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通过深度学习辅助的多重SERS指纹,快速准确地区分和认证农产品的真实性

Xueqing Wang¹, Fan Li¹, Lan Wei¹

¹Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China.

Analytical chemistry

|March 7, 2024

概括

本研究介绍了一种使用多重表面增强拉曼散射 (SERS) 和深度学习来验证农产品真实性的快速方法. 该技术准确识别产品类别,原产地和等级,确保食品安全.

科学领域:

分析化学分析化学
食品科学食品科学食品科学
频谱学是一种光谱学.

背景情况:

准确的农产品认证对于食品安全和质量控制至关重要.
差异化方面的挑战来自相似的化学成分和复杂的样本矩阵,特别是假冒产品.

研究的目的:

开发一种新的,快速和可靠的方法,根据类别,原产地和等级区分农产品.
提高农产品的识别准确性,包括伪造的样本.

主要方法:

结合多重表面增强拉曼散射 (SERS) 指纹与一维卷积神经网络 (1D-CNN) 的指纹.
使用了三种不同的SERS活性纳米粒子来创建一个"SERS超级指纹"以获得全面的特征信息.
采用定制设计的1D-CNN模型用于特征提取和预测分析.

主要成果:

在识别农产品 (97.7%) 和模拟伪造样本 (94.8%) 中实现了高准确度.
从样本准备到深度学习分析的总过程时间仅为35分钟.
"SERS超级指纹"方法提供了更全面的样本特征表示.

结论:

深度学习辅助的多重SERS指纹识别为农产品识别和认证提供了快速可靠的解决方案.
这种方法有效地解决了复杂矩阵和类似化学成分所带来的挑战.
开发的战略显著改善了食品安全和质量控制措施.

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