Identification of the Origin of White Tea Based on Mineral Element Content

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Deep Learning Network of Amomum villosum Quality Classification and Origin Identification Based on X-ray Technology

A machine vision system based on a convolutional neural network (CNN) was proposed to sort Amomum villosum using X-ray non-destructive testing technology in this study. The Amomum villosum fruit network (AFNet) algorithm was developed to identify the internal structure for quality classification and origin identification in this manuscript. This network model is composed of experimental features of Amomum villosum. In this study, we adopted a binary classification method twice consecutive to identify the origin and quality of Amomum villosum. The results show that the accuracy, precision, and specificity of the AFNet for quality classification were 96.33%, 96.27%, and 100.0%, respectively, achieving higher accuracy than traditional CNN under the condition of faster operation speed. In addition, the model can also achieve an accuracy of 90.60% for the identification of places of origin. The accuracy of multi-category classification performed later with the consistent network structure is lower than that of the cascaded CNNs solution. With this intelligent feature recognition model, the internal structure information of Amomum villosum can be determined based on X-ray technology. Its application will play a positive role to improve industrial production efficiency.

Production regions discrimination of Huangguanyin oolong tea by using the content of chemical components and rare earth elements

Oolong tea is one of the most popular tea beverages in China. Tea cultivars, processing technology and origin of production affect the quality and price of oolong teas. To investigate the differences in Huangguanyin oolong tea from different production regions, the chemical components, mineral elements and rare earth elements of Huangguanyin oolong tea produced in Yunxiao (YX) and Wuyishan (WY) were analyzed by using spectrophotometry methods, targeted metabolomics and inductive plasma coupled mass spectrometry (ICP-MS). The results of spectrophotometry methods revealed that there were significant differences in thearubigin, tea polyphenols and water extract between Huangguanyin oolong teas from different production regions. Targeted metabolomics identified a total of 31 chemical components in Huangguanyin oolong teas from the two production regions, of which 14 chemical components were significantly different and contributed to the regional differentiation of Huangguanyin oolong tea. Yunxiao Huangguanyin had relatively higher contents of (-)-Epigallocatechin-3-O-(3-O-methylgallate) (EGCG3″Me), ornithine (Orn) and histidine (His), while Wuyishan Huangguanyin had relatively higher contents of glutamic acid (Glu), γ-aminobutyric acid (GABA), β-aminobutyric acid (β-ABA) and other components. Moreover, ICP-MS identified a total of 15 mineral elements and 15 rare earth elements in Huangguanyin oolong tea from the two production regions, of which 15 elements were significantly different between YX and WY, and contributed to the regional differentiation of Huangguanyin oolong tea. K had a relatively higher content in Yunxiao Huangguanyin, while rare earth elements had relatively higher contents in Wuyishan Huangguanyin. The classification results by the production region showed that the discrimination rate of the support vector machine (SVM) model based on the 14 different chemical components reached 88.89%, while the SVM model based on the 15 elements reached 100%. Therefore, we used targeted metabolomics and ICP-MS techniques to screen and explore the chemical components, mineral elements and rare earth elements differences among two production regions, which indicated the feasibility of Huangguanyin oolong tea classification by production regions in the study. The results will provide some reference for the distinction between the two production regions of Huangguanyin oolong tea.

Use of Microscopic Characteristics and Multielemental Fingerprinting Analysis to Trace Three Different Cultivation Modes of Medicinal and Edible Dendrobium officinale in China

The traceability of different cultivation modes is critical for ensuring the commercial viability of high-value Dendrobium officinale. In this study, by means of polarizing microscopy, SEM-EDX, ICP-MS and ICP-AES, the possibility of combining microscopic characteristics, multielemental analysis and multivariate statistical authenticity analysis was realized to determine the origins of the fresh stem and dried stem powder of D. officinale derived from three different cultivation modes from six provinces of China. The microscopic structure, chemical elements on the surface of the main microstructures and concentrations of Ca, K, Ba, Cs, As and Cu varied among specimens derived from different cultivation modes. The fresh stems of D. officinale derived from different cultivation modes can be effectively and quickly identified by various microscopic characteristics and different contents of Ca on the surface of the parenchyma, phloem and xylem. Meanwhile, linear discriminant analysis showed that 98.1% of the dried stem powder samples were correctly classified, and the accuracy of cross-validation was 95.3%. This study facilitated an effective integrated method for determining the traceability of the fresh stem and dried stem powder of D. officinale derived from three different cultivation modes. This approach offers a potential method for identifying the origins of medicinal plants derived from different cultivation modes.

