheric pollutants (SO2, NO2, CO, O3 and PM2.5) rather than PM1 and meteorological factors (temperature, relative humidity, atmospheric pressure and wind speed). Models performed better for Pb, Tl and Zn, as evidenced by training R and test R values consistently >0.85, whereas their performances for Ti and V were relatively poor. Predicted results by the fully trained models showed atmospheric heavy metal pollution was heavier in December and January and lighter in August and July of 2019. For the period covering the COVID-19 outbreak in China, from January to March 2020, most of the predicted element concentrations were lower than in 2018 and 2019, and the concentrations of nearly all metals were lowest during the nationwide implementation of countermeasures taken against the pandemic.

Feature-Based Molecular Networking Analysis of the Metabolites Produced by In Vitro Solid-State Fermentation Reveals Pathways for the Bioconversion of Epigallocatechin Gallate.

Dark teas are prepared by a microbial fermentation process. Flavan-3-ol B-ring fission analogues (FBRFAs) are some of the key bioactive constituents that characterize dark teas. The precursors and the synthetic mechanism involved in the formation of FBRFAs are not known. Using a unique solid-state fermentation system with ß-cyclodextrin inclusion complexation as well as targeted chromatographic isolation, spectroscopic identification, and Feature-based Molecular Networking on the Global Natural Products Social Molecular Networking web platform, we reveal that dihydromyricetin and the FBRFAs, including teadenol A and fuzhuanin A, are derived from epigallocatechin gallate upon exposure to fungal strains isolated from Fuzhuan brick tea. In particular, the strains from subphylum Pezizomycotina were key drivers for these B-/C-ring oxidation transformations. These are the same transformations seen during the fermentation process of dark teas. These discoveries set the stage to enrich dark teas and other food products for these health-promoting constituents.

Biomagnetic monitoring combined with support vector machine: a new opportunity for predicting particle-bound-heavy metals.

Biomagnetic monitoring includes fast and simple methods to estimate airborne heavy metals. Leaves of Osmanthus fragrans Lour and Ligustrum lucidum Ait were collected simultaneously with PM10 from a mega-city of China during one year. Magnetic properties of leaves and metal concentrations in PM10 were analyzed. Metal concentrations were estimated using leaf magnetic properties and meteorological factors as input variables in support vector machine (SVM) models. The mean concentrations of many metals were highest in winter and lowest in summer. Hazard index for potentially toxic metals was 5.77, a level considered unsafe. The combined carcinogenic risk was higher than precautionary value (10-4). Ferrimagnetic minerals were dominant magnetic minerals in leaves. Principal component analysis indicated iron & steel industry and soil dust were the common sources for many metals and magnetic minerals on leaves. However, the poor simulation results obtained with multiple linear regression confirmed strong nonlinear relationships between metal concentrations and leaf magnetic properties. SVM models including leaf magnetic variables as inputs yielded better simulation results for all elements. Simulations were promising for Ti, Cd and Zn, whereas relatively poor for Ni. Our study demonstrates the feasibility of prediction of airborne heavy metals based on biomagnetic monitoring of tree leaves.

Particulate matter exposure disturbs inflammatory cytokine homeostasis associated with changes in trace metal levels in mouse organs.

Few studies have focused on the impact of particulate matter (PM) exposure with respect to the relationship between PM-induced inflammation and the levels of trace metals in tissues and organs. In this study, C57BL/6 male mice were exposed to ambient air alongside control mice breathing air filtered through a high-efficiency particulate air (HEPA) filter. In both groups, mRNA levels of pro- and anti-inflammatory cytokines were measured after 4, 8 and 12 weeks together with the trace metal contents of the lungs, heart, liver, hippocampus and blood. PM exposure resulted in a general upward trend in the levels of pro-inflammatory cytokines in lung, heart, liver and hippocampus. By contrast, IL-10 mRNA expression varied depending on the organ, with a continuous upward trend in heart and liver and an up-regulation at 8 weeks followed by a down-regulation at 12 weeks in lung and hippocampus. The disturbed homeostasis of inflammatory cytokines was accompanied by changes in trace metal levels in the mice. These alterations may have constituted a compensatory effect conferring protection from inflammatory damage. However, prolonged PM exposure finally resulted in the deficiency of several essential trace metals in the lungs and hippocampus, which may have contributed to the observed histological changes typical of an inflammatory response.

