## 3 Results and Discussion
### The Exploratory Analysis  
To determine whether the raw datasets contain sufficient information to allow us to distinguish the groups, an exploratory analysis was performed in this study. All 656 instances in the dataset  
Figure 3: The second-derivative SK-NIRS spectra in all regions (**b**) and the zoomed-in 1600–1800 nm region (**a**).  
were subjected to exploratory data analysis in a PCA model at a confidence level of 95%. PCA is generally applied to data that has been \"mean-centered\" by subtracting off the original mean value of each column. In addition to this basic preprocessing method, derivatives as a common method to remove unimportant baseline signals from samples combined with the mean centering are also used as a contrast. Figure 4 shows the two different preprocessed data viewed by classes; one is using the preprocessing method of mean centering (a), the other instead firstly uses the Savitzky-Golay first derivative (second polynomial with a filter width of 15), and then goes through the mean centering method and finally shows in (b). It could be seen from Figure 4 that both methods were able to distinguish samples of five classes at most bands, but the latter one uniquely had one advantage: by taking the first derivative, it had removed the predominant background variation from the original data such as bands between 1000 and 1150 nm.  
Figure 5 presents scores plots showing the instances viewed by classes projected on the top two principal components of the relative PCA model.  
Figure 4: Preprocessed SK-NIRS spectra by only mean centering (**a**) and first derivative before mean centering (**b**).  
Not any clear independent cluster could be found on the scores subplot (a); different classes just highly overlapped together. This can also be inferred from Figure 4a, the preprocessed data by the method of the mean centering does not have sufficient information that could be used to exhibit the differences between classes. In contrast, lots of absorption features existed in samples of five classes can be found in Figure 4b: the 1600-1800 nm region, 1900 nm, etc. In addition, three obvious groups can be identified from the scores subplot (b). That is, samples in the MS3, MS4, and MS5 classes overlapped and formed a cluster, while samples in the MS2 class and those in the MS1 class formed independent clusters. To figure out which correlative variables are responsible for the scores subplot (b), Figure 6 shows the loadings plot on the top two principal components of the relative PCA model.  
Figure 5: Scores plot of principal component analysis (PCA) model preprocessed by only mean centering (**a**) and the first derivative mean centering (**b**).  
From the relative scores subplot in Figure 5b, it could be found that samples of MS3, MS4, and MS5 have positive correlations with the first principal component (PC 1). Moreover, the wavelengths where samples of MS3, MS4, and MS5 classes are distinguished from those of MS1 and MS2 in Figure 4b are almost the same as those where PC1 is different from PC2 in Figure 6. It indicates that this preprocessing method helps reveal the hidden distinctions of different maturation classes. So, it is reasonable to use this preprocessing method in the following classification models.  
Another great reward that this explorative analysis can bring is to provide an intuition that helps build the tree-structured hierarchical classification scheme. Taking an overview of the group structure, the relationship shown in the scores subplot in Figure 5b, a PCA submodel of SIMCA could be constructed to separate the MS1 class from the rest of the samples using sample projections on the Q statistic of this submodel. For the remained four unassigned classes, it may be appropriate to successively assign one class upon one binary classifier, and it seems that PLS-DA is a good candidate.