## 5 Conclusions  
In this paper, the lunar soil characterization consortium data of lunar regolith consisting of Apollo samples is taken as the research object. The PLS regression model is used to estimate the spectra of lunar regolith measured in RELAB laboratory of Brown University. The mineral contents in lunar highland and lunar mare have been optimized and inversed.  
PLS method can identify system information and noise, eliminate data redundancy, extract comprehensive variables with the strongest explanatory power to the dependent variables, and build models with high accuracy when the number of samples is less than the number of variables. This method can be used to optimize the inversion of mineral content in lunar regolith, make up for the limited number of Apollo and Luna lunar landing points and the insufficiency of a small number of collected samples of lunar regolith, and can be further applied to mineral identification of lunar regolith on a whole-moon scale.  
The spectra data of lunar regolith were pre-processed with MSC to eliminate the influence of sample size, density, surface roughness and other surface factors on the spectra, so as to weaken or eliminate the influence of various non-target factors on the spectra and highlight the spectra features of lunar regolith. The cross-validity test method was used to select the best number of principal components, and the information of mineral content in lunar regolith was maximally expressed and the error was small.  
In the study, PLS models have been established for samples from lunar highland and lunar mare respectively. Two-thirds of samples have been randomly selected as experimental group to establish the prediction relationship between the spectra of lunar regolith and mineral content. The remaining one-third of samples have been used as verification group to further verify the prediction relationship. The results show that the prediction of mineral content of 20 lunar highland samples and 18 lunar mare samples in the experimental group is mostly accurate, and the errors between simulated and measured mineral content of plagioclase, olivine, pyroxene, ilmenite, agglutinate and volcanic glass are small. Based on the PLS equation established by the experimental group, the mineral content of 10 lunar highland samples and 9 lunar mare samples of the validation group was further predicted, with little error from the measured values. The PLS model has high accuracy and good stability. It has good application prospects to optimize the inversion of mineral content in lunar regolith using spectral data of lunar regolith.  
P.Z. and Z.Z. are equally contributed to this paper, both of them are co-first authors. The specific contribution of all authors are: Conceptualization, P.Z., H.H. and Z.Z.; methodology, Z.Z.; software, Z.Z.; validation, Z.Z., P.Z. and H.H.; formal analysis, Z.Z.; investigation, Z.Z., G.W. and P.Z.; resources, Z.Z. and G.W.; data curation, G.W.; writing--original draft preparation, Z.Z., P.Z. and H.H.; writing--review and editing, H.H.; supervision, P.Z. and H.H.; project administration, P.Z. and H.H.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Volcanic Glass** & **Agglutinate** & **Ilmenite** & **Plagioclase** & **Pyroxene** & **Olivine** \\ \hline Correlation & 0.8617 & 0.9011 & 0.9331 & 0.7298 & 0.9790 & 0.7708 \\ Coefficient RMSE & 2.1364 & 4.8934 & 1.8143 & 1.4403 & 2.4985 & 0.9611 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of prediction accuracy of mineral content of lunar mare from the validation group.  
**Funding:** This research was funded by Major Projects of High-Resolution Earth Observation System of China (04-Y30B01-9001-18/20).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.