Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review

Non-destructive authentication of Cachaças from Brejo Paraibano based on MIR spectroscopy

Cachaça, an alcoholic beverage derived from the fermentation of sugarcane juice, is a quintessential Brazilian product, with the Brejo Paraibano region standing out as one of the leading producers of high-quality cachaças. Therefore, geographic authentication of these beverages is essential to guarantee their quality and prevent fraud. This study employed mid-infrared spectroscopy (MIR) combined with chemometric models, including One-Class Partial Least Squares (OC-PLS) and Data-Driven Soft Independent Modeling by Class Analogy (DD-SIMCA), to authenticate the geographic origin of cachaças from Brejo Paraibano, as opposed to cachaças originating from other Brazilian regions. The DD-SIMCA model, incorporating spectral preprocessing with baseline shift correction and Savitzky-Golay smoothing using 21 points, achieved 95.6 % sensitivity during training, 100 % in testing, and 100 % specificity, with an overall classification efficiency of 98.4 %. These findings underscore the effectiveness of the proposed approach as a robust, green, and reliable tool to verify the authenticity and geographic origin of cachaças.

Determination and comparison of lipid profiles of Chinese green tea varieties using untargeted lipidomics analysis combined with chemometrics

Tea is the most widely consumed drink in the world. Lipids as important chemical components are closely related to the flavor and quality of tea. However, the information about the lipidomic fingerprints of different Chinese green tea varieties was limited. Therefore, an untargeted lipidomics analysis combined with chemometrics was applied for discriminating Chinese green tea varieties. A total of 147 molecular species belonging to 16 lipid classes were identified. And the green tea samples exhibited high levels of phospholipids, glycolipids, linoleic acid, and linolenic acid. They play important roles in metabolic processes that could influence flavor formation. Furthermore, combined with multivariate data analysis, 8 molecular species were screened out for discriminating green tea varieties, such as PC 16:0/18:3, MGDG 18:3/18:3, PC 18:2/18:2, etc. This study offers a reference for the guarantee of quality and the prevention of fraud within the Chinese green tea market.

Differentiation of black tea according to country of origin using the μ-CTE/TD/GC-MS method combined with decision tree-optimizable neural network analysis

BACKGROUND

Accurate discrimination of the country of origin of teas is critical to determine their actual commercial value, to meet consumer preferences, and to ensure compliance with labeling regulations. Therefore, in this study, we developed a new approach to accurately discriminate the country of origin of teas in the Turkish market.

RESULTS

A thermal desorption/gas chromatographic-mass spectrometric (TD/GC-MS) method combined with optimizable neural networks (ONN) was developed to analyze the volatile organic compounds (VOCs) of tea samples subjected to infusion or grinding pretreatments. Prior to GC-MS analysis, the conventional thermal desorption method was applied to VOCs in the powdered teas, while VOCs in the infused teas were adsorbed on Tenax-TA sorbent tubes attached to a micro-chamber/thermal extractor (μ-CTE) and then thermally desorbed. Using a feature selection technique, a total of 11 VOCs from infused tea samples, 21 VOCs from ground tea samples, and 18 VOCs from both groups were identified as specific VOCs that critically affect the classification of the teas. As a result of ONN classification of selected VOCs from only ground tea samples and infused tea samples, 95.51% and 96.7% accuracy was obtained, respectively, while 100% classification accuracy was achieved by ONN classification of VOCs from both sample groups.

CONCLUSION

The results showed that different pretreatments applied to Turkish and Ceylon teas caused the release of different volatile compounds, resulting in more specific VOC profiles. In addition, the developed μ-CTE/TD/GC-MS method allowed a more accurate classification of the black tea samples than the TD/GC-MS system alone. © 2025 Society of Chemical Industry.

Identifying key contributors to the sweet aftertaste of raw Pu-erh tea through analytical and sensory methods

In this study, the key components contributing to raw Pu-erh tea (RAPT) sweet aftertaste were identified. Six RAPTs were investigated through sensory evaluation, mass spectrometry, and taste addition experiments, and 96 taste components of tea infusion were annotated and analyzed. Saliva analysis after drinking tea revealed that 27 components present in tea remained in the mouth. On the basis of the results of the multivariate statistical analyses, we hypothesized that alkaloids and flavonoids might influence the sweet aftertaste strength of RAPT. Finally, the results of the taste addition experiments revealed that theophylline and rutin are key components that significantly influence the sweet aftertaste intensity of the RAPT. This strategy can be used as a methodology for analyzing the taste of tea, and the results can provide an evaluation index for evaluating the quality of RAPT.

