## 3 Results
### Identifying Glass Type and Weathering  
The previous sect0 ion investigated the chemical components that are sensitive to weathering in the two types of glass and estimated the content of each component prior to weathering. However, for an unidentified glass sample, the type of the glass was unknown and needed to be classified in advance. The logistic regression model was used to extract the most influential chemical components affecting the classification of high-potassium and lead-barium glass artifacts (Figure 6). The results showed that the identified types were completely consistent with the factual types for all ten testing samples. Therefore, the impact weights of each chemical component in the glass types could be determined using the coefficients in the regression model [33]. The regression coefficients of each chemical component used in the classification of high-potassium and lead-barium glass artifacts are shown in Table 3. If the regression coefficient was positive, this indicated that with an increase in the chemical component content, the higher the likelihood of a glass artifact being classified as a high-potassium type. Conversely, a negative regression coefficient suggested that the higher chemical component content, the greater the probability of the artifact being categorized as a lead-barium type. Comparing the absolute values of the regression coefficients of the various chemical components, it can be seen that the PbO, K\({}_{2}\)O, BaO, SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and P\({}_{2}\)  
Figure 5: The predicated chemical component proportion in high-potassium and lead–barium glass before weathering. Figure 6: Results of the logistic regression model test. have significantly higher values, offering critical contributions to the different glass types. Furthermore, these six chemical components are crucial variables for identifying glass weathering status as mentioned above in Section 3.2. Therefore, the main classes of high-potassium and lead-barium and the sub classes of weathered and non-weathered could be judged accordingly using these six chemical components. Both BP neural network and random forest models were employed to accurately identify the main and sub classes of the glass based on chemical components (Figure 7). The total 52 glass samples were initialized and divided into training and testing datasets of 7:3. The input variables were the six chemical components and outputs were the glass types of weathered high-potassium (HP\({}_{\text{w}}\)), non-weathered high-potassium (HP\({}_{\text{nw}}\)), weathered lead-barium (LB\({}_{\text{w}}\)), and non-weathered lead-barium (LB\({}_{\text{nw}}\)). Overall, the neural network model showed an overall identification accuracy rate of 81.25%, with three incorrectly identified samples (the first, fifth, and twelfth) in the weathering status of the lead-barium glass. In contrast, the random forest model achieved a 100% identification accuracy rate for all of the tested samples. To mitigate the influence of random sampling on the accuracy evaluation of the models, ten rounds of random sampling were conducted from the 52 samples. After each random sample partition, 36 samples were allocated to the training set and 16 to the testing set, followed by model training and testing. Following ten rounds of random sampling and testing, the results from these ten rounds were aggregated for accuracy analysis. Confusion matrix plots of the identification results were presented in Figure 8.  
In Figure 8, the blue boxes denote the correct rates of identification for each sub class, while the orange boxes represent the identification error rates. The sum of the values in each row indicates the total number of samples in that category. Then results showed that both the neural network and random forest models could effectively and accurately identify the main classes (high-potassium and lead-barium). The main identification errors for both models were found in the sub classes (weathering and non-weathering) of lead-barium.