Simultaneous Sers Detection of Multiple Amino Acids Using Zif-8@Aunps as Substrate: Classified with 1d Convolutional Neural Network

Amino acids found in minor coarse cereals are essential for human growth and development and play a crucial role in efficient and rapid quantitative detection. Surface-enhanced Raman spectroscopy (SERS) enables non-destructive, efficient, and rapid sample detection. Traditional SERS detection efficiency is constrained by the use of a single target. In this study, three different amino acids (cysteine, valine, and tryptophan) were detected simultaneously using a ZIF -8@AuNPs composite substrate. The linear range of detection was 10-3 to 10-1 M, with a limit of detection (LOD) of 2.40 ´ 10-4 M, 2.24 ´ 10-4 M, and 1.55 ´ 10-4 M, respectively. Same linear range and LODs were achieved with one-dimensional convolutional neural network method. Furthermore, this substrate enabled the effective detection of amino acids in millet and efficient detection of cysteine in health products. This study presents a novel method for simultaneous detection of multiple analytes.