Variety identification and age prediction of Pu-erh tea using graphene oxide and porphyrin complex based mid-infrared spectroscopy coupled with chemometrics

Pu-erh tea is a unique post-fermented tea with special quality and unique aroma characteristics, which are achieved by aging as part of the tea-making processing in China. In this work, we developed a new method, based on the combination of the mid-infrared (MIR) spectroscopy of graphene oxide-5,10,15,20-mesotetra (4-aminophenyl) porphyrin hybrid (GO-TAP), with chemometrics, for the variety identification and age prediction of twelve Pu-erh teas. Partial least squares discriminant analysis (PLSDA) and partial least squares regression (PLSR) models were respectively employed to classify the variety of and predict the age of Pu-erh teas based on the collected MIR spectra. Compared with traditional MIR spectroscopy, MIR spectroscopy based on GO-TAP gave better accuracy and specificity in variety classification and age prediction. The GO-TAP complex-based MIR spectroscopy sensor coupled with chemometrics could achieve 100% accuracy for variety classification and predicts storage time of Pu-erh tea. This method can be applied for the accurate identification and prediction of Pu-erh tea storage time, and it might have enormous potential for the application in storage time identification of other food stuff.