posted on 2024-03-07, 10:13authored byXueqing Wang, Fan Li, Lan Wei, Yun Huang, Xiang Wen, Dongmei Wang, Guiguang Cheng, Ruijuan Zhao, Yechun Lin, Hui Yang, Meikun Fan
Accurate and rapid differentiation and authentication
of agricultural
products based on their origin and quality are crucial to ensuring
food safety and quality control. However, similar chemical compositions
and complex matrices often hinder precise identification, particularly
for adulterated samples. Herein, we propose a novel method combining
multiplex surface-enhanced Raman scattering (SERS) fingerprinting
with a one-dimensional convolutional neural network (1D-CNN), which
enables the effective differentiation of the category, origin, and
grade of agricultural products. This strategy leverages three different
SERS-active nanoparticles as multiplex sensors, each tailored to selectively
amplify the signals of preferentially adsorbed chemicals within the
sample. By strategically combining SERS spectra from different NPs,
a ‘SERS super-fingerprint’ is constructed, offering
a more comprehensive representation of the characteristic information
on agricultural products. Subsequently, utilizing a custom-designed
1D-CNN model for feature extraction from the ‘super-fingerprint’
significantly enhances the predictive accuracy for agricultural products.
This strategy successfully identified various agricultural products
and simulated adulterated samples with exceptional accuracy, reaching
97.7% and 94.8%, respectively. Notably, the entire identification
process, encompassing sample preparation, SERS measurement, and deep
learning analysis, takes only 35 min. This development of deep learning-assisted
multiplex SERS fingerprinting establishes a rapid and reliable method
for the identification and authentication of agricultural products.