Rapid authentication of Chinese oolong teas using atmospheric solids analysis probe-mass spectrometry (ASAP-MS) combined with supervised pattern recognition models

Abstract Ambient mass spectrometry (AMS) is an emerging technique in food authenticity and traceability study due to the minimal sample preparation required and short analysis time. Herein, a non-targeted fingerprinting approach using an AMS technique, atmospheric solids analysis probe – mass spectrometry (ASAP-MS), was used to authenticate Chinese oolong teas. In the first part of the study, a total of 38 authentic samples from three main varieties – Guangdong Dancong, Taiwan Dongding, and Anxi Tieguanyin – were analysed and four discriminant analysis models were built using the fingerprint data from ASAP-MS. The principal component analysis-k nearest neighbour (PCA-kNN) model yielded the best classification outcome, where the classification accuracies of the training and validation sets were 100% and 92.6%, respectively. The second part of the study involved detecting possible adulteration of Anxi Tieguanyin, which is a high-value oolong tea under the register of protected geographical indication (PGI) of the European Union (EU). Adulteration of Anxi Tieguanyin was simulated by blending the authentic samples with 20–80% w/w of low-quality oolong teas. One-class modelling using data-driven soft independent modelling of class analogies (DD-SIMCA), with the Anxi Tieguanyin as the target class, was built using the fingerprint data of the authentic and adulterated samples. An excellent sensitivity of 100% and a high specificity of 98.1% were achieved, indicating that it is possible to detect substitution adulteration of Anxi Tieguanyin using ASAP-MS combined with one-class modelling. Overall, findings from this study exemplify the potential of ASAP-MS to be used for rapid, inexpensive, and high throughput classification of Chinese oolong tea varieties and screening for substitution adulteration of Anxi Tieguanyin oolong tea.