Machine learning-guided Orbitrap-HRAMS-based metabolomic fingerprinting for geographical origin, variety and tissue specific authentication, and adulteration detection of turmeric and ashwagandha

The increasing global demand for herbs and spices in food and nutraceutical industries highlights their key functional benefits, including antioxidant and anti-inflammatory properties. Ensuring authenticity and traceability is essential to counteract challenges such as geographical origin (GO) mislabelling and tissue- or variety-specific adulteration, which can undermine product quality and safety. This study employs LC-Orbitrap-MS-based untargeted metabolomics coupled with machine learning to authenticate the GO, variety, and tissue specificity of turmeric (Curcuma longa) and ashwagandha (Withania somnifera), two widely used food ingredients. Four GO-specific turmeric samples, three tissue- and variety- specific ashwagandha samples, and adulterated market samples were analysed using data-dependent acquisition mode. Machine learning algorithms identified key biomarkers and constructed robust classification models, achieving 98 % specificity and accuracy in authenticating GO, variety, and tissue specificity, even in adulterated samples. These results demonstrate the value of integrating advanced metabolomics and machine learning for quality assurance and food safety in the global market.

A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions

With the steady rise in tea production, the need for effective tea quality monitoring has become increasingly pressing. Traditional sensory evaluation and wet chemical detection methods are insufficient for real-time tea quality monitoring. As an emerging technology, near infrared spectroscopy (NIRS) offers numerous advantages, such as preserving sample integrity, generating objective results, and enabling rapid, straightforward assessments. These features make it an ideal choice for real-time tea quality testing. This paper systematically reviews the principles of NIRS, spectral preprocessing methods, statistical modeling techniques, and commonly used machine learning approaches. Furthermore, it provides an in-depth discussion of the research progress of NIRS in areas such as fresh tea leaf quality evaluation, rapid detection of tea-specific components, tea quality assessment and species identification, geographic traceability, development of NIRS equipment, and standardization. Future research directions in the tea field are also proposed. This review serves as a valuable resource for researchers aiming to understand the application and development of NIRS technology in the tea field. It offers insights to facilitate real-time tea quality monitoring and ultimately achieve intelligent quality control.

Tracing the geographical origin of Chinese green tea based on fluorescent sensor array combined with multi-way chemometrics analyses

Fluorescent sensor arrays are becoming a hot topic in many fields because they can simultaneously detect multiple targets in complex research systems. However, most researches have only collected two-dimensional fluorescence spectral data from fluorescent sensor array interacting with target analytes. In contrast, three-dimensional fluorescence spectra can provide richer information than two-dimensional fluorescence spectra. Based on the hypothesis that collecting three-dimensional fluorescence spectra can obtain more abundant information of green tea samples from different regions, which can improve the accuracy and reliability of origin identification. This study aimed to explore the feasibility of using three-dimensional fluorescent sensor array combined with multi-way pattern recognition methods for the origin discrimination of green tea based on the differences in the contents and types of metal ions in green tea. To investigate this, we first designed a fluorescent sensor array based on amino acid-derived carbon dots and examined its ability to recognize common metal ions in green tea. Excitation-emission matrix spectra of green tea extracts from different geographical origins after interaction with the fluorescent sensor array were collected. Several multi-way pattern recognition methods were used to analyze the three-dimensional fluorescent array data of 100 green tea samples from five origins. The overall classification results of green tea from the five geographical origins were satisfactory, with the best prediction accuracy reaching 96.88%. In comparison, multilinear partial least squares discriminant analysis could make full use of the information of three-dimensional fluorescence data. And its correct identification results for green tea were superior to those of unfold partial least squares discriminant analysis. These results sufficiently demonstrated that the fluorescent sensor array integrated with multi-way pattern recognition, has promising potential for tracing the origin of Chinese green tea.

