## 3 Results and Discussion
### Rule Nodes: Subdivide the Four Remaining Classes by Three Increasingly Specific PLS-DA Classifiers  
All three PLS-DA classifiers used here were binary; for example, the first PLS-DA classifier denoted PLS-DA 1 could only separate members of the MS2 class from those of a general category consisting of MS3, MS4, and MS5. Likewise, the second PLS-DA classifier denoted PLS-DA 2 separated the MS3 class from the remaining MS4 and MS5 class. Finally, the last PLS-DA classifier denoted PLS-DA 3 separated the MS4 class from the MS5 class. The same preprocessing method (the first Savitzky-Golay derivative (second polynomial with a filter width of 15) followed by mean centering) was used to build these three PLS-DA classifiers. The configurations used in the cross-validation of the Venetian blinds method were 10 splits, featuring one sample per split. The correlated optimal LVs were selected based on the value of the RMSECV. The confusion matrices of the training and test samples in the three PLS-DA models are shown in Tables 3 and 4, respectively. In terms of the results shown in Tables 3 and 4, the performances of the first and last PLS-DA classifiers in both the training and test phases were perfect; all the samples were correctly classified in both the training and test phases with the anti-diagonal elements all equal to zero. Nevertheless, the performance of the second PLS-DA classifier was inferior to that of the others. As shown in Table 3, in the training phase, it had an FN value of 2, indicating that 2 of the 61 samples in the MS3 class were incorrectly classified as belonging to the general category consisting of the MS4 and MS5 classes. The FP value of 1 showed that 1 of the 126 samples in the general category consisting of MS4 and MS5 classes were incorrectly assigned to the MS3 class. Furthermore, this problem was a little bit more serious in the test phase with a large FP value of 3 in Table 4; actually, the 48 th and 85 th samples from the MS4 class and the 119 th sample from the MS5 class were incorrectly identified as belonging to the MS3 class. In addition, noticing that FN value was 1, it indicated that the 117 th sample from the MS3 class had been misclassified as belonging to the MS4 class. In the training phase, three PLS-DA models have achieved classification accuracy rates of 100%, 98.4%, and 100%, respectively. As for the testing phase, the corresponding classification accuracy rates are 100%, 97.8%, and 100%. All these three PLS-DA models have achieved very nice predictive results on correlated sample classes in general, but it is still unclear which variables are responsible for these good performances. VIP scores can estimate the importance of each variable in the projection used in a PLS-DA model. Specifically, a variable with a VIP score close to or greater than one (significance threshold) can be considered important in the given model. The VIP scores of three PLS-DA models regarding specific classes are shown in Figure 9.  
In terms of the bands annotated in Figure 9, only a few of them have been publicly reported as meaningful in bands assignment [35]. Firstly, absorption bands associated with protein as amides include a peak at 1688 nm explained by one functional group of CONH\({}_{2}\) specifically due to peptide sheet structures; Secondly, it is reported that poly(ether urethane urea) has a band assignment at 1690 nm, which is due to the C-H aromatic first overtone. Finally, a second overtone of the asymmetric bending mode in the methyl functional group is expected to be at 2270 nm. It could be found that the 1600-1800 nm NIR region both contributed a lot to the VIP scores of all three PLS-DA models and did have a solid basis in band assignment.