Simultaneous SERS Detection of Multiple Amino Acids Using ZIF-8@AuNPs as Substrate: Classified with 1D Convolutional Neural Network

Abstract

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 nondestructive, 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 limits of detection (LODs) of 2.40 × 10−4 M, 2.24 × 10−4 M, and 1.55 × 10−4 M, respectively. Same linear ranges and LODs were achieved with a 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.