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.
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Explore this issueDecember/January 2019
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
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).