High-Pressure Photoionization/Chemical Ionization-Time-of-Flight Mass Spectrometry for Classification and Identification of Green Tea Aromas

As the basic element of tea, aroma is one of the most important factors determining the quality of tea. In this paper, a novel and objective method for rapid discrimination and classification of green tea aromas was developed by coupling of dynamic headspace sampling high-pressure photoionization/chemical ionization-time-of-flight mass spectrometer (HPPI-TOFMS) and multivariate statistical analysis techniques, such as partial least squares discrimination analysis (PLS-DA) and layer cluster analysis (HCA). The classification efficiency was evaluated by analyzing the receiver operating characteristic (ROC) curve. The observations and the robustness were assessed using 200 permutation tests. The volatile fingerprint mass spectra of 31 green tea samples of 4 sub. types (tender chestnut-like, tender-like, chestnut-like and roasted chestnut-like aroma) collected from different production areas in Sichuan, Guizhou, Zhejiang, Anhui, etc, were obtained based on this method. In the HPPI/CI mass spectra of the aroma VOCs from different sub-types of green teas, the types and relative intensities of the characteristic mass peaks were obviously different. The results of multivariate statistical analysis showed that there were little differences among the aroma VOCs of the same sub-type, but significant differences were exhibited among different sub-types, which could achieve accurate classification and identification. The prediction ability of the model for unknown samples reaches 85.1%. This method provided a new idea and approach for the effective identification of green tea aromas, indicating that it had potential application value and broad development prospects in the fields of food quality and safety evaluation.