## 3 Results and Discussion
### Validation for Four Machine Learning Algorithms  
Four machine learning algorithms were evaluated by using a confusion matrix and overall accuracy, which are shown in Table 5 for all four machine learning algorithms and all training data sets.  
Figure 5: Confusion Matrices; (**a**) binary classification and (**b**) Multi-class classification.  
Analyses of the overall accuracies reveal that multi-class logistic regression and multi-class neural networks have superior accuracies versus other methods. Confusion matrices for the multi-class logic regression and multi-class neural network methods for all training datasets are shown in Figures 8-11.  
Figure 8 illustrates that the prediction models based on the multi-class logistic regression and the multi-class neural network appropriately classify the degree of thermal degradation. The true positives have relatively high probabilities in both algorithms. For the multi-class logistic regression, the probabilities of the predicted class are 89.5%, 5.3%, and 5.3% at 100 \({}^{\circ}\)C, 120 \({}^{\circ}\)C, and 140 \({}^{\circ}\)C, respectively, when the true class is 100 \({}^{\circ}\)C. For the multi-class neural network, the predicted probabilities are 94.7% and 5.3% at 100 \({}^{\circ}\)C and 140 \({}^{\circ}\)C, respectively, when the true class is 100 \({}^{\circ}\)C. Both algorithms show a high predictive capacity.  
Figure 6: Images of PLA specimens degraded by using the temperature-controlled storage test in which specimens were incubated at various temperatures for 24 h.  
Figure 7: Images of PLA specimens degraded under the time-controlled storage test, in which temperature was held constant at 160 \({}^{\circ}\)C and incubation time was varied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Data Set** & \begin{tabular}{c} **Multiclass** \\ **Decision Forest** \\ \end{tabular} & \begin{tabular}{c} **Multiclass** \\ **Decision Jungle** \\ \end{tabular} & \
\begin{tabular}{c} **Multiclass Logistic** \\ **Regression** \\ \end{tabular} &
\begin{tabular}{c} **Multi-Class** \\ **Neural Network** \\ \end{tabular} \\ \hline
1 st data set & 0.6875 & 0.6938 & 0.8875 & 0.8688 \\
2 nd data set & 0.3813 & 0.3375 & 0.4750 & 0.4063 \\
3 rd data set & 0.6069 & 0.5597 & 0.6604 & 0.6824 \\
4 th data set & 0.6972 & 0.6872 & 0.8882 & 0.8101 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall accuracies of the four machine learning algorithms.  
The confusion tables in Figure 9 reveal poor predictive capacities for both models trained using the second dataset. All four machine learning algorithms trained on the second dataset could not generate accurate predictive models.  
The size of the data in both the third and fourth datasets were reduced from 6949 to 806. For these datasets, the FTIR spectra were obtained by using both the desktop and the portable spectrometers. The confusion tables in Figure 10 show that the predictive models based on the third and fourth datasets properly classify the degree of thermal degradation. True positives are relatively higher than the others of FP and FN. These results indicate that more data is apparently needed to improve the accuracy of the prediction models for both machine learning algorithms trained on the third and fourth datasets.  
Figure 8: Confusion matrices for two machine learning algorithms trained on the 1 st data set.  
Figure 9: Confusion matrices for two machine learning algorithms trained on the 2 nd training data set.  
The confusion tables shown in Figure 11 reveal that models trained on the fourth dataset more accurately predict time-controlled storage testing behaviors than models trained on the third dataset (Figure 10). Reducing the size of the data in the FTIR spectra may improve the accuracy of the prediction models. The results show that data from two different types of spectrometers can be combined to predict the degree of thermal degradation. Additional FTIR spectral data are necessary to improve the accuracies of the models.