**4. Discussion**

It has been shown previously that spectroscopy technology can be applied in quantificational evaluations of the quality parameters of agricultural products. This was a new exploration to intelligently evaluate SCC for Korla fragrant pears by Vis/NIR reflection spectroscopy. An intelligent evaluating system was introduced. Its illuminating angle could be adjusted and Korla fragrant pears could be rotated along the long axis with a set angle.

Several processes were undertaken in order to obtain an accurate and stable evaluating model of SCC. The SNV algorithm had the best evaluation effect among the spectral pretreatment methods, according to evaluating accuracy and robustness. A total of 83 characteristic wavelengths were selected by using Vis/NIR spectra combined with MCUVE algorithm. Finally, the MCUVE-PSO-SVR model was established. The correlation coefficients of the Cs and Vs were 0.966 and 0.951, and the root mean square errors of both sets were 0.0209% and 0.0239%. The results demonstrate that the model could achieve the quantitative detection of SCC in Korla fragrant pear.

The nondestructive and rapid evaluation system combined with the MCUVE-PSO-SVR model for the SCC of Korla fragrant pears could meet the requirements of detection time and evaluation precision. It could be rendered more suitable for industry work requirements through the adjustment of key parameters and the development of supporting equipment. Its transformation and applications in industry are more meaningful, and present an attractive target for future research.

**Author Contributions:** Resources, Y.L.; data curation, Y.Z.; writing—original draft preparation, T.W.; writing—review and editing, Y.L.; visualization, Z.Z.; supervision, T.Y.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China, grant number 31960498, the Open Project Program of the Key Laboratory of Colleges & Universities under the Department of Education of Xinjiang Uygur Autonomous Region, grant number TDNG20221044 and TDNG20200102.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the request of funding scientific research projects.

**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
