Understanding processing, maturity and harvest period effects to authenticate early-spring Longjing tea using stable isotopes and chemometric analyses

Fertilizer Effects on the Nitrogen Isotope Composition of Soil and Different Leaf Locations of Potted Camellia sinensis over a Growing Season

The nitrogen-stable isotopes of plants can be used to verify the source of fertilizers, but the fertilizer uptake patterns in tea (Camellia sinensis) plants are unclear. In this study, potted tea plants were treated with three types of organic fertilizers (OFs), urea, and a control. The tea leaves were sampled over seven months from the top, middle, and base of the plants and analyzed for the δ15N and nitrogen content, along with the corresponding soil samples. The top tea leaves treated with the rapeseed cake OF had the highest δ15N values (up to 6.6‱), followed by the chicken manure, the cow manure, the control, and the urea fertilizer (6.5‱, 4.1‱, 2.2‱, and 0.6‱, respectively). The soil treated with cow manure had the highest δ15N values (6.0‱), followed by the chicken manure, rapeseed cake, control, and urea fertilizer (4.8‱, 4.0‱, 2.5‱, and 1.9‱, respectively). The tea leaves fertilized with rapeseed cake showed only slight δ15N value changes in autumn but increased significantly in early spring and then decreased in late spring, consistent with the delivery of a slow-release fertilizer. Meanwhile, the δ15N values of the top, middle, and basal leaves from the tea plants treated with the rapeseed cake treatment were consistently higher in early spring and lower in autumn and late spring, respectively. The urea and control samples had lower tea leaf δ15N values than the rapeseed cake-treated tea and showed a generalized decrease in the tea leaf δ15N values over time. The results clarify the temporal nitrogen patterns and isotope compositions of tea leaves treated with different fertilizer types and ensure that the δ15N tea leaf values can be used to authenticate the organic fertilizer methods across different harvest periods and leaf locations. The present results based on a pot experiment require further exploration in open agricultural soils in terms of the various potential fertilizer effects on the different variations of nitrogen isotope ratios in tea plants.

Tracing the geographical origin of tobacco at two spatial scales by stable isotope and element analyses with chemometrics

Tobacco is a widely cultivated cash crop, but it is often smuggled and sold illegally. Unfortunately, there is currently no way to verify the origin of tobacco in China. In an effort to address this issue, we conducted a study using stable isotopes and elements from 176 tobacco samples at both provincial and municipal scales. Our findings revealed significant differences in δ¹³C, K, Cs, and ^208/206Pb at the provincial-level, and Sr, Se, and Pb at the municipal level. We created a heat map at the municipal level, which showed a similar cluster classification to geographic grouping and provided an initial assessment of tobacco origins. Using OPLS-DA modeling, we achieved a 98.3% accuracy rate for the provincial scale and 97.6% for the municipal scale. It is worth noting that the importance of rankings of variables varied depending on the spatial scale of the evaluation. This study offers the first traceability fingerprint dataset of tobacco and has the potential to combat mislabeling and fraudulent conduct by identifying the geographical origin of tobacco.

Discrimination of the harvesting season of green tea by alcohol/salt-based aqueous two-phase systems combined with chemometric analysis

The flavor and aroma quality of green tea are closely related to the harvest season. The aim of this study was to identify the harvesting season of green tea by alcohol/salt-based aqueous two-phase system (ATPS) combined with chemometric analysis. In this paper, the single factor experiments (SFM) and response surface methodology (RSM) optimization were designed to investigate and select the optimal ATPS. A total of 180 green tea samples were studied in this work, including 86 spring tea and 94 autumn tea. After the active components in green tea samples were extracted by the optimal ethanol/(NH₄)₂SO₄ ATPS, the qualitative and quantitative analysis was realized based on HPLC-DAD combined with alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) algorithm, with satisfactory spiked recoveries (86.00 %-112.45 %). The quantitative results obtained from ATLD-MCR model were subjected to chemometric pattern recognition analysis. The constructed partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models showed better results than the principal component analysis (PCA) model, and the R²X_cum values (>0.835) and R²Y_cum (>0.937) were close to 1, the Q²_cum values were greater than 0.75 (>0.933), and the differences between R²Y_cum and Q²_cum were not larger than 0.2, indicating excellent cross-validation prediction performance of the models. Furthermore, the classification results based on the hierarchical clustering analysis (HCA) were consistent with the PCA, PLS-DA and OPLS-DA results, establishing a good correlation between tea active components and the harvesting seasons of green tea. Overall, the combination of ATPS and chemometric methods is accurate, sensitive, fast and reliable for the qualitative and quantitative determination of tea active components, providing guidance for the quality control of green tea.

Simultaneously Verifying the Original Region of Green and Roasted Coffee Beans by Stable Isotopes and Elements Combined with Random Forest

Simultaneously verifying the original region of green and roasted coffee beans is very important for protecting legal interests of the stakeholder according to the chemical analyzing method. 131 green coffee bean samples are collected from six different original regions and pretreated with three degrees (green, middle, and dark roasted); five stable isotope ratios (δ13C, δ14N, δ18O, δ2H, and δ32S) and twelve elemental contents (Al, Cr, Ni, Zn, Ba, Cu, Na, Mn, Fe, Ca, K, and Mg) of green, middle, and dark roasted coffee bean samples (131×3) were analyzed. Fractionation of stable isotopes and variation of elemental contents were evaluated, only isotope hydrogen (2H) significantly fractionated, and elemental concentrations increased with a certain rate during the roasting process. One-way analysis of variance (ANOVA) was used to compare the stable isotope ratios and elemental concentrations of all coffee bean samples from six different original regions. Random forest (RF) was employed to build a discriminating model for simultaneously verifying the original regions of green and roasted coffee bean samples; this model provided 100% accuracy. Inclusion of this mathematical model for simultaneously verifying the original region of green and roasted coffee beans had powerful distinguishing capability and which will not be influenced by fractionation of hydrogen (2H) and variation of element contents during the roasted process.

