## 3 Results and Discussion
### SERS Performance  
The three amino acids of Val, Cys, and Trp, can be quantitatively analyzed separately, which is the basis for quantitatively analyzing their mixture. Therefore, we first conducted separate quantitative analyses of the three amino acids, and the results are shown in Figure 6. The bending vibration of the C-H bond of Val at 1450 cm\({}^{-1}\), out-of-plane bending vibration of C-S-H bond of Cys at 679 cm\({}^{-1}\), and atomic stretching of the C-C bond of Trp at 757/cm\({}^{-1}\) were taken as the quantitative characteristic peaks. As shown in Figure 6, the linear range for all three amino acids was10\({}^{-1}\)-10\({}^{-3}\) M. The linear relationships were satisfactory with correlation coefficients (R\({}^{2}\)) of 0.9996 for Val, 0.9806 for Cys, and 0.9929 for Trp. LODs were calculated as 2.40 \(\times\) 10\({}^{-4}\) M, 2.24 \(\times\) 10\({}^{-4}\) M, 1.55 \(\times\) 10\({}^{-4}\) M for Cys, Val, and Trp, respectively. Error bars represent the variance of seven data points associated with each concentration-intensity pair. The consistent width of the error bars suggests the uniform stability of the SERS substrate. Additionally, SERS intensities of 13 random sites were analyzed to study the reproducibility. The results are shown in Figure S3, and the adequate relative standard deviation (RSD) ranged from 6.41% to 9.40%. In the context of composite samples, adherence to the principles of permutations and combinations dictates that the quantity of standard concentration data sets is contingent on the product of standard concentration data points for each amino acid. Here, the base denotes the number of standard concentration data points pertaining to each amino acid, and the exponent signifies the number of distinct amino acid types. In this study, three distinct  
Figure 6: SERS spectra of various concentrations of Val (**a**). The relationship between Val concentration and SERS intensity (**b**). SERS spectra of various concentrations of Cys (**c**). The relationship between Cys concentration and SERS intensity (**d**). SERS spectra of various concentrations of Trp (**e**). The relationship between Trp concentration and SERS intensity (**f**). amino acids were tested at discrete concentrations of \(10^{-1}\), \(10^{-2}\), and \(10^{-3}\) M, yielding 27 unique sample sets. The detailed concentration information is listed in Table S1. To mitigate the potential stochastic occurrences and systematic errors inherent in the instrumentation, a meticulous collection of 60 spectral data points was undertaken for each amalgamated sample set, culminating in the acquisition of 1620 spectral diagrams. All the characteristic SERS spectra of the 27 mixed samples are presented in Figure S4a-c. Discerning spectra of mixed samples with varying concentrations poses a challenge through conventional spectral analysis methods. Therefore, a strategy employing 1D CNN was introduced for the quantitative analysis of three distinct types of amino acids at various concentrations. After randomizing the data order, a training dataset comprising 70% of the total data (1120 spectra) and a validation dataset comprising 30% (486 spectra) were established. Meticulous analysis of the confusion matrix, delineating distinct amino acid types and concentrations (as shown in Figure 7), revealed sporadic misclassifications within the extensive dataset. This observation signifies the congruence between the predictive outputs of the model and the actual values. The computed recognition accuracy of the model was 97.35% coupled with a data recall rate of 98.75%, underscoring the robustness of the method. In contrast to traditional principal component analysis (PCA) [35], the proposed approach not only automates the extraction of spectral features but also facilitates the classification of large and complex nonlinear datasets.