Authenticating vintage in white tea: Appearance-taste-aroma-based three-in-one non-invasive anticipation

To safeguard the legal rights of tea enterprises and promote sustainable development in the tea industry, this study proposes a rapid, non-destructive method for authenticating white tea vintages based on the hypothesis that the appearance, taste and aroma cannot be simultaneously replicated in counterfeit teas. Using visible-near infrared hyperspectral imaging, this three-in-one appearance-taste-aroma method was applied to Bai Mudan white tea, produced from the Jinggu Dabai Tea cultivar harvested in 2020, 2021 and 2022. Hyperspectral imaging captured appearance data from dry samples of different vintages, with preprocessing using multiplicative scatter correction (MSC) and standard normal variate (SNV). Partial least squares regression (PLSR) and support vector regression (SVR) models were used to explore correlations between appearance data, electronic tongue-measured taste and electronic nose-measured aroma. The results showed that appearance data can predict tea infusion taste (0.6540 < Rp < 0.8873) and aroma (0.8880 < Rp < 0.9703) across vintages. Further integration of high-performance liquid chromatography (HPLC), high-performance liquid chromatography (GC-IMS) and regression models revealed that appearance-based spectral data predict taste through gallic acid (GA), catechin (C) and gallocatechin gallate (GCG), and predict aroma via styrene, 2,5-dimethylpyrazine and 2-octanone. This non-invasive method, leveraging visible-near infrared spectroscopy, provides a standardized approach for white tea vintage authentication by integrating appearance, taste and aroma assessments.

Unveiling the chemical composition of unique flavor profiles in raw Pu-erh tea from different Tea-Producing Mountains

The Chinese proverb "One mountain, one flavor" reflects that raw pu-erh tea (RPT) from different tea-producing mountains (TPMs) possesses distinct flavor profiles. However, limited research has been conducted on the chemical composition underlying distinct flavor profiles. In this study, sensory evaluation and main phytochemical compositions revealed diverse aromas of RPTs from 26 TPMs. A total of 225 volatile compounds were analyzed qualitatively and quantitatively by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry, including hydrocarbon compounds, ketones, alcohols, and aldehydes. Furthermore, twenty-one common key odor-active compounds, potentially influencing the regional flavor, were identified by gas chromatography-olfactometry and odor activity value. Multivariate statistical analysis showed that geographical factors-longitudes, latitudes, counties, and elevations-account for 37.02 % of the aroma profile variance, implying the possible influence of other potential factors. The study results serve as a reference for a better understanding of the correlation between the RPT flavor characteristics and geographical attributes in TPMs.

Comparison of Untargeted and Markers Analysis of Volatile Organic Compounds with SIFT-MS and SPME-GC-MS to Assess Tea Traceability

Tea is one of the most consumed beverages in the world and presents a great aromatic diversity depending on the origin of the production and the transformation process. Volatile organic compounds (VOCs) greatly contribute to the sensory perception of tea and are excellent markers for traceability and quality. In this work, we analyzed the volatile organic compounds (VOCs) emitted by twenty-six perfectly traced samples of tea with two analytical techniques and two data treatment strategies. First, we performed headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) as the most widely used reference method for sanitary and quality controls of food. Next, we analyzed the samples with selected-ion flow-tube mass spectrometry (SIFT-MS), an emerging method for direct analysis of food products and aroma. We compared the performances of both techniques to trace the origin and the transformation processes. We selected the forty-eight most relevant markers with HS-SPME-GC-MS and evaluated their concentrations with a flame ionization detector (FID) on the same instrument. This set of markers permitted separation of the origins of samples but did not allow the samples to be differentiated based on the color. The same set of markers was measured with SIFT-MS instrument without success for either origin separation or color differentiation. Finally, a post-processing treatment of raw data signals with an untargeted approach was applied to the GC-MS and SIFT-MS dataset. This strategy allowed a good discrimination of origin and color with both instruments. Advantages and drawbacks of volatile profiles with both instruments were discussed for the traceability and quality assessment of food.

HPLC-DAD method for simultaneous determination of gallic acid, catechins, and methylxanthines and its application in quantitative analysis and origin identification of green tea

The high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD) was optimized for the simultaneous determination of 11 compounds, belonging to polyphenols (gallic acid and seven catechins) and methylxanthines (caffeine, theobromine, and theophylline). The results obtained for all the validation parameters of the HPLC-DAD method showed that the method is sensitive enough for routine analysis with basic chromatographic equipment, thus it has a significant potential to be highly applicable in common laboratory practice. The method was used in the analysis of 60 green tea infusions originating from four tea-producing countries. The dataset contributes to enhancing current data on green tea. The analysis of green tea extracts revealed significant differences depending on the origin of the samples. Linear Discriminant Analysis (LDA) was applied to test the accuracy of identification of the origin of the tea samples, based on the chemical composition of tea with a focus on polyphenolic compounds and methylxanthines analysed in this study. Based on cross-validation results, the model showed 93.75 % accuracy in the classification of green tea originating from Japan, China (Mainland), China (Taiwan) and South Korea.

