**4. Conclusions**

The findings in this study showed that *G. rigescens* chemical profiles were influenced by the latitude gradients of producing areas and lower latitudes and higher latitudes samples seemed to be clearly distinguishable. According to the score plots of PCA and OPLS-DA, the phytochemical geographic variation of the overground and underground part along the latitude gradients was visualized. Subsequently, the potential of fingerprint data obtained while using HPLC-DAD to discriminate and classify *G. rigescens* grown in four different latitudes was investigated. Additionally, RF and OPLS-DA models were used to develop an effective way for geographical traceability of the *G. rigescens* that were grown in four different latitudes. When using independent data sets to build models, rhizomes data set combined with OPLS-DA presented the best performance with a classification accuracy of calibration and validation set varied from 94.68% to 98.94%. In a further step, the feasibility of combining the chromatographic fingerprint data from overground and underground organs was investigated based on two kinds of data fusion strategies in order to improve the performance of classification models: low-level and mid-level. Notably, classification performances of OPLS-DA models were efficiently improved by low-level data fusion strategy and better performances of RF models appeared to be achieved by mid-level data fusion strategy. Although satisfactory results were obtained with both RF and OPLS-DA based on two kinds of data fusion strategies, OPLS-DA combined with rhizome-stem fusion data set was the optimum model for discriminating *G. rigescens* samples according to their grown latitudes, with an accuracy of (97.87–100.00%), SE of (0.96–1.00), SP of (0.98–1.00), MCC of (0.95–1.00), and EFF of (0.97–1.00).

**Supplementary Materials:** The following are available online. Figure S1: Variation of stems score plots along the latitude gradients, Figure S2: Variation of stems score plots between the adjacent latitudes, Figure S3: Variation of leaves score plots along the latitude gradients, Figure S4: Variation of leaves score plots between the adjacent latitudes, Figure S5: Permutation plot of the OPLS-DA of rhizome samples, Figure S6: Permutation plot of the OPLS-DA of stem samples, Figure S7: Permutation plot of the OPLS-DA of leaf samples, Figure S8: The *n*tree and *<sup>m</sup>*try screening of RF models based on low-level data fusion strategy, Figure S9: Result of variables selection of rhizome fingerprint data based on "Boruta" algorithm, Figure S10. Result of variables selection of stem fingerprint data based on "Boruta" algorithm, Figure S11: Result of variables selection of leaf fingerprint data based on "Boruta" algorithm, Figure S12: The *n*tree and *<sup>m</sup>*try screening of RF models based on mid-level data fusion strategy, Figure S13: The importance variables of OPLS-DA models of rhizomes, stems and leaves fingerprints data, Figure S14: Permutation testing (200 times) of the R\_OPLS-DA model, Figure S15: Permutation testing (200 times) of the S\_OPLS-DA model, Figure S16: Permutation testing (200 times) of the L\_OPLS-DA model, Figure S17: Permutation testing (200 times) of the RS\_OPLS-DA model based on low-level data fusion, Figure S18: Permutation testing (200 times) of the RL\_OPLS-DA model based on low-level data fusion, Figure S19: Permutation testing (200 times) of the SL\_OPLS-DA model based on low-level data fusion, Figure S20: Permutation testing (200 times) of the RSL\_OPLS-DA model based on low-level data fusion, Figure S21: Permutation testing (200 times) of the RS\_OPLS-DA model based on mid-level data fusion, Figure S22: Permutation testing (200 times) of the RL\_OPLS-DA model based on mid-level data fusion, Figure S23: Permutation testing (200 times) of the SL\_OPLS-DA model based on mid-level data fusion, Figure S24: Permutation testing (200 times) of the RSL\_OPLS-DA model based on mid-level data fusion, Table S1: The evaluation indexes for predictive power of OPLS-DA model of rhizome, stem and leaf, Table S2: The evaluation indexes for predictive power of OPLS-DA models based on low-level and mid-level data fusion strategies.

**Author Contributions:** H.Y. and Y.-Z.W. designed the project and revised the manuscript. T.S. performed the experiments, analyzed the data and wrote the manuscript.

**Funding:** This research was supported by the Key Project of Yunnan Provincial Natural Science Foundation (2017FA049), the Projects for Applied Basic Research in Yunnan (2017FH001-028), Biodiversity Survey, Monitoring and Assessment (2019HB2096001006) and the Department of Science and Technology of Yunnan Province (2018IA075).

**Conflicts of Interest:** The authors declare no conflict of interest.
