A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model.

The paper proposes an A-ResNet model to improve ResNet. The residual attention module with shortcut connection is introduced to enhance the focus on the target object; the dropout layer is introduced to prevent the overfitting phenomenon and improve the recognition accuracy; the network architecture is adjusted to accelerate the training convergence speed and improve the recognition accuracy. The experimental results show that the A-ResNet model achieves a top-1 accuracy improvement of about 2% compared with the traditional ResNet network. Image recognition is one of the core technologies of computer vision, but its application in the field of tea is relatively small, and tea recognition still relies on sensory review methods. A total of 1,713 images of eight common green teas were collected, and the modeling effects of different network depths and different optimization algorithms were explored from the perspectives of predictive ability, convergence speed, model size, and recognition equilibrium of recognition models.

Quality chemical analysis of crush-tear-curl (CTC) black tea from different geographical regions based on UHPLC-Orbitrap-MS.

Crush-tear-curl (CTC) black tea is a popular beverage, owing to its unique taste characteristics and health benefits. However, differences in the taste quality and chemical profiles of CTC black tea from different geographical regions remain unclear. In this study, 28 CTC black tea samples were collected from six geographical regions and analyzed using electronic tongue and ultrahigh performance liquid chromatography-Orbitrap-mass spectroscopy. The e-tongue analysis indicated that each region's CTC black tea has its own relatively prominent taste characteristics: Sri Lanka (more umami and astringent), North India (more umami), China (more sweetness and astringent), South India (moderate umami and sweetness), and Kenya (moderate umami and astringent). Based on multivariate statistical analysis, 78 metabolites were tentatively identified and used as potential markers for CTC black tea of different origins, mainly including amino acids, flavone/flavonol glycosides, and pigments. Different metabolites, which contributed to the taste characteristics of CTC black tea, were clarified by partial least squares regression correlation analysis. Our findings may serve as useful references for future studies on origin traceability and quality characteristic determination of CTC black teas. PRACTICAL APPLICATION: This study provides useful references for future studies on the origin traceability and taste characteristic determination of CTC black teas from different geographical regions.

Region identification of Xinyang Maojian tea using UHPLC-Q-TOF/MS-based metabolomics coupled with multivariate statistical analyses.

Xinyang Maojian tea is a kind of famous roasted green tea produced in the middle of China. Ultra-high performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-Q-TOF/MS)-based metabolomics coupled with multivariate statistical analyses, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were carried out in XMMJTs collected from Luoshan, Shangcheng, and Shihe Counties, respectively. Additionally, seven catechins, four flavonoids, two purine alkaloids, and gallic acid contents were determined by HPLC. Differential metabolites were selected by p-value <0.05, and fold change >1.50 or < 0.66 among 745 detected metabolites in metabolomics analysis. The results showed significant (p < 0.05) differences of three catechins including (-)-epicatechin, (-)-epicatechin gallate, and (-)-gallocatechin gallate, four flavonoids (i.e. quercetin, kaempferol, myricetin, and rutin), and theobromine among three various regions, and significant (p < 0.05) differences of (-)-epicatechin gallate, (-)-epigallocatechin, (+)-catechin, gallic acid, and kaempferol between Shuchazao and Group cultivar. The HCA showed that, except for two samples (i.e. LS 2 and SH 2) of Shuchazao cultivar clustered together, others could be clustered completely according to production place. The 63 relevant differential metabolites could achieve the purpose of region identification through PCA. Kyoto encyclopedia of genes and genomes (KEGG) metabolic pathway analysis elaborated the impact of geographical origin and tea cultivar on physiological metabolism in tea tree. PRACTICAL APPLICATION: Ultra-high performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-Q-TOF/MS)-based liquid chromatography-tendem mass spectrometry (LC-MS/MS) metabolomics coupled with multivariate statistical analyses, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA), revealed 63 differential metabolites related to production place, which contributed to the region identification of Xinyang Maojian teas.

Differentiating Pu-erh raw tea from different geographical origins by 1 H-NMR and U-HPLC/Q-TOF-MS combined with chemometrics.

