化学
多路复用
认证(法律)
人工智能
指纹(计算)
鉴定(生物学)
模式识别(心理学)
卷积神经网络
深度学习
纳米技术
计算机安全
生物信息学
材料科学
计算机科学
生物
植物
作者
Xueqing Wang,Fan Li,Lan Wei,Yun Huang,Wenguo Xiang,Dongmei Wang,Guiguang Cheng,Ruijuan Zhao,Yechun Lin,Hui Ying Yang,Meikun Fan
标识
DOI:10.1021/acs.analchem.4c00064
摘要
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.
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