Seasonal variation in non-volatile flavor substances of fresh tea leaves (Camellia sinensis) by integrated lipidomics and metabolomics using UHPLC-Q-Exactive mass spectrometry

Harvest season exerts great influence on tea quality. Herein, the variations in non-volatile flavor substances in spring and summer fresh tea leaves of four varieties were comprehensively investigated by integrating UHPLC-Q-Exactive based lipidomics and metabolomics. A total of 327 lipids and 99 metabolites were detected, among which, 221 and 58 molecules were significantly differential. The molecular species of phospholipids, glycolipids and acylglycerolipids showed most prominent and structure-dependent seasonal changes, relating to polar head, unsaturation and total acyl length. Particularly, spring tea contained higher amount in aroma precursors of highly unsaturated glycolipids and phosphatidic acids. The contents of umami-enhancing amino acids and phenolic acids, e.g., theanine, theogallin and gallotannins, were increased in spring. Besides, catechins, theaflavins, theasinensins and flavone/flavonol glycosides showed diverse changes. These phytochemical differences covered key aroma precursors, tastants and colorants, and may confer superior flavor of black tea processed using spring leaves, which was verified by sensory evaluation.

Pruned tea biomass plays a significant role in functional food production: A review on characterization and comprehensive utilization of abandon-plucked fresh tea leaves

Tea is the second largest nonalcoholic beverage in the world due to its characteristic flavor and well-known functional properties in vitro and in vivo. Global tea production reaches 6.397 million tons in 2022 and continues to rise. Fresh tea leaves are mainly harvested in spring, whereas thousands of tons are discarded in summer and autumn. Herein, pruned tea biomass refers to abandon-plucked leaves being pruned in the non-plucking period, especially in summer and autumn. At present, no relevant concluding remarks have been made on this undervalued biomass. This review summarizes the seasonal differences of intrinsic metabolites and pays special attention to the most critical bioactive and flavor compounds, including polyphenols, theanine, and caffeine. Additionally, meaningful and profound methods to transform abandon-plucked fresh tea leaves into high-value products are reviewed. In summer and autumn, tea plants accumulate much more phenols than in spring, especially epigallocatechin gallate (galloyl catechin), anthocyanins (catechin derivatives), and proanthocyanidins (polymerized catechins). Vigorous carbon metabolism induced by high light intensity and temperature in summer and autumn also accumulates carbohydrates, such as soluble sugars and cellulose. The characteristics of abandon-plucked tea leaves make them not ideal raw materials for tea, but suitable for novel tea products like beverages and food ingredients using traditional or hybrid technologies such as enzymatic transformation, microbial fermentation, formula screening, and extraction, with the abundant polyphenols in summer and autumn tea serving as prominent flavor and bioactive contributors.

Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system

BACKGROUND

The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables.

RESULTS

The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC  = 0.915; RMSECV = 1.39; RP  = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC  = 0.9723, RMSECV = 0.81, RP  = 0.9114, RMSEP = 1.45 and RPD = 2.59.

CONCLUSION

The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

Spectroscopic studies on a natural biomarker for the identification of origin and quality of tea extracts for the development of a portable and field deployable prototype

Even in the era of smart technologies and IoT enabled devices, tea testing technique continues to be a person specific subjective task. In this study, we have employed optical spectroscopy-based detection technique for the quantitative validation of tea quality. In this regard, we have employed the external quantum yield of quercetin at 450 nm (λ_ex = 360 nm), which is an enzymatic product generated by the activity of β-glucosidase on rutin, a naturally occurring metabolite responsible for tea-flavour (quality). We have found that a specific point in a graph representing Optical Density and external Quantum Yield as independent and dependent variables respectively of an aqueous tea extract objectively indicates a specific variety of the tea. A variety of tea samples from various geographical origin have been analysed with the developed technique and found to be useful for the tea quality assessment. The principal component analysis distinctly showed the tea samples originated from Nepal and Darjeeling having similar external quantum yield, while the tea samples from Assam region had a lower external quantum yield. Furthermore, we have employed experimental and computational biology techniques for the detection of adulteration and health benefit of the tea extracts. In order to assure the portability/field use, we have also developed a prototype which confirms the results obtained in the laboratory. We are of the opinion that the simple user interface and almost zero maintenance cost of the device will make it useful and attractive with minimally trained manpower at low resource setting.

