Firstly, principal component analysis (PCA) was applied to perform data-dimensionality reduction and get a data preview . Afterwards, we use partial least squares discriminant analysis (PLS-DA), a supervised pattern recognition method, to determine the modeling that generated the greatest difference between radiated sample and untreated sample. Grouping information used as response variable Y in calculating PLS-DA model. Q2 and R2were generated by software calculation to evaluate the degree of fit and forecasting ability of model. To avoid over-fitting, 7-fold cross-validation was performed for 400 repeats [21–23].