## 3 Results and Discussion
### Identification of Penicillin Manufacturers  
In the manufacturer identification for each type of penicillin, the most important spectral lines were selected as the inputs of three classifiers, LDA, SVM and ANN. The number of selected characteristic spectral lines affects the classification accuracy of the model. Therefore, after ranking the spectral lines based on their importance from high to low, we selected different numbers of penicillin feature lines, from the top one to the first twenty-five, as input data. Figure 4 shows the relationship between the test set accuracies of the three models--RF-LDA, RF-SVM and RF-ANN--and the number of characteristic spectral lines when these models were established using the optimal parameters. We used a computer (model: Lenovo, R9000X 2021; CPU: AMD Ryzen 7 4800 H with Radeon  
Figure 3: Importance of each (**a**) PPT, (**b**) AC and (**c**) ACPT spectral line for manufacturer identification based on the decrease in the Gini index of RF. Graphics) to deal with this particular problem. When the top \(n\) feature lines were used as the inputs of the classifiers for modeling, the classification accuracies obtained and the test time of one spectrum were as listed in Table 3.  
Figure 4a shows the test set classification accuracies of the PPT manufacturer identification using the three models--RF-LDA, RF-SVM and RF-ANN--with different numbers of characteristic spectral lines. The classification accuracy of the RF-LDA model reached 100% with the top ten important characteristic spectral lines. When inputting the top nine important characteristic spectral lines and using the RF-SVM model (\(c\) = 0.1, \(g\) = 19.4909), the classification accuracy was 100%. The classification accuracy of the RF-ANN model (\(l\) = 4) was 100% when using the top two important characteristic spectral lines. The LIBS spectra of four different PPT manufacturers were correctly classified. The RF-ANN model provided an excellent classification result when using the top two important characteristic spectral lines. It can be seen from Figure 3a that the order of the important weights of  
Figure 4: Test set classification accuracies of RF-LDA, RF-SVM and RF-ANN models for the three types of penicillin samples ((**a**) PPT, (**b**) AC and (**c**) ACPT) with different numbers of characteristic spectral lines. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Model** & **Top \(n\) Feature Lines** & **Test Time (s)** & **CCR** \\ \hline \multirow{4}{*}{PPT} & LDA & 10 & 0.0017 & 100\% \\  & SVM & 9 & 0.0036 & 100\% \\  & ANN & 2 & 0.0049 & 100\% \\  & LDA & 4 & 0.0015 & 97.50\% \\ \multirow{2}{*}{AC} & SVM & 16 & 0.0047 & 98.33\% \\  & ANN & 6 & 0.0054 & 100\% \\ \multirow{2}{*}{ACPT} & LDA & 23 & 0.0021 & 99.17\% \\  & SVM & 15 & 0.0045 & 100\% \\ \multirow{2}{*}{ACPT} & ANN & 4 & 0.0051 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The best classification accuracy and corresponding analysis time of each model. the first two important characteristic variables is C\({}_{2}\) (497.7-505.9 nm) > Na (588.9 nm). C\({}_{2}\) emission was principally fragmented directly from the samples. The molecular structure of PPT contained some carbon-carbon bonds (C-C) and carbon-carbon double bonds (C=C). After the high-energy laser interacted with the samples, some molecular fragments were excited, and C\({}_{2}\) mainly came from these molecular fragments [44]. The element of Na came from the sodium citrate (C\({}_{6}\)H\({}_{5}\)Na\({}_{3}\)O\({}_{7}\)), which could be used as an antioxidant, in the prescription of PPT [45]. For AC manufacturer identification, Figure 4b shows that, when inputting the top four important characteristic spectral lines and using RF-LDA to build the model, the classification accuracy reached 97.50%. Three LIBS spectra of AC3 were misclassified into AC2. It can also be seen from Figure 2 that the LIBS spectrum of AC3 was more similar to that of AC2 compared to the spectra of AC1 and AC4.