Pu-erh tea is believed to be a beneficial beverage for health due to its many kinds of pharmacological effects. Nevertheless, detailed information related to differences in metabolites of Pu-erh raw tea from different geographical origins remains scarce. In this study, 43 elements were found in water-soluble components of Pu-erh raw tea by highly sensitive ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (U-HPLC/Q-TOF-MS). The characteristic groups of 29 metabolites from nondestructive proton nuclear magnetic resonance (1 H-NMR) spectroscopy were assigned. The variables contributed largely to the origin classification, mainly including valine, threonine, chlorogenic acid, quinic acid, epiafzelechin, and gallic acid ester, were screened out by sparse partial least squares discriminant analysis (sPLS-DA) method. This study provided a feasible and rapid technique for distinguishing Pu-erh tea from different areas by 1 H-NMR combined with sPLS-DA.

Comparative analysis of chloroplast genomes indicated different origin for Indian tea (Camellia assamica cv TV1) as compared to Chinese tea.

Based upon the morphological characteristics, tea is classified botanically into 2 main types i.e. Assam and China, which are morphologically very distinct. Further, they are so easily pollinated among themselves, that a third category, Cambod type is also described. Although the general consensus of origin of tea is India, Burma and China adjoining area, yet specific origin of China and Assam type tea are not yet clear. Thus, we made an attempt to understand the origin of Indian tea through the comparative analysis of different chloroplast (cp) genomes under the Camellia genus by performing evolutionary study and comparing simple sequence repeats (SSRs) and codon usage distribution patterns among them. The Cp genome based phylogenetic analysis indicated that Indian Tea, TV1 formed a different group from that of China tea, indicating that TV1 might have undergone different domestications and hence owe different origins. The simple sequence repeats (SSRs) analysis and codon usage distribution patterns also supported the clustering order in the cp genome based phylogenetic tree.

Characterizing microRNAs and their targets in different organs of Camellia sinensis var. assamica.

To comprehensively annotate miRNAs and their targets in tea plant, Camellia sinensis, we sequenced small and messenger RNAs of 9 samples of Camellia sinensis var. assamica (YK-10), a diploid elite cultivar widely grown in southwest China. In order to identify targets of miRNAs, we sequenced two degradome sequencing profiles from leaves and roots of YK-10, respectively. By analyzing the small RNA-Seq profiles, we newly identified 137 conserved miRNAs and 23 species specific miRNAs in the genome of YK-10, which significantly improved the annotation of miRNAs in tea plant. Approximately 2000 differently expressed genes were identified when comparing RNA-Seq profiles of any two of the three organs selected in the study. Totally, more than 5000 targets of conserved miRNAs were identified in the two degradome profiles. Furthermore, our results suggest that a few miRNAs play roles in the biosynthesis pathways of theanine, caffeine and flavonoid. These results enhance our understanding of small RNA guided gene regulations in different organs of tea plant.

Analytical strategy coupled to chemometrics to differentiate Camellia sinensis tea types based on phenolic composition, alkaloids, and amino acids.

Catechins, amino acids, and alkaloids are primary chemical components of tea and play a crucial role in determining tea quality. Their composition and content largely vary among different types of tea. In this study, a convenient chemical classification method was developed for six Camellia sinensis tea types (white, green, oolong, black, dark, and yellow) based on the quantification of their major components. Twenty-one free amino acids, 6 catechins, 2 alkaloids, and gallic acid in 24 teas were quantified using ultra-high-performance liquid chromatography (UHPLC). The total catechin contents in these tea samples ranged from 10.96 to 95.67 mg/g, while total free amino acid content ranged from 2.63 to 25.89 mg/g. Theanine (Thea) was the most abundant amino acid in all tea varieties. Catechin and amino acid levels in tea were markedly reduced upon fermentation of tea. Furthermore, high-temperature processing (roasting) during tea production induced degradation and epimerization of catechins, yielding epimerized catechins, simple catechins, and gallic acid. Principal component analysis revealed that major ester-catechins (EGCG and ECG), major amino acids (Thea), and major alkaloids (caffeine) are potential factors for distinguishing different types of tea. Linear discriminant analysis showed that 100% of teas were correctly classified in which (+)-catechin, ECG, EGC, gallic acid, GABA, cysteine, lysine, and threonine were the most discriminating compounds. This study shows that quantification of the major tea components combined with chemometric analysis, can serve as a simple, convenient, and reliable approach for classifying tea according to fermentation level. PRACTICAL APPLICATION: Different Camellia sinensis tea types can be produced worldwide but it is still challenging to know which chemical markers can be used to trace their production. in this paper we used a targeted methodology to classify six tea types (white, green, oolong, black, dark, and yellow) based on phenolic composition, alkaloids, and amino acids. The main chemical markers responsible for the discrimination were pinpointed with the use of chemometric tools.