1H NMR Spectroscopy Combined with Machine-Learning Algorithm for Origin Recognition of Chinese Famous Green Tea Longjing Tea

Premium green tea is a high-value agricultural product significantly influenced by its geographical origin, making it susceptible to food fraud. This study utilized nuclear magnetic resonance (NMR) spectroscopy to perform chemical fingerprint analysis on 78 Longjing tea (LJT) samples from both protected designation of origin (PDO) regions (Zhejiang) and non-PDO regions (Sichuan, Guangxi, and Guizhou) in China. Unsupervised algorithms and heatmaps were employed for the visual analysis of the data from PDO and non-PDO teas while exploring the feasibility of linear and nonlinear machine-learning algorithms in discriminating the origin of LJT. The findings revealed that the nonlinear model random forest (92.2%), exhibited superior performance compared to the linear model linear discriminant analysis (85.6%). The random forest model identified 15 key marker metabolites for the geographical origin of LJT, such as kaempferol glycoside, glutamine, and ECG. The results support the conclusion that the integration of NMR with machine-learning classification serves as an effective tool for the quality assessment and origin identification of LJT.

A colorimetric sensor for rapid discrimination of tea polyphenols and tea authentication based on Rh-decorated Pd nanocubes with high peroxidase-like activity

The rapid development of nanozymes has offered substantial opportunities for the fields of biomedicine, chemical sensing, and food safety. Among these applications, multichannel sensors, with the capability of simultaneously detecting multiple target analytes, hold promise for the practical application of nanozymes in chemical sensing with high detection efficiency. In this study, Rh-decorated Pd nanocubes (Pd-Rh nanocubes) with significantly enhanced peroxidase-like activity are synthesized through the mediation of underpotential deposition (UPD) and subsequently employed to develop a multichannel colorimetric sensor for discriminating tea polyphenols (TPs) and tea authentication. Based on a single reactive unit of efficient catalytic oxidation of 3,3',5,5'-tetramethylbenzidine dihydrochloride (TMB), the nanozyme-based multichannel colorimetric sensor responds to each analyte in as short as 1 min. With the aid of principal component analysis (PCA) and hierarchical cluster analysis (HCA), various TPs and types of tea can be accurately identified. This work not only provides a new type of simply structured and highly active nanozymes but also develops a concise and rapid multichannel sensor for practical application in tea authentication and quality inspection.

Recent advances in the authentication (geographical origins, varieties and aging time) of tangerine peel (Citri reticulatae pericarpium): A review

The consumption of tangerine peel (Citri reticulatae pericarpium, CRP) has been steadily increasing worldwide due to its proven health benefits and sensory characteristics. However, the price of CRP varies widely based on its origin, variety, and aging time, which has led many manufacturers to offer inferior products by exploiting the sensory similarity of CRP, seriously undermining consumers' interests. Therefore, it is essential to identify the authenticity of the CRP. In this study, the research progress on the authenticity of CRP from different origins, years and varieties over the past 10 years and the application and prospects of the main technologies and techniques were reviewed. The advantages and disadvantages of the commonly used methods were also summarized and compared. Mass spectrometry-based and spectroscopy-based techniques are the most commonly used methods for analyzing CRP authenticity. However, designing fast, non-destructive and green methods for identifying CRP authenticity would be the future trend.

Effect of in-situ biochemical modification on the synthesis, structure, and function of xanthan gum based bacterial cellulose generated from Tieguanyin oolong tea residue hydrolysate

The effect of in-situ biochemical modification on the synthesis, structure, and function of xanthan gum based bacterial cellulose generated from Tieguanyin oolong tea residue hydrolysate was evaluated for the first time. This modification could overcome the inhibitory effect of the hydrolysate and the bacterial cellulose yield with 0.6% xanthan gum addition increased by 260.8% compared with that without xanthan gum addition. Bacterial cellulose and xanthan gum were combined by the in-situ modification and the alteration of fermentation medium rheological properties by xanthan gum addition might be beneficial for their combination. The average diameter of the bacterial cellulose microfibrils was increased by the modification, and it had a great influence on the crystalline structure of the bacterial cellulose. Additionally, both the water absorption and texture properties of the bacterial cellulose was strengthened by the modification. Overall, this modification showed great potential for efficient and effective xanthan gum based bacterial cellulose production.