A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms

Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.

Identification of the age of white tea using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) coupled with multivariate analysis

RATIONALE

In recent years, white tea has become increasingly popular. Some merchants confuse the age of white tea and sell poor-quality products for profit. Therefore, it is necessary to provide technical support for product authentication and valorization in white tea of different marked ages.

METHODS

Volatile organic compounds (VOCs) were detected by proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) and identified as volatile fingerprints. PTR-TOF-MS combined with multivariate analysis was found to identify white tea of four different marked ages (1, 3, 5, and 8 years) for authentication. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used as classification models to identify key volatile metabolites.

RESULTS

The OPLS-DA model achieved the best results (96.67%, 96.67%, 96.67%, and 96.67% in the training set and 96.00%, 96.00%, 100%, and 100% in the prediction set for 1-year, 3-year, 5-year, and 8-year tea samples, respectively), showing that PTR-TOF-MS with the OPLS-DA model could successfully be used in the identification of white tea with different marked ages. Out of the 60 identified VOCs, 26 volatile materials were closely correlated with tea age and were used as markers to discriminate white tea of different ages.

CONCLUSIONS

PTR-TOF-MS coupled with multivariate analysis could be applied for quality evaluation of tea products of different ages and provided a feasible technical support for product authentication and valorization in white tea of different marked ages.

The Role of Soil Mineral Multi-elements in Improving the Geographical Origin Discrimination of Tea (Camellia sinensis)

The combination of mineral multi-elements with chemometrics can effectively trace the geographical origin of tea (Camellia sinensis). However, the role of soil mineral multi-elements in discriminating the origin of tea was unknown. This study aimed to further validate whether the geographical origin of tea can be authenticated based on mineral multi-elements, the concentrations of which in tea leaves were significantly correlated with those in soil. Eighty-seven tea leaves samples and paired soils from Meitan and Fenggang (MTFG), Anshun, and Leishan in China were sampled, and 24 mineral elements were measured. The data were processed using one-way analysis of variance (ANOVA), Pearson correlation analysis, principal component analysis (PCA), and stepwise linear discriminant analysis (SLDA). Results indicated that tea and soil samples from different origins differed significantly (p < 0.05) in terms of most mineral multi-elemental concentrations. Conversely, the intra-regional differences of different cultivars of the same origin were relatively minor. Seventeen mineral elements in tea leaves were significantly correlated with those in soils. The SLDA model, based on the 17 aforementioned elements, produced a 98.85% accurate classification rate. In addition, the origin was also identified satisfactorily with 94.25% accuracy when considering the cultivar effect. In conclusion, the tea plant cultivars unaffected the accuracy of the discrimination rate. The geographical origin of tea could be authenticated based on the mineral multi-elements with significant correlation between tea leaves and soils. Soil mineral multi-elements played an important role in identifying the geographical origin of tea.

Investigations of metallic elements and phenolics in Chinese medicinal plants

Traditional Chinese Medicines (TCM) can be contaminated with heavy metals, and therefore, the aim of this study is to analyze the Fe, Mn, Zn, Cu, Cd, Pb, Cr, and phenolic compounds contents in TCM plants used against civilization diseases. Metals were determined by flame atomic absorption spectroscopy (FAAS) for Fe, Mn, Zn, and Cu and inductively coupled plasma-optical emission spectroscopy (ICP-OES) for Pb, Cd, and Cr. The total phenolic, flavonoid, and phenolic acid contents were determined by HPLC and UV/vis spectrometry. The contents of the studied elements were highest in Radix Rehmanniae, whereas lowest in Fructus Lycii and Fructus Crataegi. The studied metals were assayed in the decreasing order: Fe, Zn, Mn, Cu, Cr, Pb, and Cd. Radix Rehmanniae Glutinosae Preparata showed the lowest phenolic composition, while Fructus Lycii showed the richest content. Principal component analysis (PCA) revealed that the contents of ferulic acid, caffeic acid, rutin, and Cu, Cr, and Cd were among the most important factors responsible for the differentiation between the investigated medicinal plants. Cluster analysis (CA) showed that the TCM samples originating from the same botanical plant species were often found in the same cluster, which confirms the similar level of studied elements determined within the samples.