Synchronous fluorescence spectroscopy combined with chemometrics for determination of total phenolic content and antioxidant activity in different tea types.

BACKGROUND: The aim of this study is to monitor the antioxidant activity (AA) and total phenolic content (TPC) variations of different kinds of teas depending on the infusion temperature and time by using synchronous fluorescence spectroscopy (SFS) combined chemometrics as a rapid method. In this study, black tea, oolong tea, green tea, and green tea powder (matcha) samples were brewed at 80 °C and 96 °C for 2.5 to 30 min. Synchronous fluorescence spectra were recorded at different wavelength interval (Δλ) values for optimal models. AA and TPC of tea samples were determined by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and Folin-Ciocalteu methods as reference methods, respectively. Partial least square (PLS) method was used for correlation between reference methods and SFS method. RESULTS: Results showed that SFS combined with chemometrics could be an alternative rapid way to monitor TPC and AA of teas with 0.932 and 0.918 of validation R2 values in fermented teas (black tea-oolong) while with 0.961 and 0.860 of validation R2 values in non-fermented teas (green tea and green tea powder), respectively. Limit of detection (LOD) and root mean square error of prediction (RMSEP) values were ≤ 6.61 µg mL-1 and ≤ 17.42, respectively. CONCLUSION: Based on the lowest R2 value (0.860) on TPC analysis, the proposed method is more appropriate for AA analysis of green tea and green tea powder. Furthermore, infusion time was more effective for obtaining different amounts of TPC and AA in fermented tea types while only infusion temperature was effective on green tea and green tea powder samples. Therefore, obtained calibration-validation models gave better results for fermented tea types.

Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging.

BACKGROUND: The quality of fresh tea leaves after harvest determines, to some extent, the quality and price of commercial tea. A fast and accurate method to evaluate the quality of fresh tea leaves is required. RESULTS: In this study, the potential of hyperspectral imaging in the range of 328-1115 nm for the rapid prediction of moisture, total nitrogen, crude fiber contents, and quality index value was investigated. Ninety samples of eight tea-leaf varieties and two picking standards were tested. Quantitative partial least squares regression (PLSR) models were established using a full spectrum, whereas multiple linear regression (MLR) models were developed using characteristic wavelengths selected by a successive projections algorithm (SPA) and competitive adaptive reweighted sampling. The results showed that the optimal SPA-MLR models for moisture, total nitrogen, crude fiber contents, and quality index value yielded optimal performance with coefficients of determination for prediction (R2 p) of 0.9357, 0.8543, 0.8188, 0.9168; root mean square error of 0.3437, 0.1097, 0.3795, 1.0358; and residual prediction deviation of 4.00, 2.56, 2.31, and 3.51, respectively. CONCLUSION: The results suggested that the hyperspectral imaging technique coupled with chemometrics was a promising tool for the rapid and nondestructive measurement of tea-leaf quality, and had the potential to develop multispectral imaging systems for future online detection of tea-leaf quality. © 2020 Society of Chemical Industry.

Use of mineral multi-elemental analysis to authenticate geographical origin of different cultivars of tea in Guizhou, China.