Determination of C═C Positions of Unsaturated Fatty Acids in Foods via Ambient Reactive Desorption Ionization with Water Dimer Radical Cations

The positions of C═C bonds in unsaturated fatty acids (FAs) are one of the main factors determining the quality of food flavor. Herein, we developed an approach for the determination of C═C bonds of FAs by online epoxidation reaction with water dimer radical cations. The limit of detection for octenoic acid isomers was ∼9 μg/L. The positions of C═C bonds in trans-2/3-hexenoic acid, trans-2/3-octenoic acid, oleic acid, linoleic acid, and linolenic acid in black tea or olive oil samples were directly determined by the established method. These results indicate that the established method allows the rapid determination of unsaturated FAs in black tea and olive oil. The advantages of this approach include the analysis speed (∼1 min per sample), simple device, and no need for complex pretreatment. This study not only provides a strategy for the determination of C═C positions but also offers new possibilities for applications in the field of food chemistry.

Development of sequential online extraction electrospray ionization mass spectrometry for accurate authentication of highly-similar Atractylodis Macrocephalae

The accurate and rapid authentication techniques and strategies for highly-similar foods are still lacking. Herein, a novel sequential online extraction electrospray ionization mass spectrometry (S-oEESI-MS) was developed to achieve spatio-temporally resolved ionization and comprehensive characterization of complex foods with multi-components (high, medium, and low polarity substances). Meanwhile, a characteristic marker screening method and an integrated research strategy based on MS fingerprinting, characteristic marker and chemometrics modeling were established, which are especially suitable for the accurate and rapid authentication of highly-similar foods that are difficult to be authenticated by traditional techniques (e.g., LC-MS). Thirty-two batches of highly-similar Atractylodis macrocephalae rhizome from four different origins were used as model samples. As a result, S-oEESI-MS enabled a more comprehensive MS characterization of substance profiles in complex plant samples in 1.0 min. Further, 22 characteristic markers of Atractylodis macrocephalae were ingeniously screened out and combined with multivariate statistical analysis model, the accurate authentication of highly-similar Atractylodis macrocephalae was realized. This study presents a comprehensive strategy for accurate authentication and origin analysis of highly-similar foods, which has potentially significant applications for ensuring food quality and safety.

Rapid identification of the geographical origin of Baimudan tea using a Multi-AdaBoost model integrated with Raman Spectroscopy

The potential of Multi-AdaBoost in spectral analysis is substantial, particularly when combined with weak classifiers and trained to develop into a robust classifier. Given the variable quality of Baimudan tea sourced from diverse regions, the novel application of Raman spectroscopy in conjunction with the Multi-AdaBoost model to analyze the geographic origin of Baimudan tea was introduced. Initially, Raman spectra of Baimudan tea from four distinct origins in Fujian province were gathered, namely Fuan (FA), Fuding (FD), Zhenghe (ZH), and Songxi (SX). Decision Tree (DT) and Support Vector Machine (SVM) models were employed as fitting classifiers to construct the Multi-AdaBoost-DT and Multi-AdaBoost-SVM models. The results demonstrated that the Multi-AdaBoost-DT model exhibited significantly improved recognition rates for FA, FD, ZH, and SX origin compared to the DT model, with the average recognition rate increasing from 86.46% to 91.67%. In contrast, the recognition rates for FA and SX origin in the Multi-AdaBoost-SVM model remained unchanged, attributed to the model having reached a local optimum. The recognition rates of FD origin increased from 91.67% to 95.83%, a significant improvement, while those of ZH origin escalated from 83.33% to 87.50%. The average recognition rate increased from 92.71% to 94.79%. Additionally, Multi-AdaBoost-SVM and Multi-AdaBoost-DT enhanced the sensitivity and specificity of the discrimination outcomes. These results corroborated the effectiveness of the proposed Multi-AdaBoost-SVM model in identifying the geographical origin of Baimudan tea. Moreover, the Multi-AdaBoost model demonstrates potential in elevating the discrimination accuracy of weak classifiers, which bodes well for its application in food authentication and quality control.