Evaluation of astringent taste of green tea through mass spectrometry-based targeted metabolic profiling of polyphenols.

The contributions of many polyphenols other than catechins and flavonols to the astringency of tea are often neglected. Here, the contributions of polyphenols were assessed through targeted metabolic profiling using liquid chromatography-mass spectrometry. A total of 86 polyphenols were identified from 47 green tea samples with varying astringency scores, of which 76 compounds were relatively quantified. A correlation matrix analysis revealed that monohydroxyflavonol and acyl derivatives of polyphenols, except for galloylated catechins, had negative correlations with the other polyphenols. Principal component analysis revealed a distinct separation of monohydroxyflavonol and acyl derivatives of polyphenols from the other polyphenols. The results suggest metabolic differences in terms of hydroxylation, glycosylation, acylation, and condensation reactions of polyphenols between the different tea samples, particularly between the samples obtained in spring and autumn. The correlation analysis showed that metabolic fluxes toward the aforementioned four reactions of polyphenols played unique roles in the astringency of tea infusions.

The impacts of brewing in glass tumblers and thermos vacuum mugs on the aromas of green tea (Camellia sinensis).

This study investigated the effect of brewing apparatus on the aromatic feature of tea infusion. Huangshan Maofeng tea infusion was brewed under glass tumblers (GT) or thermos vacuum mugs (TVM) for up to 180 min. Tea infusion sensory attributes were evaluated using quantitative descriptive analysis and the composition of volatiles were analyzed using headspace solid phase microextraction coupled with gas chromatography-mass spectrometry. Results showed that GT tea infusion at each brewing duration possessed stronger 'Pure', 'Fresh' and 'Grassy' attributes than TVM tea infusion, whereas TVM tea infusion showed a higher intensity on 'Steamed' aroma. A total of 74 volatiles were detected in tea infusion, and aldehydes and alcohols appeared to be the major volatiles. Total aldehydes concentration percentage decreased in tea infusion with brewing process, whereas an increase on total alcohol percentage was found. Principal component analysis indicated that brewing duration and apparatus played vital roles in altering the volatile composition in tea infusion, whereas orthogonal partial least squares discriminant analysis (OPLS-DA) revealed that GT tea infusion samples were separated from TVM tea infusion samples. OPLS-DA also screened 20 volatiles that significantly contributed to the differentiation of GT and TVM tea infusion.

Recommended storage temperature for green tea based on sensory quality.

This study aimed to evaluate the effect of storage temperature on the alteration of the sensory quality of tea. Huangshan Maofeng tea was stored at - 80 °C, - 20 °C, 4 °C, or room temperature for up to 150 days. The physicochemical parameters, taste-related components, appearance color, volatile compounds and sensory quality of tea were analyzed and compared. Results showed that storing tea at - 80 °C and - 20 °C effectively preserved the physicochemical parameters, taste-related compounds and appearance color in tea. Multivariate statistical analysis (PCA and OPLS-DA) indicated that tea stored at - 80 °C exhibited a similar volatiles composition as fresh tea based on gas chromatography-mass spectrometry, whereas the composition of volatiles was significantly altered in tea stored at 4 °C after 100 days of storage. Sensory evaluation illustrated that tea stored at - 80 °C and - 20 °C remained the freshness regarding leaves appearance and tea infusion color, taste and aroma, whereas an obvious decrease on the tea freshness was found in tea stored at 4 °C and room temperature. These findings indicated that storage temperature played a vital role in altering the aromatic and sensory quality of Huangshan Maofeng tea and the recommended tea storage temperature was - 80 °C or - 20 °C.

In vitro antioxidant and immunostimulating activities of polysaccharides from Ginkgo biloba leaves.