BACKGROUND: The geographical origin of tea (Camellia sinensis) can be traced using mineral elements in its leaves as fingerprints. However, the role that could be played by soil mineral elements in the geographical authentication of tea leaves has been unclear. In this study, 22 mineral elements in 73 pairs of tea leaves and soils from three regions (Pu'an, Duyun, and Liping) in Guizhou, China, were determined using inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma atomic emission spectrometry (ICP-AES). The mineral element concentrations were processed by multivariate statistical analysis, including one-way analysis of variance (ANOVA), correlation analysis, principal component analysis (PCA), and stepwise linear discriminant analysis (S-LDA). RESULTS: Based on a one-way ANOVA, tea leaves and soils with different origins possessed unique mineral element fingerprints. Sixteen mineral element concentrations in tea leaves were significantly correlated with those in soils (P < 0.05). The geographical origins of tea leaves were effectively differentiated using the 16 correlated mineral elements combined with PCA. The S-LDA model offered a 100% differentiation rate, and six indicative elements (phosphorus, Sr, U, Pb, Cd, and Cr) were selected as important fingerprinting markers for the geographic traceability of tea leaves. The accurate discrimination rate of geographical origin was unaffected by the cultivars of tea in the S-LDA model. CONCLUSIONS: Mineral elements in soils played an important role in the geographical authentication of tea leaves. Mineral elemental concentrations with significant correlations between tea leaves and soils could be robust, and could be used to trace the geographical origins of tea leaves. © 2020 Society of Chemical Industry.

Identification of tea based on CARS-SWR variable optimization of visible/near-infrared spectrum.

BACKGROUND: The identification of tea varieties is essential to obtain high-quality tea that can command a high price. To identify tea varieties quickly and non-destructively, and to fight against counterfeit and inferior products in the tea market, a new method of visible / near-infrared spectrum processing based on competitive adaptive reweighting algorithms-stepwise regression analysis (CARS-SWR) variable optimization is proposed in this paper. RESULTS: The spectral data of five different tea varieties were obtained by visible / near-infrared spectrometry. The spectral data were preprocessed by the multivariate scattering correction (MSC) algorithm. First, 20 wavelength variables were selected by CARS, and then six optimal wavelength variables were selected using the SWR method, based on the CARS optimal variables. The generalized regression neural network (GRNN) classification model and probabilistic neural network (PNN) classification model were established, based on spectral information from the full wavelength, the CARS preferred wavelength variable, the SWR preferred wavelength variable, and the CARS-SWR preferred wavelength variable. CONCLUSION: It was found that the CARS-SWR-PNN model had the best classification effect by comparing different modeling results. The classification accuracy of its training set and test set reached 100%. This shows that the CARS-SWR preferred variable method combined with the visible / near-infrared spectrum is feasible for the rapid and non-destructive identification of tea varieties. © 2019 Society of Chemical Industry.

Genome-wide identification, characterization, and expression analysis of nucleotide-binding leucine-rich repeats gene family under environmental stresses in tea (Camellia sinensis).

Plants often use nucleotide-binding leucine-rich repeats (NLRs) to recognize specific virulence proteins and activate the hypersensitive response thereby defending against invaders. However, data on NLRs and the resistance mechanism of NLR protein mediation in tea plant are extremely limited. In this study, 400 and 303 CsNLRs were identified from the genomes of C. sinensis var. sinensis (CSS) and C. sinensis var. assamica (CSA), respectively. Phylogenetic analysis revealed that the numbers in CNL groups are predominant in both CSS and CSA. RNA-Seq revealed that the expression of CsNLRs is induced by Colletotrichum fructicola, cold, drought, salt stress and exogenous methyl jasmonate. The 21 CsCNLs that are highly expressed in tea plant under biotic and abiotic stresses as well as during bud dormancy and in different tissues are identified. Gene structure analysis revealed several cis-regulatory elements associated with phytohormones and light responsiveness in the promoter regions of these 21 CsCNLs.

Characterization of genome-wide genetic variations between two varieties of tea plant (Camellia sinensis) and development of InDel markers for genetic research.