High-Throughput Flow Injection Analysis-Mass Spectrometry (FIA-MS) Fingerprinting for the Authentication of Tea Application to the Detection of Teas Adulterated with Chicory

Tea is a broadly consumed beverage worldwide that is susceptible to fraudulent practices, including its adulteration with other plants such as chicory extracts. In the present work, a non-targeted high-throughput flow injection analysis-mass spectrometry (FIA-MS) fingerprinting methodology was employed to characterize and classify different varieties of tea (black, green, red, oolong, and white) and chicory extracts by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Detection and quantitation of frauds in black and green tea extracts adulterated with chicory were also evaluated as proofs of concept using partial least squares (PLS) regression. Overall, PLS-DA showed that FIA-MS fingerprints in both negative and positive ionization modes were excellent sample chemical descriptors to discriminate tea samples from chicory independently of the tea product variety as well as to classify and discriminate among some of the analyzed tea groups. The classification rate was 100% in all the paired cases-i.e., each tea product variety versus chicory-by PLS-DA calibration and prediction models showing their capability to assess tea authentication. The results obtained for chicory adulteration detection and quantitation using PLS were satisfactory in the two adulteration cases evaluated (green and black teas adulterated with chicory), with calibration, cross-validation, and prediction errors below 5.8%, 8.5%, and 16.4%, respectively. Thus, the non-targeted FIA-MS fingerprinting methodology demonstrated to be a high-throughput, cost-effective, simple, and reliable approach to assess tea authentication issues.

Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics

The geographical origin and processing month of green tea greatly affect its economic value and consumer acceptance. This study investigated the feasibility of combining near-infrared hyperspectral imaging (NIR-HSI) with chemometrics for the identification of green tea. Tea samples produced in three regions of Chongqing (southeastern Chongqing, northeastern Chongqing, and western Chongqing) for four months (from May to August 2020) were collected. Principal component analysis (PCA) was used to reduce data dimensionality and visualize the clustering of samples in different categories. Linear partial least squares-discriminant analysis (PLS-DA) and nonlinear support vector machine (SVM) algorithms were used to develop discriminant models. The PCA-SVM models based on the first four and first five principal components (PCs) achieved the best accuracies of 97.5% and 95% in the prediction set for geographical origin and processing month of green tea, respectively. This study demonstrated the feasibility of HSI in the identification of green tea species, providing a rapid and nondestructive method for the evaluation and control of green tea quality.

Stable isotope and photosynthetic response of tea grown under different temperature and light conditions

The stable isotope and photosynthesis response of tea (Camellia sinensis) is determined under different light and temperature conditions. The results showed that isotopes of young tea leaves were more enriched with increasing light intensity (31 ~ 411 µmol m^-2∙s^-1). However, the value of δ¹³C and δ¹⁵N seemed depleted, while δ²H and δ¹⁸O became enriched as temperature increasing from 15 to 35 °C. Significant isotope differences were found in tea leaves harvested between early growth (0 ~ 10 days) and later growth (10 ~ 21 days) periods (p < 0.05). Pearson's correlation showed a negative correlation between isotopes (δ¹³C, δ¹⁵N and δ²H) and photosynthetic parameters (EVAP and CI) ranging from 0.497 to 0.872, under 25 °C/203 µmol m^-2∙s^-1. But δ¹⁸O had a weak correlation with all photosynthetic parameters under the same conditions. These distinctive correlations between isotopes and photosynthetic parameters provide new insights which could be used to predict tea isotope responses arising from subtle seasonal or climate change conditions.

Tea and Chicory Extract Characterization, Classification and Authentication by Non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics

Tea is a widely consumed drink in the world which is susceptible to undergoing adulterations to reduce manufacturing costs and rise financial benefits. The development of simple analytical methodologies to assess tea authenticity, as well as to detect and quantify frauds, is an important matter considering the rise of adulteration issues in recent years. In the present study, untargeted HPLC-UV and HPLC-FLD fingerprinting methods were employed to characterize, classify, and authenticate tea extracts belonging to different varieties (red, green, black, oolong, and white teas) by partial least squares-discriminant analysis (PLS-DA), as well as to detect and quantify adulteration frauds when chicory was used as the adulterant by partial least squares (PLS) regression, to ensure the authenticity and integrity of foodstuffs. Overall, PLS-DA showed a good classification and grouping of the tea samples according to the tea variety and, except for some white tea extracts, perfectly discriminated from the chicory ones. One hundred percent classification rates for the PLS-DA calibration models were achieved, except for green and oolong tea when HPLC-FLD fingerprints were employed, which showed classification rates of 96.43% and 95.45%, respectively. Good predictions were also accomplished, also showing, in almost all the cases, a 100% classification rate for prediction, with the exception of white tea and oolong tea when HPLC-UV fingerprints were employed that exhibited a classification rate of 77.78% and 88.89%, respectively. Good PLS results for chicory adulteration detection and quantitation were also accomplished, with calibration, cross-validation, and external validation errors beneath 1.4%, 6.4%, and 3.7%, respectively. Acceptable prediction errors (below 21.7%) were also observed, except for white tea extracts that showed higher errors which were attributed to the low sample variability available.