Assessing Polyphenol Components and Antioxidant Activity during Fermented Assam Tea Ball Processing

Fermented tea is traditionally consumed in many Asian countries. In Thailand, the product is made by anaerobic submerged fermentation of semi-mature tea leaves before being made into a ball form. This study aims to investigate the composition of health-associated bioactive compounds in fermented tea balls made from Camellia sinensis var. assamica, which is naturally grown in the forests of northern Thailand. The processing involves steaming semi-mature tea leaves followed by anaerobic fermentation in 2% NaCl solution (1:5 w/v of tea leaves solution). Levels of catechin (C), epicatechin (EC), epicatechin gallate (ECG), epigallocatechin gallate (EGCG), gallocatechin (GC), flavonols (myricetin, quercetin, and kaempferol), phenolic acids (caffeic acid, chlorogenic acid, coumaric acid, and sinapic acid), total phenolic content, and in vitro antioxidant activity were evaluated in fresh tea leaves, steamed tea leaves, and fermented tea leaves over a period of 60 days’ monitoring. The results indicated that fermented tea balls still contain significant amounts of tea polyphenols, although their processing may result in some loss of most bioactive compounds. The antioxidant activity measured by Ferric Reducing Antioxidant Power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and Oxygen Radical Absorbance Capacity (ORAC) assays also declined as the fermentation time was extended. However, phenolic acids, including caffeic acid and sinapic acid, contrastingly increased during prolonged fermentation by 74.35% and 171.43% from fresh leaves, respectively.

Monitoring the authenticity of pu'er tea via chemometric analysis of multielements and stable isotopes

Mineral elements and stable isotopes combined with stoichiometric methods were used as a potential tool for first authenticating Chinese tea according to it's production year. A total of 86 mineral elements and stable isotope compositions were determined from the Xiangzhujing Pu'er tea in five different production years using ICP-MS and ICP-OES. On the basis of 78 statistically significant mineral elements and stable isotopes, HCA, PCA, PLS-DA, BP-ANN, and LDA were employed to build authentication models for predicting the Pu'er tea with different production years. The clustering results of the HCA and PCA were worse than that of PLS-DA, BP-ANN, and LDA. The PLS-DA model displayed a perfect model performance (R²X = 0.86, R²Y = 0.974, and Q² = 0.922). The authentication performance of LDA and BP-ANN revealed their 100% recognition sensitivity and prediction ability and was thus better than that of PLS-DA. Mn, ⁶⁸Zn, and ²⁰³Tl were the markers for enabling the successful authentication of Pu'er tea with different production years. This study contributes toward generalizing the use of mineral element and stable isotope fingerprinting combined with LDA and BP-ANN as a promising tool for authentication of tea worldwide.

Improved geographical origin discrimination for tea using ICP-MS and ICP-OES techniques in combination with chemometric approach

BACKGROUND

There is an urgent need to strengthen the testing and certification of geographically iconic foods, as well as to use discriminatory science and technology for their regulation and verification. Multi-element and stable isotope analyses were combined to provide a new chemometric approach for improving the discrimination tea samples from different geographical origins. Different stoichiometric methods [principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), back propagation based artificial neural network (BP-ANN) and linear discriminant analysis (LDA)] were used to demonstrate this discrimination approach using Yongchuanxiuya tea samples in an experimental test.