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.

Development of a novel wavelength selection method VCPA-PLS for robust quantification of soluble solids in tomato by on-line diffuse reflectance NIR

This work was attempted to evaluate the feasibility of a constructed on-line NIR platform coupled with efficient algorithms for rapid and robust quantification of quality parameter in cherry tomato. Specifically, a system was developed based on shortwave NIR spectroscopy for on-line quality inspection of cherry tomatoes. The spectra were recorded in diffuse reflectance mode from 900 to 1700 nm, and the conveyor belt speed was fixed to five samples per second. Three novel methods, namely variable combination population analysis (VCPA), uninformative variable elimination (UVE) and competitive adaptive reweighed sampling algorithm (CARS) were coupled with partial least square (PLS) for selecting optimal dataset, and modeling. The obtained results showed that under the optimal tuning parameters (N = 100, k = 500, ω = 14, σ = 10%), a total of 512 original variables, only 9 variables (1.75%) were extracted by VCPA. Subsequently, VCPA-PLS yielded outstanding performance in predicting soluble solid content in cherry tomatoes, with a higher correlation coefficient (R_P = 0.9053), and lower root mean square errors (RMSEP = 0.382) in prediction set. This methodology demonstrated the versatile potential of the proposed installation coupled with VCPA methods for on-line detection of total soluble solids in cherry tomatoes.

Quality assessment of instant green tea using portable NIR spectrometer

Caffeine and catechin are two main components of instant green tea, and are essential components of tea quality. This paper mainly focuses on the feasibility of rapidly determining instant green tea components by using a portable near infrared (NIR) spectrometer. The two main components (caffeine and catechin) were studied. In addition, the instrument performance levels of portable and benchtop NIR spectrometers were studied and compared. Quantitative models developed using portable and benchtop spectrometers for measuring caffeine, total catechins, and four individual catechins were established and compared. After preprocessing using standard normal variate (SNV), the Rp values of the caffeine, total catechins, (-)-epigallocatechin, (-)-epigallocatechin 3-gallate, (-)-epicatechin, and (-)-epicatechin gallate in the partial least squares models for a portable NIR spectrometer were 0.974, 0.962, 0.669, 0.945, 0.942 and 0.905, respectively. For a benchtop NIR spectrometer, Rp values were 0.993, 0.958, 0.883, 0.955, 0.966 and 0.936, respectively. Passing-Bablok regression method results indicated no significant differences between the two instruments. A genetic algorithm (GA) and the successive projections algorithm (SPA) were used to screen the wavelength of the NIR spectrum and establish the model. The GA obtained more robust modeling results. This study concludes that the developed portable spectroscopy system combined with appropriate variable selection methods can be effectively used for rapid determination of caffeine, total catechins, and four individual catechins in instant green tea.

Temporal variation in nutrient requirements of tea (Camellia sinensis) in China based on QUEFTS analysis

Fertilisation datasets collected from field experiments (n = 21) in tea-producing areas from 2016 to 2018 were used to build a quantitative evaluation of the fertility of tropical soils (QUEFTS) model to estimate nutrient uptake of tea plants, and to investigate relationships between tea yield and nutrient accumulation. The production of 1000 kg spring tea (based on one bud with two young expanding leaves) required 12.2 kg nitrogen (N), 1.2 kg phosphorus (P), and 3.9 kg potassium (K), and the corresponding internal efficiencies (IEs) for N, P, and K were 82.0, 833.3, and 256.4 kg kg^-1. To produce 1000 kg summer tea, 9.1 kg N, 0.8 kg P, and 3.1 kg K were required, and the corresponding IEs for N, P, and K were 109.9, 1250.0, and 322.6 kg kg^-1. For autumn tea, 8.8 kg N, 1.0 kg P, and 3.2 kg K were required to produce 1000 kg tea, and the corresponding IEs for N, P, and K were 113.6, 1000.0, and 312.5 kg kg^-1. Field validation experiments performed in 2019 suggested that the QUEFTS model can appropriately estimate nutrient uptake of tea plants at a certain yield and contribute to developing a fertiliser recommendation strategy for tea production.