Varietal Authenticity Assessment of QTMJ Tea Using Non-Targeted Metabolomics and Multi-Elemental Analysis with Chemometrics

In this paper, a combination of non-targeted metabolomics and multi-element analysis was used to investigate the impact of five different cultivars on the sensory quality of QTMJ tea and identify candidate markers for varietal authenticity assessment. With chemometric analysis, a total of 54 differential metabolites were screened, with the abundances significantly varied in the tea cultivars. By contrast, the QTMJ tea from the Yaoshan Xiulv (XL) monovariety presents a much better sensory quality as result of the relatively more abundant anthocyanin glycosides and the lower levels of 2'-o-methyladenosine, denudatine, kynurenic acid and L-pipecolic acid. In addition, multi-elemental analysis found 14 significantly differential elements among the cultivars (VIP > 1 and p < 0.05). The differences and correlations of metabolites and elemental signatures of QTMJ tea between five cultivars were discussed using a Pearson correlation analysis. Element characteristics can be used as the best discriminant index for different cultivars of QTMJT, with a predictive accuracy of 100%.

Application of GC-IMS coupled with chemometric analysis for the classification and authentication of geographical indication agricultural products and food

Geographical indications (GI) are used to protect the brand value of agricultural products, foodstuffs, and wine and promote the sustainable development of the agricultural and food industries. Despite the necessity for the traceability and recognition of GI product characteristics, no rapid, non-destructive approaches currently exist to identify, classify, and predict these properties. The application of gas chromatography-ion mobility spectrometry (GC-IMS) has increased exponentially due to instrument robustness and simplicity. This paper provided a detailed overview of recent GC-IMS applications in China for the quality evaluation of GI products and food, including agricultural products, as well as traditional Chinese food and liquor. The general workflow of GC-IMS coupled with chemometric analysis is presented, including sample collection, model construction and interpretation, and data acquisition, processing, and fusion. Several conclusions are drawn to increase partial least squares-discriminant analysis (PLS-DA) model precision, a chemometric technique frequently combined with GC-IMS.

Cationic Conjugated Polymer Fluorescence Resonance Energy Transfer for DNA Methylation Assessment to Discriminate the Geographical Origins of Lonicerae japonicae flos

The flavor and taste of Lonicerae japonicae flos (LJF) products are heavily influenced by geographical origin. Tracing the geographical origin is an important aspect of LJF quality assessment. Here, DNA methylation analysis coupled with chemometrics revealed that, in 10 CpG islands upstream of genes in the chlorogenic acid and iridoid biosynthetic pathways, DNA methylation differences appear close association with LJF geographical origin. DNA methylation status in these CpG islands was determined using the cationic conjugated polymer fluorescence resonance energy transfer method. As a result, LJFs from 39 geographical origins were classified into four groups corresponding to Northern China, Central Plain of China, Southeast China, and Western China, according to cluster analysis and principal component analysis. Our findings contribute to an understanding of the modulation of LJF taste and can assist in understanding how DNA methylation in LJF varies with geographical origin.

In-depth potential mechanism of combined demulsification pretreatments (isopropanol ultrasonic pretreatments and Ca2+ flow additions) during aqueous enzymatic extractions of Camellia oils

Emulsification is the practical limitation of aqueous enzymatic extractions of Camellia oils. This study aimed to investigate the influence and demulsification mechanisms of isopropanol ultrasonic pretreatments and Ca²⁺ additions on aqueous enzymatic extractions of Camellia oils. Combining isopropanol ultrasonic pretreatments with Ca²⁺ flow additions obtained the highest free oil recovery (78.03 %) and lowest emulsion content (1.5 %). Results indicated that the superior demulsification performance originated from the decrease in emulsion stabilities and formations. First, demulsification pretreatments reduced the oil (14.69 %) and solid (13.21 %) fractions in emulsions to decrease the stability of as-formed emulsions. Meanwhile, isopropanol ultrasonic pretreatments extracted tea saponins (0.38 mg/mL) and polysaccharides (0.23 mg/mL), while Ca²⁺ combined with protein isolates (5.82 mg/mL), tea saponins (7.48 mg/mL) and polysaccharides (0.78 mg/mL) to form precipitates and reduce emulsion formation. This work could promote the practical application of aqueous enzymatic extractions of Camellia oils and enlighten the rise of advanced demulsification pretreatments.

Tracing the origin of Taiping Houkui green tea using 1H NMR and HS-SPME-GC-MS chemical fingerprints, data fusion and chemometrics

The narrow geographical traceability of green tea is both important and challenging. This study aimed to establish multi-technology metabolomic and chemometric approaches to finely discriminate the geographic origins of green teas. Taiping Houkui green tea samples were analyzed by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and ¹H NMR of polar (D₂O) and non-polar (CDCl₃). Common dimension, low-level and mid-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of samples from different origins. In assessments of tea from six origins, the single instrument data test set results in 40.00% to 80.00% accuracy. Data fusion improved single-instrument performance classification with mid-level data fusion to obtain 93.33% accuracy in the test set. These results provide comprehensive metabolomic insights into the origin of TPHK fingerprinting and open up new metabolomic approaches for quality control in the tea industry.

