## 2 Results
### Identification of VOCs by HS-SPME-GC-Ms
In this study, OPLS-DA analysis was used to eliminate the interference of sample differences from different batches of the same Chinese medicinal materials on the VOC characteristics. OPLS-DA is usually applied as an approach to discriminate two or more groups and to model multiple classes simultaneously [24]. The characteristics of OPLS-DA are integrated orthogonal signal correction filters, which can separate system changes in the prediction (related to Y-related) and orthogonal (and Y-in-correlated) components. Therefore, OPLS-DA can reduce system noise and extract variable information, with stronger classification capabilities. [PERSON] et al. used OPLS-DA to compare the applicability and predictive ability of E-nose and HS-SPME-GC-MS for the regional classification of Baijiu samples [25]. In our study, to achieve more accurate classification results, preprocessing is applied to experimental data. First, peak area collected from HS-SPME-GC-MS and HS-GC-IMS was subjected to a logarithmical transformation. The peak area acquired was transformed to log10, thus narrowing the scope of data [26]. It was then centered and pareto-scaled for the transformed variables. The Pareto scale, through dividing by the square root of the standard deviation of each column, for each variable provides a standard deviation that is equal to its initial variance [27]. For more than 50% of variables, the missing value has been excluded before the model is established, the datasets of different regional samples are introduced into the OPLS-DA model to perform region classification. The OPLS-DA models are based on the results of HS-SPME-GC-MS and HS-GC-IMS; the whole sample size in the two models was set at 54 (six cultivars, three batches \(\times\) triplicate). Seventy-four VOCs were used in the OPLS-DA model of HS-GC-IMS, while a total of 49 VOCs were used in the OPLS-DA model of HS-SPME-GC-MS. The sevenfold cross  
Figure 2: (**A**) Total ion chromatograms of volatile oils in SS of the B region; (**B**) reference compounds spectrogram; (**C**) the heat map clustering of the VOCs in SS samples in different regions (The codes of the compounds correspond to those in Table 2). validation procedures were used to validate the OPLS-DA models. The component is significant if the Y variation fraction was predicted by the X model >0.01 [25]. Then, based on analytical methods of both HS-GC-IMS and HS-SPME-GC-MS, five predictive components and eight orthogonal components were selected for the OPLS-DA model. As shown in Figure 3A,C, the classification model fitness of model based on HS-GC-IMS was 96.9% (R2Y(cum) = 0.969), while, for HS-SPME-GC-MS, it was 93.5% (Q2(cum) = 0.935). From the prediction ability point of view, the model built from HS-GC-IMS was as high as 95.5% (Q2(cum) = 0.935); however, it was only 85.9% (Q2(cum) = 0.859) correspondingly in HS-SPME-GC-MS. The higher values of R2 and Q2 indicated that the model based on HS-GC-IMS had better fitness and prediction ability compared with HS-SPME-GC-MS. From the predictive point of view, both models acquired Q2 > 0.5, indicating that there are good predictive capabilities, but the predictive ability of the HS-GC-IMS model is better. In order to better verify the ruggedness of the two OPLS-DA model, the two models have been exchanged for 200 replacement experiments. As shown in Figure 3B,D, the values of intercepts of randomly permutated models were significantly lower than that of the original one, indicating the ruggedness of the developed model [28]. As can be observed, in the score plot of OPLS-DA model of HS-GC-IMS, the SS from different regions show strong clustering, without any overlap. However, in the score plot of OPLS-DA mode of HS-SPME-GC-MS, SS samples of six geographical origins were not clearly discriminated. Samples from regions A, B, and C were not separated and samples from regions N and B also overlapped.