Ginkgo biloba leaves (GBLs) are used as herbal dietary supplements and medicine worldwide. In this study, crude GBL polysaccharides (GBPSs) were extracted and further purified on a DEAE (diethylaminoethanol) Sepharose Fast Flow column to obtain GBPS-2 and GBPS-3. The molecular weights of GBPS-2 and GBPS-3 were 672 and 723 kDa, respectively. GBPS-2 and GBPS-3 were typical acidic heteropolysaccharides, composed of mannose (Man), rhamnose (Rha), glucuronic acid (GlcA), galacturonic acid (GalA), glucose (Glc), galactose (Gal), and arabinose (Ara) (molar ratio: 0.08:0.12:0.16:0.06:0.11:1.00:0.32) and Man, Rha, GlcA, GalA, Gal, and Ara (molar ratio: 0.92:1.00:0.83:0.11:0.42:0.23), respectively. GBPS-2 and GBPS-3 exhibited limited scavenging abilities for the hydroxyl and 2,2­diphenyl­1­picrylhydrazyl radicals as well as noticeable scavenging effects on superoxide radicals and 2,2'­azino­bis(3­ethylbenzothiazoline­6­sulphonic acid) radicals. Furthermore, GBPS-2 and GBPS-3 significantly increased the phagocytosis of macrophages and promoted the production of NO, tumor necrosis factor (TNF)-α, interleukin (IL)-1ß, and IL-6. Thus, GBPS-2 and GBPS-3 exhibit potential application as functional food supplements.

Effect of interaction of epigallocatechin gallate and flavonols on color alteration of simulative green tea infusion after thermal treatment.

Epigallocatechin gallate (EGCG) and flavonols are important phenolic compounds in green tea. These compounds are sensitive to thermal condition and their structural alteration results in making browning the green tea infusion. This study aimed to research the interaction between EGCG and flavonols during thermal infusion. EGCG and flavonols model solutions were prepared based on concentration in green tea infusion, and their colors appearance and attributes were analyzed in 10 h by thermal treatment. Results showed that kaempferol, quercetin, and myricetin accelerated the oxidation of EGCG and made the model solution browning. HPLC analysis revealed there was an obvious shift of a broaden peak in the mixed model solution of EGCG and flavonols after thermal treatment. This broaden peak was further purified on HPLC and solid phase extraction methods to yield colored complexes. The complexes showed the maximal absorption around 424, 442, and 482 nm. MS/MS analysis revealed that the complexes possessed of three components those were consisted of the interaction between EGCG and myricetin. These results indicated that the interaction between EGCG and flavonols might form complexes during thermal treatment, The complexes were contributed to make green tea infusion browning.

Effect of quantity and intensity of pulsed light on human non-visual physiological responses.

BACKGROUND: Exposure to pulsed light results in non-visual physiological responses in humans. The present study aims to investigate whether such non-visual effects are influenced to a greater extent by the intensity of lighting or by the power (quantity) of lighting. METHODS: >Twelve healthy young male participants (23 ± 0.3 years, 21-24 age range) were recruited for the present study. Participants were exposed to light of varying levels of intensity and quantity whose frequency was held constant across the conditions, which consisted of exposure to blue (different intensity, constant quantity) and white (constant intensity, different quantity) LEDs. Pupillary constriction, electroencephalogram (EEG) alpha band ratio, subjective sleepiness, concentration and perception of blueness were measured. RESULTS: Pupillary constriction and subjective concentration were significantly greater under the high-intensity and short pulse width (HS) condition than under the low-intensity and long pulse width (LL) conditions at three time points during exposure to high-intensity light. However, no significant differences were observed among the results at the three time points during exposure to different quantities of pulsed light. CONCLUSIONS: The results of the present study indicate that non-visual influences of pulsed light on physiological function are mainly determined not by the quantity but by the intensity of the emitted light, with relatively higher levels of intensity producing more significant physiological changes, suggesting potent excitation of intrinsically photosensitive retinal ganglion cells.