Authenticating teas using multielement signatures, strontium isotope ratios, and volatile compound profiling

High value food products are subject to adulterations and frauds. This study aimed to combine, in our knowledge for the first time, inorganic chemical tracers (multi-elements and Sr isotopy) with volatile organic compound (VOCs) to discriminate the geographic origin, the varieties and transformation processes to authenticate 26 tea samples. By measuring Sr isotope ratio using the multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), 6 out of 11 regions were successfully discriminated. The combination with the ICP-MS inorganic pattern allowed to discriminate 4 more regions with a significance level of 0.05. VOCs fingerprints, obtained with selected ion flow tube mass spectrometer (SIFT-MS), were not correlated with origin but with the cultivar and transformation processes. Green, oolong, and dark teas were clearly differentiated, with hexanal and hexanol contributing to the discrimination of oxidation levels. With this multi-instrumental approach, it is possible to certify the geographical origin and the tea conformity.

Tea authentication and determination of chemical constituents using digital image-based fingerprint signatures and chemometrics

Tea (Camellia sinensis) fraud has been frequently identified and involves tampering with the labelling of inferior products or without geographical origin certification and even mixing them with superior quality teas to mask an adulteration. Consequently, economic losses and health damage to consumers are observed. Thus, a Chemometrics-assisted Color Histogram-based Analytical System (CACHAS) was employed a simple, cost-effective, reliable, and green analytical tool to screen the quality of teas. Data-Driven Soft Independent Modeling of Class Analogy was used to authenticate their geographical origin and category simultaneously, recognizing correctly all Argentinean and Sri Lankan black teas and Argentinean green teas. For the determination of moisture, total polyphenols, and caffeine, Partial Least Squares obtained satisfactory predictive abilities, with values of root mean squared error of prediction (RMSEP) of 0.50, 0.788, and 0.25 mg kg^-1, r_pred of 0.81, 0.902, and 0.81, and relative error of prediction (REP) of 6.38, 9.031, and 14.58%., respectively. CACHAS proved to be a good alternative tool for environmentally-friendly non-destructive chemical analysis.

Characteristics and Relationships between Total Polyphenol and Flavonoid Contents, Antioxidant Capacities, and the Content of Caffeine, Gallic Acid, and Major Catechins in Wild/Ancient and Cultivated Teas in Vietnam

Vietnam has diverse and long-established tea plantations but scientific data on the characteristics of Vietnamese teas are still limited. Chemical and biological properties including total polyphenol and flavonoid contents (TPCs and TFCs), antioxidant activities (DPPH, ABTS, FRAP, and CUPRAC), as well as the contents of caffeine, gallic acid, and major catechins, were evaluated for 28 Vietnamese teas from North and South Vietnam. Higher values of TPCs and TFCs were found for green (non-oxidised) and raw Pu'erh (low-oxidised) teas from wild/ancient tea trees in North Vietnam and green teas from cultivated trees in South Vietnam, as compared to oolong teas (partly oxidised) from South Vietnam and black teas (fully oxidised) from North Vietnam. The caffeine, gallic acid, and major catechin contents depended on the processing, geographical origin, and the tea variety. Several good Pearson's correlations were found (r² > 0.9) between TPCs, TFCs, the four antioxidant capacities, and the content of major catechins such as (-)-epicatechin-3-gallate and (-)-epigallocatechin-3-gallate. Results from principal component analysis showed good discriminations with cumulative variances of the first two principal components varying from 85.3% to 93.7% among non-/low-oxidised and partly/fully oxidised teas, and with respect to the tea origin.

Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

The geographic origin of agri-food products contributes greatly to their quality and market value. Here, we developed a robust method combining metabolomics and machine learning (ML) to authenticate the geographic origin of Wuyi rock tea, a premium oolong tea. The volatiles of 333 tea samples (174 from the core region and 159 from the non-core region) were profiled using gas chromatography time-of-flight mass spectrometry and a series of ML algorithms were tested. Wuyi rock tea from the two regions featured distinct aroma profiles. Multilayer Perceptron achieved the best performance with an average accuracy of 92.7% on the training data using 176 volatile features. The model was benchmarked with two independent test sets, showing over 90% accuracy. Gradient Boosting algorithm yielded the best accuracy (89.6%) when using only 30 volatile features. The proposed methodology holds great promise for its broader applications in identifying the geographic origins of other valuable agri-food products.

