## 4 Experimental Results and Analysis  
### Establishment of PLS Prediction Model for Mineral Content of Lunar Regolith  
In this paper, the cross-validity test method was used to select the number of principal components, that is, the data set was randomly divided into k parts, one of which was used as a test set each time, and the remaining k-1 parts were trained as a training set [26]. The relationship between the number of principal components and the cumulative variance contribution of corresponding Y and the sum of squares of predicted residuals was calculated, as shown in Figures 3 and 4. Regarding to lunar highland, as can be seen from Figure 3, the variance captured by the first principal component is only about 20% in the modeling of LSCC data of lunar highland. When the principal component number is 7, its cumulative variance contribution to Y has reached 85%, while the sum of squares of predicted residuals at this time is less than 1. It shows that the selection of the 7 principal components does not lose much information, reduces the data dimension and meets the modeling requirements. While regarding to lunar mare, as demonstrated by Figure 4, we can find that the variance captured by the first principal component is less than 10%, but the total value of variance of the first and second principal components is slightly more than 20%. The cumulative variance contribution to Y reached to more than 85% when summing the first  
Figure 4: The optimal number of principal components of lunar mare, (**a**) is the relationship between the number of principal components and the cumulative variance contribution of corresponding Y, (**b**) is the relationship between the number of principal components and the sum of squares of predicted residual. Figure 3: The optimal number of principal components of lunar highland, (**a**) is the relationship between the number of principal components and the cumulative variance contribution of corresponding Y, (**b**) is the relationship between the number of principal components and the sum of squares of predicted residual. 7 principal components, at the same time, the sum of squares of predicted residuals at this time is less than 1. This figure also shows that the first 7-principal-components does not lose much information, thus, the reducing of the data's dimension could meet the modeling requirements. As far as the mineral content retrieval of lunar highland, 20 randomly selected samples from 30 highland samples were used to model the spectra and mineral content of lunar regolith based on PLS method, as shown in Figure 5. The mineral contents of volcanic glass (Figure 5a), agglutinate (Figure 5b), ilmenite (Figure 5c), plagioclase (Figure 5d), pyroxene (Figure 5e), and olivine (Figure 5f) in the lunar regolith were optimized and retrieved, and compared with the measured mineral contents in the lunar regolith. Figure 5 shows that the PLS model has a good simulation effect. This calculation is done for all grain size range, it is combined from all grain sizes. There are a total of 4 particle size groups in LSCC data, of which 3 groups have measured mineral content, so we use these 3 groups of data. These 3 sets of data are calculated as a whole, and the total range is 0-45 \(\upmu\)m. The LSCC data is subdivided into four groups of \(<\)10 \(\upmu\)m, 10-20 \(\upmu\)m, 20-45 \(\upmu\)m, and \(<\)45 \(\upmu\)m according to the particle size level, a total of 76 samples. Among them, \(<\)10 \(\upmu\)m, 10-20 \(\upmu\)m, 20-45 \(\upmu\)m particle size group samples contain mineral abundance information. This paper selects a total of 57 sets of data in three particle size groups of \(<\)10 \(\upmu\)m, 10-20 \(\upmu\)m, and 20-45 \(\upmu\)m (30 sets of lunar highlands and 27 sets of lunar seas) as the research objects, and the overall range of mineral particle sizes is between 0-45 \(\upmu\)m. The mineral content of lunar regolith retrieved from highland samples can be linearly fitted with the measured mineral content of lunar regolith.