## 4 Conclusions  
In this study, the developed SERS substrate of ZIF-8@AuNPs demonstrated superior performance by incorporating the advantages of MOFs and AuNPs. Optimization studies, including the size selection of AuNPs and tuning of ZIF-8 quantity, underscored the delicate balance needed to achieve optimal SERS performance. Three different amino acids (Cys, Val, and Trp) were detected simultaneously using the ZIF-8@AuNPs composite substrate. The linear range of detection was from 10\({}^{-3}\) to 10\({}^{-1}\) M, with LODs of 2.40 \(\times\) 10\({}^{-4}\) M, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Amino** & **Detected** & **Spiked (M)** & **Found (M)** & **Recovery** & **RSD** \\  & **Acid** & **(M)** & & & **(\%)** & **(\%)** \\ \hline \multirow{8}{*}{millet} & \multirow{2}{*}{cys} & \multirow{4}{*}{Nd \({}^{\star}\)} & 5.00 \(\times\) 10\({}^{-2}\) & 4.80 \(\times\) 10\({}^{-2}\) & 96.0 & 8.13 \\  & & & 1.00 \(\times\) 10\({}^{-2}\) & 1.02 \(\times\) 10\({}^{-2}\) & 102.3 & 9.95 \\  & & & 5.00 \(\times\) 10\({}^{-3}\) & 5.18 \(\times\) 10\({}^{-3}\) & 98.7 & 7.82 \\  & & & 5.00 \(\times\) 10\({}^{-2}\) & 5.67 \(\times\) 10\({}^{-2}\) & 110.0 & 9.04 \\  & & & 1.00 \(\times\) 10\({}^{-2}\) & 1.12 \(\times\) 10\({}^{-2}\) & 95.2 & 7.38 \\  & & & 5.00 \(\times\) 10\({}^{-3}\) & 6.87 \(\times\) 10\({}^{-3}\) & 103.8 & 5.17 \\  & & & 5.00 \(\times\) 10\({}^{-2}\) & 4.92 \(\times\) 10\({}^{-2}\) & 98.4 & 6.01 \\  & & & 1.00 \(\times\) 10\({}^{-2}\) & 1.06 \(\times\) 10\({}^{-2}\) & 105.9 & 3.58 \\  & & & 5.00 \(\times\) 10\({}^{-3}\) & 5.22 \(\times\) 10\({}^{-3}\) & 104.4 & 2.86 \\ \hline \hline \end{tabular} \({}^{\star}\)Nd means not detected.  
\end{table} 
Table 1: Detection of Val, Cys, and Trp in Shaanbei Millet. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Amino** & **Detected** & **Spiked (M)** & **Found (M)** & **Recovery** & **RSD** \\  & **Acid** & **(M)** & & **(\%)** & **(\%)** & **(\%)** \\ \hline L-Cysteine & & & 5.00 \(\times\) 10\({}^{-2}\) & 6.16 \(\times\) 10\({}^{-2}\) & 97.0 & 4.66 \\ capsule & Cys & 1.31 \(\times\) 10\({}^{-2}\) & 1.00 \(\times\) 10\({}^{-2}\) & 2.36 \(\times\) 10\({}^{-2}\) & 105.3 & 2.80 \\  & & & 5.00 \(\times\) 10\({}^{-3}\) & 1.80 \(\times\) 10\({}^{-2}\) & 98.7 & 3.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection of L-Cysteine in healthcare products. 2.24 \(\times\) 10\({}^{-4}\) M, and 1.55 \(\times\) 10\({}^{-4}\) M, respectively. The developed 1D CNN model exhibited high accuracy and reliability for classifying amino acids in complex mixtures. The method was further validated through cross-analysis experiments, which provided consistent and satisfactory results. The application of the SERS substrate to real samples, such as Shaanbei Millet and healthcare products, demonstrated its practical utility with recoveries within reasonable ranges. This study contributes to the advancement of efficient amino acid detection methods and highlights the potential of combining SERS with advanced computational techniques for complex sample analysis. The developed approach opens new avenues for exploring the intricate components in various food analytical studies. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14052118/s1](https://www.mdpi.com/article/10.3390/app14052118/s1)]([https://www.mdpi.com/article/10.3390/app14052118/s1](https://www.mdpi.com/article/10.3390/app14052118/s1)). [PERSON] performed the experiments and data analysis and wrote the original draft; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] performed data analysis; [PERSON], [PERSON] and [PERSON] designed and supervised the project and revised manuscripts. All authors have read and agreed to the published version of the manuscript. The authors gratefully acknowledge the National Natural Science Foundation of China (NSFC) (Nos.