Lu'an Gua Pian tea (LAGP) is a traditional historical tea and one of the top ten famous teas in China. The price of LAGP from the same place of origin with different harvest time varies greatly in the market, among which the LAGP before the Qingming Festival (between April 4th and 6th every year) is regarded as a precious product. To accurately determine the harvest time of LAGP, especially around Qingming, HPLC-DAD combined with three different data analysis strategies (including targeted components analysis, non-targeted components analysis, and non-targeted fingerprint analysis) was evaluated and compared. Four machine learning algorithms are used to build the corresponding classification models, among which principal component analysis coupled with linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA) and K-nearest neighbors (K-NN) achieved more than 95% classification accuracy. In addition, the non-targeted component analysis can classify LAGP picking time to a much smaller extent with an accuracy of 100% for PLS-DA. The advantages and disadvantages of three data analysis strategies were compared. Regardless of the data analysis strategy used, the final classification accuracy was satisfactory. The corresponding data analysis strategy can be selected according to the specific experimental purpose. This work provides a variety of alternative solutions based on HPLC-DAD for identifying the picking time of Lu'an Gua Pian.
Yao Chen,Yuan Li,Li-Lin Lin,Yue Liao,Huan Fang,Tong Wang
Lu’an Gua Pian (LAGP) tea is one of the most famous green teas in China. The quality of green tea is related to its picking periods, especially the green tea before Qingming Festival (usually April 6th) is highly praised as precious in the market. In this work, a simple and cheap indicator displacement colorimetric sensor array combined with smartphone was developed to rapidly identify LAGP picked during different picking periods. First, the chemical component contents of LAGP picked before and after Qingming Festival were analyzed. Second, a well-designed colorimetric sensor array was proposed based on the tea component contents differences. Finally, machine learning was used to process the array data taken by a smartphone. By comparison, the accuracy of the best model for the prediction set was 97%. Meanwhile, the multi-channel advantages of the sensing array were demonstrated by an ablation experiment. In addition, the method achieved an AGREE analysis score of 0.88, indicating that it was environmental-friendly.
Hang Ren,Lin-Li Lin,Ming-Yue Dong,Xiao-Yue Yin,Tong Wang,Hai-Long Wu,Ru-Qin Yu
Lu’an Guapian (LAGP) is a renowned green tea, with its price significantly higher when picked before the Qingming Festival compared to after, posing risks of confusion and counterfeiting. This study proposed using an excitation-emission matrix (EEM) fluorescence method combined with chemometrics for rapid identification of tea picked before and after Qingming Festival. Firstly, the differences among the EEM fingerprints of different tea samples were analyzed using the alternating trilinear decomposition (ATLD) algorithm. To determine the differences between LAGP before and after Qingming Festival, the total contents of ten main components in tea were detected, and their effects on the EEM fluorescence fingerprint of tea were analyzed using correlation heatmaps. Finally, two chemometric algorithms, partial least squares discriminant analysis (PLS-DA) and k -nearest neighbor ( k -NN), were used to classify LAGP before and after Qingming Festival, achieving a classification accuracy of 100% for the training set, test set, and prediction set. To further explore the potential of this method, LAGP was further classified in detail according to four detailed picking periods, achieving an accuracy of over 83%. The same chemometric algorithm was used to classify the data based on the high-performance liquid chromatography (HPLC) method, yielding results comparable to those of the EEM-based method, though slightly inferior. Variable importance projection (VIP) analysis shows that catechin analogs are the main contributors to the classification of LAGP. The results demonstrated the EEM method’s significant potential in identifying the picking time of green tea.
危险品化学品经营许可证（带存储）