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.

Sustainable and effective approach to recover antioxidant compounds from purple tea (Camellia sinensis var. assamica cv. Zijuan) leaves

Camellia sinensis var. assamica cv. Zijuan (purple tea) is known for its content of anthocyanins, flavan-3-ols, and bioactivities. This study aimed to verify the influence of solvent polarity, in a solid-liquid extraction, on the content of phenolic compounds and chlorophylls, instrumental color, and antioxidant activity. Different proportions of water and ethanol (0:100, 25:75, 50:50, 75:25, and 100:0 v/v) were used for extraction. The results showed that the hydroalcoholic extract (75 % ethanol + 25 % water) had the highest contents of total flavonoids, total anthocyanins, chlorophyll A, and total carotenoids, as well as presenting the highest color intensity, proportion of yellow pigments, and antioxidant activity (total reducing capacity and scavenging of the DPPH free radical). Twenty-two compounds were identified, with chlorogenic acid, hesperidin, (-)-epicatechin, (-)-epigallocatechin gallate, and isoquercitrin being the main phenolics. This phenolic-rich extract inhibited lipoperoxidation induced in egg yolk homogenate (IC₅₀ = 455 mg/L), showed no hemolytic behavior when human erythrocytes were subjected to osmotic stress, and exerted in vitro cytotoxic effects against cancer and hybrid cells. The extract obtained with the mixture of non-toxic solvents presented critical bioactivities, as well as a comprehensive identification of phenolic compounds in the cultivar, and has potential to be used in technological applications.

Validation of antioxidant, antiproliferative, and in vitro anti-rheumatoid arthritis activities of epigallo-catechin-rich bioactive fraction from Camellia sinensis var. assamica, Assam variety white tea, and its comparative evaluation with green tea fraction

The epigallocatechin-rich polyphenolic fraction of Assam variety white tea, traditionally used for the management of diverse inflammatory ailments and health drink, was investigated through eco-friendly green aqueous extraction, TLC, and HPLC characterization, phytochemical screening, in vitro DPPH assay, anti-proteinase, MTT assay on synovial fibroblast and colon cancer cells, apoptotic FACS analysis, cytokine ELISA, p-STAT3 western blotting, and in silico docking analysis. HPLC-TLC standardized white tea fraction (WT-F) rendered higher extractive-yield (21%, w/w), than green tea fraction(GT-F) (12%, w/w). WT-F containing flavonoids and non-hydrolysable polyphenols showed better antioxidant activity, rather than equivalent GT-F. WT-F demonstrated remarkable anti-rheumatoid-arthritis activity via killing of synovial fibroblast cells (66.1%), downregulation of TNF-α (93.33%), IL-6 (87.97%), and p-STAT3 inhibition (77.75%). Furthermore, WT-F demonstrated better anti-proliferative activity against colon cancer cells (HCT-116). Collectively, our study revealed that the white tea fraction has boundless potential as anti-rheumatoid arthritis and anti-proliferative agent coupled with apoptotic, antioxidant anti-proteinase, and anti-inflammatory properties. PRACTICAL APPLICATIONS: Our eco-friendly extracted bioactive aqueous fraction of white tea, characterized by TLC-HPLC study and phytochemical screening have demonstrated remarkable anti-rheumatoid arthritis property and anti-proliferative action on colon cancer cells including potential anti-oxidant, anti-inflammatory, and anti-proteinase efficacy. The test WT-F sample has shown impressive safety on normal mammalian cells. WT-F has demonstrated better efficacy against rheumatoid arthritis and cancer model compared to equivalent green tea fraction. Traditionally, it is extensively used for boosting immunity, and energy, with cosmetic, and agricultural applications by the native inhabitants. So, the aqueous fraction of WT is suggested to be used as a prophylactic nutraceutical supplement and or therapeutic agent in commercial polyherbal formulation to attenuate and management of auto-inflammatory rheumatoid arthritis and carcinogenesis of colon. It is additionally suggested to establish in vivo rheumatoid arthritis animal and clinical study to validate their pharmacokinetic stability and dose optimization coupled with anti-inflammatory, cytotoxicity, and anti-oxidant property.

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin

Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.