## 4 Conclusions  
Hyperspectral imaging covering two different spectral ranges (380-1030 nm and 874-1734 nm) was successfully utilized to identify Longjing tea from six geographical origins. Single tea leaves were used for analysis. PCA was firstly applied on pixel-wise spectra to form score images of each PC. For hyperspectral images at the two spectral ranges, differences among samples from different geographical origins could be observed in the PCA score images. Furthermore, spectral data were extracted from each single tea leaf for modeling. SVM and PLS-DA models built using spectral data at the spectral range of 502-947 nm and 975-1646 nm obtained good performances, with classification accuracy of the calibration and prediction sets over 84%. SVM and PLS-DA models obtained close  
Figure 3: Prediction maps obtained from the hyperspectral images of Longjing tea from six different geographical origins at the two different spectral ranges. (**a**) spectral range 1: 502-947 nm; (**b**) spectral range 2: 975-1646 nm. (geographical origins from left to right: Bashan Village, TongWu Village, Meijiawu Village, Tiedian Village, Xinxin Village, and Huijailing Village).  
performances. The classification performances of hyperspectral images at two different spectral ranges were close. Moreover, the classification accuracy of tea samples from each geographical origin varied. The prediction maps for identifying the geographical origins of Longjing tea were obtained by applying SVM models on the hyperspectral images. The prediction maps provided visual classification results and helped to sort Longjing tea samples within one image. The overall results illustrate the potential of using hyperspectral imaging to identify the geographical origins of single tea leaves at two different spectral ranges. Although good classification results were obtained, more studies are needed to improve the classification results. This study indicates the great potential for the development of the on-line geographical origin tracing platform of Longjing tea based on hyperspectral imaging and machine learning methods.  
The following are available on [[http://www.mdpi.com/2076-3417/10/3/1173/s1](http://www.mdpi.com/2076-3417/10/3/1173/s1)]([http://www.mdpi.com/2076-3417/10/3/1173/s1](http://www.mdpi.com/2076-3417/10/3/1173/s1)), Figure S1: Score images of the first ten PCs of spectral range 1 (502-947 nm) for tea samples from six different geographical origins. (a-j) represent PC1, PC2,... PC10. (The numbers 1, 2, 3, 4, 5, and 6 represent the corresponding geographical origin of Bashan Village, TongWu Village, Meijiawu Village, Tiedian Village, Xinxin Village, and Huijaling Village), Figure S2: Score images of the first ten PCs of spectral range 2 (975-1646 nm) for tea samples from six different geographical origins. (a-j) represent PC1, PC2,... PC10. (The numbers 1, 2, 3, 4, 5, and 6 represent the corresponding geographical origin of Bashan Village, TongWu Village, Meijiawu Village, Tiedian Village, Xinxin Village, and Huijaling Village).  
Conceptualization, Z.H.; data curation, Z.H.; formal analysis, Z.H.; funding acquisition, Y.H.; investigation, Z.H.; methodology, Z.H.; project administration, Y.H.; resources, Z.H.; software, Z.H.; supervision, Y.H.; validation, Y.H.; visualization, Y.H.; writing--original draft, Z.H.; writing--review and editing, Z.H. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFD0700501.  
We would like to thank [PERSON] (a postdoctoral fellow at the College of Biosystems Engineering and Food Science, Zhejiang University) for providing help on the experimental design and data analysis strategy during the research.  
The authors declare no conflicts of interest.