Applicability of multifunctional preprocessing device for simultaneous estimation of spreading of green tea, withering of black tea and shaking of oolong tea

Effect of withering/spreading on the physical and chemical properties of tea: A review

Withering and spreading, though slightly differing in their parameters, share the same aim of moisture reduction in tea leaves, and they have a strong impact on the physical and chemical properties of tea. Even though researchers tend to pay close attention to the characteristic crafts of different teas, increasing investigations begin to focus on the withering process due to its profound effects on the composition and content of quality-related compounds. This review provides an overview of tea withering process to address questions comprehensively during withering. Hence, it is expected in this review to figure out factors that affect withering results, the way withering influences the physical and chemical properties of withered leaves and tea quality, and intelligent technologies and devices targeted at withering processes to promote the modernization of the tea industry. Herein, several key withering parameters, including duration, temperature, humidity, light irradiation, airflow, and more, are tailored to different tea types, demanding further exploration of advanced withering devices and real-time monitoring systems. The development of real-time monitoring technology enables objective and real-time adjustment of withering status in order to optimize withering results. Tea quality, including taste, aroma, and color quality, is first shaped during withering due to the change of composition and content of quality-related metabolites through (non)enzymatic reactions, which are easily influenced by the factors above. A thorough understanding of withering is key to improving tea quality effectively and scientifically.

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.

A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing

The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3-4-1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process.

Simultaneous Screening and Quantification of 479 Pesticides in Green Tea by LC-QTOF-MS

A high-throughput screening and quantification method for 479 pesticides in green tea was established based on solid-phase extraction combined with liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Pesticides were extracted from samples using an optimized SPE (TPT cartridges) procedure. LC-QTOF-MS in All Ions MS/MS scan mode acquired full MS data for quantification and product ion spectra for identification. LC-QTOF-MS quantification was achieved using matrix-matched standard calibration curves to achieve the optimal method accuracy. The method performance characteristics included the linearity, overall recovery, precision, and measurement uncertainty being evaluated. The validation results exhibited a good sensitivity with the LOQs of 5-55 µg/kg, which was satisfactory for their MRLs in China or the EU. The recoveries of more than 92.7% of the 479 pesticides in green tea were 70-120% at the three spiked levels with a precision of ≤20%. Finally, this method was employed to analyze 479 pesticides in 95 tea samples from markets in China. The test results of the tea samples showed that tolfenpyrad, buprofezin, and pyridaben were found with lower concentrations. The method has effectively improved the determination efficiency of pesticide residue screening by high-resolution mass spectrometry in green tea.

A comprehensive review of polyphenol oxidase in tea (Camellia sinensis): Physiological characteristics, oxidation manufacturing, and biosynthesis of functional constituents

Polyphenol oxidase (PPO) is a metalloenzyme with a type III copper core that is abundant in nature. As one of the most essential enzymes in the tea plant (Camellia sinensis), the further regulation of PPO is critical for enhancing defensive responses, cultivating high-quality germplasm resources of tea plants, and producing tea products that are both functional and sensory qualities. Due to their physiological and pharmacological values, the constituents from the oxidative polymerization of PPO in tea manufacturing may serve as functional foods to prevent and treat chronic non-communicable diseases. However, current knowledge of the utilization of PPO in the tea industry is only available from scattered sources, and a more comprehensive study is required to reveal the relationship between PPO and tea obviously. A more comprehensive review of the role of PPO in tea was reported for the first time, as its classification, catalytic mechanism, and utilization in modulating tea flavors, compositions, and nutrition, along with the relationships between PPO-mediated enzymatic reactions and the formation of functional constituents in tea, and the techniques for the modification and application of PPO based on modern enzymology and synthetic biology are summarized and suggested in this article.

Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors

The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO-SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.

Dynamic Changes in Volatile Compounds of Shaken Black Tea during Its Manufacture by GC × GC-TOFMS and Multivariate Data Analysis

Changes in key odorants of shaken black tea (SBT) during its manufacture were determined using headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography−time-of-flight mass spectrometry (GC × GC−TOFMS) and multivariate data analysis. A total of 241 volatiles was identified, comprising 49 aldehydes, 40 esters, 29 alcohols, 34 ketones, 30 aromatics, 24 alkenes, 17 alkanes, 13 furans, and 5 other compounds. A total of 27 volatiles had average relative odor activity values (rOAVs) greater than 1, among which (E)-β-ionone, (E,Z)-2,6-nonadienal, and 1-octen-3-one exhibited the highest values. According to the criteria of variable importance in projection (VIP) > 1, p < 0.05, and |log2FC| > 1, 61 discriminatory volatile compounds were screened out, of which 26 substances were shared in the shaking stage (FL vs. S1, S1 vs. S2, S2 vs. S3). The results of the orthogonal partial least squares discriminate analysis (OPLS-DA) differentiated the influence of shaking, fermentation, and drying processes on the formation of volatile compounds in SBT. In particular, (Z)-3-hexenol, (Z)-hexanoic acid, 3-hexenyl ester, (E)-β-farnesene, and indole mainly formed in the shaking stage, which promoted the formation of the floral and fruity flavor of black tea. This study enriches the basic theory of black tea flavor quality and provide the theoretical basis for the further development of aroma quality control.

Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology

BACKGROUND

Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms.

RESULTS

Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%.

CONCLUSION

This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry.

Simultaneous multi-determination of pesticide residues in black tea leaves and infusion: a risk assessment study

This study aimed to investigate the concentration of 33 pesticide residues in 60 black tea samples collected from Iran, determine their transfer rate, and assess their health risk during brewing. Pesticide extraction and analysis were performed by using a quick, easy, cheap, effective, rugged, and safe (QuEChERS) method and gas chromatography/tandem mass spectrometry (GC-MS/MS), respectively. The limits of detection (LOD) and the limits of quantification (LOQ) of pesticides were ranged 0.1-7.26 and 0.8-24 μg/kg for dried tea leaves and 0.03-3.1 and 0.09-10 μg/L for the tea infusion, respectively. The levels of pesticide residue in 52 (86.67%) out of 60 tea samples were above the LOD (0.1-7.26 μg/kg). Twenty four (40%) of the samples contained pesticides in a concentration higher than the maximum residue limit (MRL) set by the European Commission (EC). Seven out of 33 validated pesticides were detected in dried tea leaf samples that only four of seven, including buprofezin, chlorpyrifos, hexaconazole, and triflumizole, were transferred into tea infusion, demonstrating that the concentrations of pesticides in infusion were raised during brewing. The risk assessment study for detected pesticides in the tea infusion samples indicated that this beverage consumption was safe for consumers, while the mean residue of some pesticides in positive samples was higher than the MRL; therefore, periodic control of these pesticides should be regularly implemented.

Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality

The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors.

Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality

The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of black tea.

Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): near-infrared spectroscopy and evolutionary algorithms

BACKGROUND

Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy.

RESULTS

Cognitive spectroscopy was proposed to combine near-infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K-nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO-SVM model showed the best predictive performance with the correlation coefficients of prediction set (R_p ) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin.

CONCLUSION

The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 2020 Society of Chemical Industry.

Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics

The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near-infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA-SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.