Next, a supervised pattern recognition algorithm was applied using PLS-DA. As shown in Fig 2B, radiated and untreated cells were able to be distinguished in the principal component in the score chart. After 400 cross-validations, little over-fitting was observed and the data presented good predictive feature in the model (Fig 2C). On the basis of a reliable PLS-DA model, orthogonal partial least squares discriminant analysis (OPLS-DA) model was established by rotated principal–component projection to filter irrelevant information, and the predication of principal components in the samples of two individual groups showed the greatest distinction (Fig 2D). Furthermore, differentially expressed metabolites between the two group samples were distinguished by analyzing the VIP of each variety, the correlation coefficient r, and the loading value. According to VIP and the correlation coefficient r, a metabogram was presented that clearly indicated changes in metabolites in different treatments. In summary, in response to radiation, the contents of leucine, lactate, acetate, creatine, creatine phosphate, methanol, ethylene glycol, UDP-glucose, ATP, and formate were decreased in cells, while the contents of alanine, glutamate, taurine, choline, glycerol, and glycine were increased in cells.