## 4 Conclusions  
Near-infrared hyperspectral imaging coupled with multivariate analysis was used to determine chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content in fresh and dry _Chrysanthemum morifolium_. Pixelwise spectra at the spectral range of 975-1646 nm were extracted and preprocessed by wavelet transform (WT) and area normalization. Successive projections algorithm (SPA) was used to select optimal wavelengths and the number of wavelengths reduced at least 95%. PLS, ELM, and LS-SVM models using full spectra and optimal wavelengths obtained good performances for both fresh and dry flowers, and most of the models had R\({}^{2}\), R\({}^{2}_{\rm P}\) over 0.80 and RPD over 2.00. Bias values of all models were small. These results indicated that hyperspectral imaging combined with multivariate analysis methods could be used to determine chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content in fresh and dry _Chrysanthemum morifolium_ flowers. In general, ELM performed relatively better than the other two calibration methods. For different chemical compositions, the models with the best performances were different, and the differences among different calibration methods were small. Statistically, these three calibration methods could all be used for chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content determination in fresh and dry _Chrysanthemum morifolium_. Indeed, the performances were not satisfactory for real-world applications and the performances of the calibration models should be improved in future studies. Prediction maps of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content were obtained, and variations of these chemical compositions content among flowers could be found. The results would be helpful to develop rapid and nondestructive online quality detection and sorting systems or portable devices during the process of harvest, processing, and consumption of _Chrysanthemum morifolium_. In future studies, strategies to improve the model robustness and accuracy should be conducted. More calibration methods should be explored, and the optimal calibration method would be selected for real-world application.  
Figure 3: Pseudo image of (**a**) dry _Chrysanthemum morifolium_ flowers and prediction maps of chlorogenic acid (**b**), luteolin-7-O-glucose (**c**), and 3,5-O-dicaffeoylquinic acid (**d**). (Unit: % DW, DW means dry weight.)  
**Author Contributions:** Conceptualization, J.H., B.C., and C.Z.; Data curation, S.Z., X.B., and Q.X.; Formal analysis, S.Z., X.B., and Q.X.; Funding acquisition, J.H., C.Z., and J.G.; Investigation, J.H., B.C., and C.Z.; Methodology, J.H., S.Z., B.C., X.B., Q.X., C.Z., and J.G.; Project administration, C.Z.; Resources, J.H. and C.Z.; Software, J.H., B.C., and C.Z.; Supervision, B.C. and C.Z.; Validation, S.Z., X.B., and Q.X.; Visualization, S.Z., X.B., and Q.X.; Writing--original draft, J.H., B.C., and C.Z.; Writing--review & editing, S.Z., X.B., and Q.X.  
**Funding:** This research was funded by the Program of Zhejiang Provincial Department of Science and Technology (LGC19H280005) and National Natural Science Foundation of China (61705195, 31871763).  
**Acknowledgments:** We thank [PERSON] (Ph.D. candidate, College of Biosystems Engineering and Food Science, Zhejiang University) for checking the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.