## 4 Experimental Results and Analysis
### Validation of PLS Prediction Model for Mineral Content of Lunar Regolith  
In this paper, PLS regression relationship between the lunar soil spectra and mineral content were established for lunar soil samples from the lunar highlands and the lunar sea. Two-thirds of samples were randomly selected as the experimental group to establish the prediction relationship, while the remaining one-third of the samples were used as the verification group to further verify the prediction relationship.  
Based on the PLS equation of 20 randomly selected samples of lunar highland, the mineral contents of volcanic glass (Figure 7a), agglutinate (Figure 7b), ilmenite (Figure 7c), plagioclase (Figure 7d), pyroxene (Figure 7e), and olivine (Figure 7f) in the remaining 10 samples of lunar highland were predicted, and compared with the measured mineral contents in the lunar regolith, as shown in Figure 7.  
Figure 7 shows that the PLS model has a good simulation effect. The mineral content of lunar regolith retrieved from highland samples can be linearly fitted with the measured mineral content of lunar regolith. The predicted and measured values of six major minerals of lunar regolith are evenly distributed along the y = x line.  
Table 3 gives the root mean square error (RMSE) and correlation coefficient of the predicted and measured content of the six major minerals in lunar highland, and evaluates the accuracy of the PLS model. The largest correlation coefficients between predicted and measured contents of the six major mineral is 0.9823 for plagioclase, and the smallest is 0.7010 for olivine. The smallest RMSE is 0.9660 for olivine and the largest is 6.2112 for agglutinate. The overall model has a good simulation effect. Based on the PLS method and the measured spectra data, the inversion of mineral content of lunar highland can be realized.  
Based on the PLS equation of 18 randomly selected samples of lunar mare, the mineral contents of plagioclase, pyroxene, olivine, ilmenite, agglutinate and volcanic glass in the remaining 10 samples of lunar mare were predicted, and compared with the measured mineral contents in the lunar regolith, as shown in Figure 8.  
Figure 8 shows that the PLS model has a good simulation effect. The mineral content of lunar regolith retrieved from mare samples can be linearly fitted with the measured mineral content of lunar regolith.  
Figure 8: (**a–f**) are respectively the prediction of mineral content of volcanic glass, agglutinate, ilmenite, plagioclase, pyroxene, and olivine of lunar mare from the validation group and their comparative analysis with the measured mineral contents.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Volcanic Glass** & **Agglutinate** & **Ilmenite** & **Plagioclase** & **Pyoxene** & **Olivine** \\ \hline Correlation & 0.8944 & 0.9390 & 0.7744 & 0.9823 & 0.8586 & 0.7010 \\ Coefficient RMSE & 1.6070 & 6.2112 & 0.3597 & 4.2077 & 4.1708 & 0.9660 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of prediction accuracy of mineral content of lunar highland from the validation group.  
mineral content of lunar regolith. The predicted and measured values of six major minerals of lunar regolith are evenly distributed along the y = x line.  
Table 4 gives the root mean square error (RMSE) and correlation coefficient of the predicted and measured content of the six major minerals in lunar mare, and evaluates the accuracy of the PLS model. The largest correlation coefficients between predicted and measured contents of the six major mineral is 0.9790 for pyroxene, and the smallest is 0.7298 for plagioclase. The smallest RMSE is 0.9611 for olivine and the largest is 4.8934 for agglutinate. The overall model has a good simulation effect. Based on the PLS method and the measured spectra data, the inversion of mineral content of lunar mare can be realized.