## 1 Introduction  
Amino acids are a class of organic compounds containing amino and carboxyl groups that constitute the basic structural unit of protein and play an irreplaceable role in the human body. For example, tryptophan can promote sleep and relieve tension and anxiety [1]. A lack of sufficient amino acids or an insufficient intake of amino acids can lead to various serious diseases that can affect memory, growth, and development. Therefore, it is of utmost importance to determine the levels of certain amino acids in various foods [2, 3, 4]. Commonly used methods for detecting amino acid content are typically time-consuming and require a significant number of detection standards [5, 6, 7]. However, handling an increasing number of samples is difficult. In addition, the detection costs are relatively high [8]. Therefore, there is an urgent need to develop a more efficient and cost-effective detection method to meet the demand.  
Surface-enhanced Raman spectroscopy (SERS) is an efficient and rapid detection technique. The analyte signals can be quickly obtained by adsorbing the analyte onto a prepared enhanced substrate surface [9]. By utilizing the fingerprint information of the analyte, detection results can be obtained without consuming more standard reagents [10].  
The characteristic peaks of a target substance are usually a set of Raman peaks. The vibrations of multiple target substances can influence each other, and the fingerprint peaksare highly complex, making them difficult to analyze [11]. Therefore, conventional surface-enhanced Raman spectroscopy typically detects a single target substance simultaneously. Numerous amino acids are involved in their detection. The advantages of using conventional SERS methods [12; 13] for improving the detection efficiency are not significant. Therefore, we used a one-dimensional convolutional neural network (1D CNN) [14] to simultaneously classify complex Raman signals from multiple amino acids to identify the concentration patterns of amino acids within the Raman peaks [15]. Several achievements have been obtained in the field of Raman spectroscopy analysis by using [16; 17; 18] 1D CNN.  
Conventional single-labelled surface-enhanced Raman substrates are typically designed to adsorb only one type of substrate. Typically, the substrate surface contains only one adsorption moiety [19]. However, multiple amino acids must be adsorbed simultaneously. Therefore, the designed substrate should simultaneously provide multiple adsorption sites. Metal-organic framework materials (MOFs) possess multiple exposed active sites on their surface [20], which promote the amplification of hotspots. The infinite, repetitive, periodic unit structure of MOFs enables homogeneous substrate preparation. In addition, MOFs exhibit characteristics such as a large specific surface area, adjustable pore size, and strong adsorption [21]. However, MOFs alone only have weak enhancement effects, and do not even exhibit significant enhancement effects. Therefore, a composite substrate was prepared by combining MOFs with precious metals, enabling the superior performance of MOFs, and achieving efficient SERS enhancement [22; 23].  
In this study, ZIF-8, one of the most commonly used MOFs, was used as a template to synthesize a novel SERS substrate via modification with AuNPs (ZIF-8@AuNPs). ZIF-8 is a representative MOF known for its large specific surface area and high stability [24], which simultaneously provide large and virous adsorption sites [25]. The AuNPs formed enhanced hotspots [26], enabling the effective detection of the Raman signals of amino acids. Subsequently, they were classified using the 1D CNN method [27]. The ZIF-8@AuNPs composite substrate significantly enhances the simultaneous detection efficiency of cysteine, valine, and tryptophan, surpassing the constraints of traditional SERS methods.