BACKGROUND: Single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) are the major genetic variations and are distributed extensively across the whole plant genome. However, few studies of these variations have been conducted in the long-lived perennial tea plant. RESULTS: In this study, we investigated the genome-wide genetic variations between Camellia sinensis var. sinensis 'Shuchazao' and Camellia sinensis var. assamica 'Yunkang 10', identified 7,511,731 SNPs and 255,218 InDels based on their whole genome sequences, and we subsequently analyzed their distinct types and distribution patterns. A total of 48 InDel markers that yielded polymorphic and unambiguous fragments were developed when screening six tea cultivars. These markers were further deployed on 46 tea cultivars for transferability and genetic diversity analysis, exhibiting information with an average 4.02 of the number of alleles (Na) and 0.457 of polymorphism information content (PIC). The dendrogram showed that the phylogenetic relationships among these tea cultivars are highly consistent with their genetic backgrounds or original places. Interestingly, we observed that the catechin/caffeine contents between 'Shuchazao' and 'Yunkang 10' were significantly different, and a large number of SNPs/InDels were identified within catechin/caffeine biosynthesis-related genes. CONCLUSION: The identified genome-wide genetic variations and newly-developed InDel markers will provide a valuable resource for tea plant genetic and genomic studies, especially the SNPs/InDels within catechin/caffeine biosynthesis-related genes, which may serve as pivotal candidates for elucidating the molecular mechanism governing catechin/caffeine biosynthesis.

Metabolomics Based on UHPLC-Orbitrap-MS and Global Natural Product Social Molecular Networking Reveals Effects of Time Scale and Environment of Storage on the Metabolites and Taste Quality of Raw Pu-erh Tea.

Raw Pu-erh tea (RPT) needs ageing before drinking. However, the influence from environment and time of storage on chemical profile and flavor of RPT is unclear. In this study, the RPTs stored in wet-hot or dry-cold environment for 1-9 years were assessed using metabolomics based on UHPLC-Orbitrap-MS and global natural product social (GNPS) feature-based molecular networking as well as electronic tongue measurement. The results exhibited that the chemical profiles of RPTs were similar at an early stage but started to differentiate from each other at the 5th and the 7th year in wet-hot and dry-cold environments. The discriminating features including N-ethyl-2-pyrrolidinone-substituted flavan-3-ols (flavoalkaloids), unsaturated fatty acids, lysophosphatidylcholines, flavan-3-ols, amino acids, and flavonol-O-glycosides among the three chemical profiles were discovered and analyzed by means of multivariate statistics, GNPS multilibraries matching, and SIRIUS calculation. The metabolomic data were consistent with the results obtained through electronic tongue measurement.

Endophytic Bacteria as Contributors to Theanine Production in Camellia sinensis.

Theanine is the most abundant non-protein amino acid in Camellia sinensis, but it is not known how a tea plant accumulates such high levels of theanine. The endophyte isolated from in vitro grown plantlets of C. sinensis cultivars was identified as Luteibacter spp., showing strong biocatalytic activity for converting both glutamine and ethylamine to theanine. Theanine was secreted outside of the bacteria. The endophyte isolated from in vitro plantlets of Camellia oleifera cultivar was identified as Bacillus safensis and did not convert glutamine and ethylamine to theanine. Enzymatic assays in vitro indicated that γ-glutamyltranspeptidases rCsEGGTs from the endophyte Luteibacter strains converted glutamine and ethylamine to theanine at higher rates than rCsGGTs from C. sinensis. This is the first report on theanine biosynthesis by an endophyte from C. sinensis, which provides a new pathway to explore the mechanism of theanine biosynthesis in C. sinensis and the interactions between an endophyte and tea plants.

Gene Coexpression Networks Reveal Key Drivers of Flavonoid Variation in Eleven Tea Cultivars (Camellia sinensis).

Following the recent completion of the draft genome sequence of the tea plant, high-throughput decoding of gene function, especially for those involved in complex secondary metabolic pathways, has become a major challenge. Here, we profiled the metabolome and transcriptome of 11 tea cultivars, and then illustrated a weighted gene coexpression network analysis (WGCNA)-based system biological strategy to interpret metabolomic flux, predict gene functions, and mine key regulators involved in the flavonoid biosynthesis pathway. We constructed a multilayered regulatory network, which integrated the gene coexpression relationship with the microRNA target and promoter cis-regulatory element information. This allowed us to reveal new uncharacterized TFs (e.g., MADSs, WRKYs, and SBPs) and microRNAs (including 17 conserved and 15 novel microRNAs) that are potentially implicated in different steps of the catechin biosynthesis. Furthermore, we applied metabolic-signature-based association method to capture additional key regulators involved in catechin pathway. This provides important clues for the functional characterization of five SCPL1A acyltransferase family members, which might be implicated in the production balance of anthocyanins, galloylated catechins, and proanthocyanins. Application of an "omics"-based system biology strategy should facilitate germplasm utilization and provide valuable resources for tea quality improvement.