RESULTS

Multi-element and stable isotope analyses of tea samples using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry easily distinguished the geographical origins. However, the clustering ability of the two unsupervised learning methods (PCA and HCA) were worse compared to that of the three supervised learning methods (PLS-DA, BP-ANN and LDA). BP-ANN and LDA, with 100% recognition and prediction abilities, were found to be better than PLS-DA. ⁸⁶ Sr and ¹¹² Cd were the markers enabling the successful classification of tea samples according to their geographical origins. Under the validation by 'blind' dataset, the prediction accuracies of the BP-ANN and LDA methods were all greater than 90%. The LDA method showed the best performance, with an accuracy of 100%.

CONCLUSION

In summary, determination of mineral elements and stable isotopes using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry techniques coupled with chemometric methods, especially the LDA method, is a good approach for improving the authentication of a diverse range of tea. The present study contributes toward generalizing the use of fingerprinting mineral elements and stable isotopes as a promising tool for testing the geographic roots of tea and food worldwide. © 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 and comparison of oligopeptides during withering process of White tea by ultra-high pressure liquid chromatography coupled with quadrupole-orbitrap ultra-high resolution mass spectrometry

Peptides could have specific tastes or bioactivities depending on the length and sequence of amino acids. Till date it remains unknown what peptides are formed during the white tea manufacturing process and whether they contribute to the flavor or bio-activities of white tea. As a first step to address these questions, we applied ultra-high pressure liquid chromatography coupled with quadrupole-orbitrap ultra-high resolution mass spectrometry (UPLC-Quadrupole-Orbitrap-UHRMS) to monitor peptides dynamic changes during the withering process. A total of 196 abundant peptides were identified. Most of them were oligopeptides within a molecular weight of 1000 Da. Four of them were randomly selected, synthesized peptides were applied for further confirmation and quantification. Sequence analysis suggested that some of them were potential taste contributors. Proteinase cleave site analysis identified two separate periods of active proteins degradation at 0-12 h and 30-42 h of the withering processes. Further analysis of cleavage sites also suggested that protein degradation during withering steps were random rather than a stepwise reaction.

Chemometric Analysis of Elemental Fingerprints for GE Authentication of Multiple Geographical Origins

The feasibility of combining elemental fingerprints and chemical pattern recognition methods for authentication of the geographical origins of a Chinese herb, Gastrodia elata BI. (GE), was studied in this paper. A total of 210 GE samples were collected from 7 different producing areas. The levels of 15 mineral elements in GE, including Zn, Cd, Co, Cr, Cu, Ca, Mg, Mn, Mo, Ni, Pb, Sr, Fe, Na, and K, were determined using inductively coupled plasma mass spectrometry (ICP-MS). Using the autoscaled data of elemental fingerprints and partial least-squares discriminant analysis (PLSDA), two chemometrics strategies for multiclass classifications, One-Versus-Rest (OVR) and One-Versus-One (OVO), were studied and compared in discrimination of GE geographical origins. As a result, OVR-PLSDA and OVO-PLSDA could achieve the classification accuracy of 0.672 and 0.925, respectively. The results indicate that mineral elemental fingerprints coupled with chemometrics can provide a useful alternative method for simultaneous discrimination of multiple GE geographical origins.

Multielemental Analysis Associated with Chemometric Techniques for Geographical Origin Discrimination of Tea Leaves (Camelia sinensis) in Guizhou Province, SW China

This study aimed to construct objective and accurate geographical discriminant models for tea leaves based on multielement concentrations in combination with chemometrics tools. Forty mineral elements in 87 tea samples from three growing regions in Guizhou Province (China), namely Meitan and Fenggang (MTFG), Anshun (AS) and Leishan (LS) were analyzed. Chemometrics evaluations were conducted using a one-way analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that the concentrations of the 28 elements were significantly different among the three regions (p < 0.05). The correct classification rates for the 87 tea samples were 98.9% for LDA and 100% for OPLS-DA. The variable importance in the projection (VIP) values ranged between 1.01–1.73 for 11 elements (Sb, Pb, K, As, S, Bi, U, P, Ca, Na, and Cr), which can be used as important indicators for geographical origin identification of tea samples. In conclusion, multielement analysis coupled with chemometrics can be useful for geographical origin identification of tea leaves.