Dynamic characteristics of dough during the fermentation process of Chinese steamed bread

The fermentation process is crucial to the production of Chinese steamed bread (CSB). In order to select suitable indicators as the basis for further research of establishing intelligent monitoring method for dough fermentation state, this study investigated the dynamic characteristics of dough during fermentation. Indicators included water mobility and distribution, starch-pasting properties, content of free amino acid (FAA), volatile organic compounds (VOCs) and electronic nose (E-nose) response value. Starch-pasting properties of dough and relaxation time (T₂₁, T₂₂) did not change significantly during the fermentation process (p < 0.05). The VOCs and FAAs of the dough had significant differences (p < 0.05) in different fermentation times, but no rule was established. The E-nose response value to headspace was most suitable to monitor the fermentation of dough. Principal component analysis (PCA) was performed on E-nose data from 75 samples and the results indicated that samples of different fermentation states were accurately classified.

NIR Spectroscopy Coupled Chemometric Algorithms for Rapid Antioxidants Activity Assessment of Chinese Dates (Zizyphus Jujuba Mill.)

In this work, near-infrared spectroscopy coupled the classical PLS and variable selection algorithms; synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS for rapid measurement of the antioxidant activity of Chinese dates. The chemometric analysis of antioxidant activity assays was performed. The built models were investigated using correlation coefficients of calibration and prediction; root mean square error of prediction, root mean square error of cross-validation and residual predictive deviation (RPD). The correlation coefficient for calibration and prediction sets and RPD values ranged from 0.8503 to 0.9897, 0.8463 to 0.9783 and 1.86 to 4.88, respectively. In addition, variable selection algorithms based on efficient information extracted from acquired spectra were superior to classical PLS. The overall results revealed that near-infrared spectroscopy combined with chemometric algorithms could be used for rapid quantification of antioxidant content in Chinese dates samples.

Rapid prediction of chlorophylls and carotenoids content in tea leaves under different levels of nitrogen application based on hyperspectral imaging

BACKGROUND

Photosynthetic pigments perform critical physiological functions in tea plants. Their content is an essential indicator of photosynthetic efficiency and nutritional status. The present study aimed to predict chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (total Chl), and carotenoid (Car) content in tea leaves under different levels of nitrogen treatment using hyperspectral imaging (HSI) in combination with variable selection algorithms.

RESULTS

A total of 150 samples were collected and scanned using the HSI system. The mean spectrum in the region of interest (ROI) was extracted, and the pigment content was measured by traditional chemical methods. Five and seven optimal wavelengths (OWs) were selected using the regression coefficients (RCs) of partial least squares regression (PLSR) and the second-derivative (2-Der), respectively. The optimal 2-Der-PLSR models for Chl a, Chl b, total Chl, and Car performed remarkably well based on seven OWs with correlation coefficients of prediction (R_P ) of 0.9337, 0.9322, 0.9333 and 0.9036, root mean square errors in prediction (RMSEP) of 0.1100, 0.0511, 0.1620, and 0.0300 mg g^-1 , respectively.

CONCLUSION

The results of this study revealed that HSI combined with variable selection method can be employed as a rapid and accurate method for predicting the content of pigments in tea plants. © 2018 Society of Chemical Industry.

Fast sensing of imidacloprid residue in tea using surface-enhanced Raman scattering by comparative multivariate calibration

This study focused on the fabrication of a rapid, highly sensitive and inexpensive technique for the quantification of imidacloprid residue in green tea, based on surface-enhanced Raman scattering (SERS) using highly roughned surface flower shaped silver nanostructure (as SERS substrate) coupled with the chemometrics algorithm. The basic principle of this method is imidacloprid yielded SERS signal after adsorption on Ag-NF under laser excitation by the electromagnetic enhancement and the intensity of the peak is proportional to the concentration ranging from 1.0 × 10³ to 1.0 × 10^-4 μg/mL. Among the models used, the GA-PLS (Genetic algorithm-partial least square) exhibited superiority to quantify imidacloprid residue in green tea. The model achieved Rp (correlation coefficient) of 0.9702 with RPD of 4.95% in the test set and RSD for precision recorded up to 4.50%. Therefore, the proposed sensor could be employed to quantify imidacloprid residue in green tea for the safeguarding of quality and human health.

Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy

Zearalenone is a contaminant in food and feed products which are hazardous to humans and animals. This study explored the feasibility of the Raman rapid screening technique for zearalenone in contaminated maize. For representative Raman spectra acquisition, the ground maize samples were collected by extended sample area to avoid the adverse effect of heterogeneous component. Regression models were built with partial least squares (PLS) and compared with those built with other variable selection algorithms such as synergy interval PLS (siPLS), ant colony optimization PLS (ACO-PLS) and siPLS-ACO. SiPLS-ACO algorithm was superior to others in terms of predictive power performance for zearalenone analysis. The best model based on siPLS-ACO achieved coefficients of correlation (R_p) of 0.9260 and RMSEP of 87.9132 μg/kg in the prediction set, respectively. Raman spectroscopy combined multivariate calibration showed promising results for the rapid screening large numbers of zearalenone maize contaminations in bulk quantities without sample-extraction steps.

Synthesized Au NPs@silica composite as surface-enhanced Raman spectroscopy (SERS) substrate for fast sensing trace contaminant in milk

With increased concerns on milk safety issues, the development of a simple and sensitive method to detect 2,4-dichlorophenoxyacetic acid (2,4-D), a common contaminant in milk, becomes relevant in safeguarding human health threats that results from its consumption. Surface-enhanced Raman spectroscopy (SERS) shows excellent ability for various targets analysis but its usage for rapid and accurate determination of analyte via SERS presents challenges. This study attempted the quantification of 2,4-dichlorophenoxyacetic acid (2,4-D) residue in milk using a novel SERS active substrate- decorated silica films with Au nanoparticles (Au NPs@ silica) coupled to chemometric algorithms. Au NPs@ silica composite was synthesized as a SERS sensor through self-assembly. Thereafter, the SERS spectrum of 2,4-D extract from milk with different concentrations based on the developed SERS sensor was collected and the spectra were analyzed by partial least squares (PLS), and variable selection algorithms - genetic algorithm-PLS (GA-PLS), competitive-adaptive reweighted sampling-PLS (CARS-PLS) and ant colony optimization-PLS (ACO-PLS), to develop quantitative models for 2,4-D prediction. The results obtained showed that the CARS-PLS model gave the optimum result with LOD of 0.01 ng/mL realized and a determination coefficient in the prediction set of (R_P) = 0.9836 within a linear range of 10^-2 to 10⁶ ng/mL was achieved. Au NPs@ silica SERS sensor combined with CARS-PLS may be employed for rapid quantification of 2,4-D extract from milk towards its quality and safety monitoring.

Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution

Total fungi count (TFC) is a quality indicator of cocoa beans when unmonitored leads to quality and safety problems. Fourier transform near infrared spectroscopy (FT-NIRS) combined with chemometric algorithms like partial least square (PLS); synergy interval-PLS (Si-PLS); synergy interval-genetic algorithm-PLS (Si-GAPLS); Ant colony optimization - PLS (ACO-PLS) and competitive-adaptive reweighted sampling-PLS (CARS-PLS) was employed to predict TFC in cocoa beans neat solution. Model results were evaluated using the correlation coefficients of the prediction (Rp) and calibration (Rc); root mean square error of prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The developed models performance yielded 0.951≤Rp≤0.975; and 3.15≤RPD≤4.32. The models' prediction stability improved in the order of PLS<CARS-PLS<ACO-PLS<Si-PLS<Si-GAPLS. FT-NIRS combined with Si-GAPLS may be employed for in-situ and noninvasive quantification of TFC in cocoa beans for quality and safety monitoring.

Nontargeted Analysis Using Ultraperformance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry Uncovers the Effects of Harvest Season on the Metabolites and Taste Quality of Tea (Camellia sinensis L.)

The chemical composition and taste quality of tea fluctuate seasonally. However, the compounds responsible for the seasonal variation of metabolic pattern and taste quality are far from clear. This study compared the metabolite profiles of green teas of nine varieties that were plucked in spring, summer, and autumn by using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) on a reversed phase column. A multivariate analysis indicated distinct differences among the metabolite phenotypes of teas harvested in different seasons. Heat-map analysis and metabolic pathway analysis demonstrated that flavan-3-ols, theasinensins, procyanidins, quercetin-O-glycosides, apigenin-C-glycosides, and amino acids exhibited sharp seasonal fluctuations. An equivalent quantification of tea tastes showed that in summer and autumn teas, the bitterness and astringency were significantly elevated, whereas umami declined. Metabolite content comparisons and partial least-squares analysis suggested that several flavonoids and amino acids are mainly responsible for the seasonal variations in taste quality.