Genome-Wide Association Study to Identify Favorable SNP Allelic Variations and Candidate Genes That Control the Timing of Spring Bud Flush of Tea (Camellia sinensis) Using SLAF-seq.

The timing of spring bud flush (TBF) is of economic importance for tea plant (Camellia sinensis) breeding. We employed a genome-wide association study (GWAS) to identify favorable single nucleotide polymorphism (SNP) allelic variations as well as candidate genes that control TBF of C. sinensis using specific-locus-amplified fragment sequencing (SLAF-seq) in a diversity panel comprising 151 tea plant germplasm resources. GWAS analysis revealed 26 SNPs associated with TBF in three years, and we eventually identified a final significant SNP for TBF. To identify candidate genes possibly related to TBF, we screened seven candidate genes within 100 kb regions surrounding the trait-related SNP loci. Furthermore, the favorable allelic variation, the "TT" genotype in the SNP loci, was discovered, and a derived cleaved amplified polymorphism (dCAPS) marker was designed that cosegregated with TBF, which could be used for marker-assisted selection (MAS) breeding in C. sinensis. The results obtained from this study can provide a theoretical and applied basis for the MAS of early breeding in tea plants in the future.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics.

BACKGROUND: Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds. RESULTS: Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples. CONCLUSION: The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.

Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms.

In order to rapidly and nondestructively identify tea grades, fluorescence hyperspectral imaging (FHSI) technology was proposed in this paper. A total of 309 Tieguanyin tea samples with three different grades were collected and the fluorescence hyperspectral data was acquired by hyperspectrometer (400 to 1000 nm). The characteristic wavelengths were respectively selected by Bootstrapping Soft Shrinkage (BOSS), Variable Iterative Space Shrinkage Approach (VISSA) and Model Adaptive Space Shrinkage (MASS) algorithms. Then, Support Vector Machine (SVM) was applied to establishing the relationship between the characteristic peaks, the full spectra, three characteristic spectra and the labels of tea grades. The results showed that VISSA-SVM model had the best classification performance, but the model precision can still be improved. Thus, Artificial Bee Colony (ABC) algorithm was introduced to optimize the parameters of SVM model. The accuracy and Kappa coefficient of test set of VISSA-ABC-SVM model were improved to 97.436% and 0.962, respectively. Therefore, the combination of fluorescence hyperspectra with VISSA-ABC-SVM model can accurately identify the grade of Tieguanyin tea. PRACTICAL APPLICATION: The rapid and accurate nondestructive tea grade identification method contributes to the construction of the tea online grade detection system. FHSI technology can solve the shortcomings of the reported methods and improved the identification accuracy of tea grades. It can be applied to the rapid detection of tea quality by tea companies, tea market, tea farmers and other demanders.

The suitability of rare earth elements for geographical traceability of tea leaves.

BACKGROUND: Rare earth elements (REEs) have been used for the identification of the geographical origins of an increasing number of foods. This study analyzed the effects of geographical origin, harvest season, variety, and their interactions on REEs in tea leaves to investigate whether REEs were suitable for geographical identification of tea leaves. Tea leaves of different varieties and the corresponding soils were collected in different seasons from different areas of China. The concentrations of 14 REEs in tea leaves and soils were determined, and then analyzed with one-way analysis of variance (ANOVA), multi-way ANOVA, correlation analysis, and linear discriminant analysis. RESULTS: All factors significantly affected the contents of REEs in tea leaves. The concentrations of REEs in tea leaves were related to those in provenance soils. However, the concentrations of most REEs in tea leaves were primarily affected by the harvest season. CONCLUSION: Seasonal variations should be considered when REE fingerprinting is applied for the identification of tea for authentication purposes. © 2019 Society of Chemical Industry.