There has been a lot of research on the health benefits of consuming tea, especially the five main types that are produced from the leaves of the Camellia sinensis plant (white, black, green, oolong, and Pu-erh). Pu-erh tea is a fermented-tea that is produced from broad-leafed variety, Camellia sinensis var. Assamica. The tea shrub only grows in the southwest Yunnan province of China. To produce the tea, the leaves are stacked, dampened, and fermented during a 60- to 180-day process. Bacteria such as Actinoplanes and Streptomyces are added during the process, and these bacteria are responsible for the tea’s distinct, earthy flavor. Pu-erh has increased in popularity in many parts of the world in recent years, leading to an increase in production to meet the growing demand.
High value, specialty food products such as Pu-erh tea, which are characterized by origin and are produced in limited quantities, are more likely to be adulterated with lower-cost ingredients for financial gain. While teas can be differentiated by profiling the type and concentration of bioactive compounds such as flavonoids, phenolic acids, alkaloids, and polyamines, these compounds are known to be affected by oxidative processing. Increasingly, the trace element composition of foodstuffs is being used for authentication studies. Since the minerals and nutrients present in plants may be representative of the composition of the surrounding soil, plants grown in the same area tend to have their own characteristic elemental fingerprint.
Fast, Multi-Element Analysis
The fast, sensitive, multi-element capabilities of inductively coupled plasma-mass spectrometry (ICP-MS) make it an ideal tool for elemental fingerprinting. Instruments fitted with a collision/reaction cell that can operate in collision mode using an inert gas such as helium are especially useful for food authentication studies. Through multiple collisions with helium gas atoms, the relative transmission of larger, polyatomic ions is reduced compared to the smaller, monatomic analyte ions that they overlap. This allows the interferences to be resolved by kinetic energy discrimination. The approach lowers the detection limits and accuracy for many elements, providing large, high-quality datasets.
Triple quadrupole ICP-MS (also referred to as ICP-QQQ) offers greater flexibility than single quadrupole ICP-MS for applications that require greater sensitivity and accuracy for some specific elements. In addition to the helium collision mode, ICP-QQQ allows the controlled use of reaction cell gases to lower detection limits for elements that suffer from isobaric or doubly-charged ions.
Building a Model for Specialty Tea
Table 1. List of elements, calibration correlation coefficient (R), detection limits (DL), and background equivalent concentrations.
Twenty-four Chinese tea samples including Pu-erh (12 samples), gunpowder green (seven), breakfast (two), green (two), and oolong (one) were obtained directly from a U.S. import merchant. Four teas from Egypt (Moroccan mint variety) were also analyzed in this study. Each tea was sampled at least two times (different lots or boxes), and each sample was prepared in triplicate using microwave-digestion.
The tea samples were measured using an 8800 ICP-QQQ from Agilent Technologies. All elements that were determined above the detection limit are given in Table 1. As indicated by the calibration correlation coefficient, excellent linearity was achieved for both major elements and trace elements. Instrument setup, operation, data acquisition, and data processing were performed using the instrument’s ICP-MS MassHunter software. Mass Profiler Professional (MMP) chemometric software was used for statistical analysis of the dataset. All software was from Agilent Technologies.
Pattern Recognition Software
The multi-element data batch file (30 tea samples, 29 elements, 3 replicates) was imported into MMP chemometric software for statistical analysis using supervised discriminant analysis, specifically, canonical variate analysis with the R-plug-in tool in MPP. As a supervised technique, discriminant analysis is able to classify groups, and identify elemental drivers for tea authentication. Using this technique, all tea samples could be separated by region of origin (Figure 1) and by type of tea (Figure 2).
In Figure 1, samples from regions within China are closer to each other compared to the two Egyptian regions (Faiyum and Sikkim). Clear separation of each group is also achieved within the different Chinese regions. These results indicate that the elemental composition of teas is influenced by where the tea leaves are grown. Along the first dimension, explaining 70 percent of the total variance ratio, macro elements like Na, Mg, and Ca, micro elements like Sr, Zr, and Mo, as well as trace elements, including the rare earth elements La and Ce, were found to be higher in the Faiyum teas. Meanwhile, Mn, Ni, Zn, Rb, and Re were higher in the other teas, leading to the clear separation between the different regions.
A similar clear separation is shown in Figure 2, where teas were classified by type. The Moroccan mint teas clearly separate from the other teas, along the first dimension, explaining 68 percent of the total variance ratio. Further, green teas (i.e., simply green, green, and gunpowder green) were located closer to each other, with the gunpowder green teas being closer towards the black and fermented teas (i.e., Chinese breakfast, oolong, and Pu-erh).
Discriminating elements along the first dimension include the macro elements Mg, Na, Ca, and Fe, micro elements like Sr, Mo, and Hf, and rare earth elements like Ce, Pr, Nd, Sm, Eu, and Gd. All of these elements were higher in the Moroccan mint teas, while Mn, Ni, Cd, and Re showed higher levels in the green, black, and fermented teas.
Along the second dimension, where green teas separate from black and fermented teas, levels of Rb, Mn, W, Re, and Tl were higher in the black and fermented teas.
In Summary
Once a model has been established based on a full range of samples, elemental profiling can be used to identify the geographical origin of specialty foods and beverages.
In this study, multiple elements were determined in digests of 29 tea samples using ICP-MS to demonstrate the capability of multi-elemental profiling for authentication purposes. The teas, which were sourced from a reliable importer, included different varieties of tea, teas grown in different growing-regions of southern China, and teas from Egypt.
The methodology could be run on a single quadrupole ICP-MS. However, the use of a triple quadrupole ICP-MS provides better detection limits and accuracy for some analytes that are prone to complex interferences in certain matrices (e.g., rare earth elements).
Easy-to-use MPP chemometric techniques were applied to process the large dataset and to visualize the differences between samples. An exploratory analysis with discriminant analysis as a supervised multivariate analysis showed a clear separation of teas by region and by type.
Looking Forward
Future studies will aim to strengthen the model with additional data from the analysis of more teas from different geographical origins, as well as tea mixtures. A market basket study on all commercially available Pu-erh teas can then be carried out. The method has the potential to quickly check the authentication of tea on a routine basis.
Dr. Nelson is a market development spectroscopy scientist for Agilent CrossLab Group. Reach her at jenny_nelson@agilent.com. Dr. Hopfer is an assistant professor of food science at the Department of Food Science for Pennsylvania State University. Reach her at hxh83@psu.edu.
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