Protected Geographical Indication Identification of a Chinese Green Tea (Anji-White) by Near-Infrared Spectroscopy and Chemometric Class Modeling Techniques

Abstract

This paper reports a rapid identification method for a Chinese green tea with PGI, Anji-white tea, by class modeling techniques and NIR spectroscopy. 167 real and representative Anji-white tea samples were collected from 8 tea plantations in their original producing areas for model training. Another 81 non-Anji-white tea samples of similar appearance were collected from 7 important tea producing areas and used for validation of model specificity. Diffuse NIR spectra were measured with finely ground tea powders. OCPLS and SIMCA were used to describe the distribution of representative Anji-white tea objects and predict the authenticity of new objects. For data preprocessing, smoothing, derivatives, and SNV were applied to improve the raw spectra and classification performance. It is demonstrated that taking derivatives and SNV can improve classification accuracy and reduce the complexity of class models by removing spectral background and baseline. For the best models, the sensitivity and specificity were 0.886 and 0.951 for OCPLS, 0.886 and 0.938 for SIMCA with SNV spectra, respectively. Although it is difficult to perform an exhaustive analysis of all types of potential false objects, the proposed method can detect most of the important non-Anji-white teas in the Chinese market.

Liver Injury Induced by Carbon Tetrachloride in Mice Is Prevented by the Antioxidant Capacity of Anji White Tea Polyphenols

“…The raw material of AJWT is the leaves of a tea plant, which are light green because of a chlorophyll deficiency caused by variance, and its tea soup is almost colorless. Tea products are made according to green tea processing technology and are classified as non-fermented tea [13]. AJWT is rich in polyphenols.…”

Preventive Effect of Anji White Tea Flavonoids on Alcohol-Induced Gastric Injury through Their Antioxidant Effects in Kunming Mice

“…However, AJWT varies from other white tea in its processing technology. It is non-fermented tea and is classified as green tea; it is made through six processes (picking, spreading, killing, sorting, drying, and preservation), unlike other white tea [4]. Many bioactive compounds are found in flavonoids, which can prevent and treat cardiovascular and cerebrovascular diseases by reducing the fragility of blood vessels, improving the permeability of blood vessels, reducing blood lipids and cholesterol, preventing and treating hypertension, cerebral hemorrhage, coronary heart disease, and angina pectoris, expanding coronary vessels, and increasing coronary flow [5,6].…”

The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

“…e same kind of tea from different geographical origins might vary dramatically in prices and quality [17]. erefore, almost all of the famous teas are labeled with their origins, such as Anji-white tea, Anxi-Tieguanyin tea, and Yingde-black tea [36,53]. However, some merchants fraudulently falsify the geographical origins of tea for illegal profits.…”

“…Based on the SNV preprocessing, the sensitivity and specificity were 0.886 and 0.938 for SIMCA and 0.886 and 0.951 for OCPLS, respectively. Although it is hard to achieve the exhaustive analysis of all types of potential counterfeits, NIR spectrometry coupled with SNV-OCPLS and SNV-OCPLS models could rapidly detect most of the non-Anji-white teas in the Chinese market [36]. Besides, Zhuang et al used the NIR spectroscopy to classify the green tea from two geographical origins.…”

“…e preprocessing of spectral data should be used for eliminating the noise and baseline shift from the background and instrument [33]. e commonly used preprocessing methods in tea analysis include standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky-Golay (SG) smoothing [35,36]. Various variables selection methods, such as synergy interval partial least squares (Si-PLS) and successive projections algorithm (SPA), are used for the screen of useful variables [37].…”