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使用卷积神经网络优化基于富里埃变换红外光谱的混合特征选择,对炸油中的3-单二-1,2-二醇进行定量分析

Xi Wang¹, Guijie Li², Jinjuan Yang³

¹State Key Laboratory of Marine Food Processing and Safety Control, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.

|December 21, 2025

概括

使用卷积神经网络 (CNN) 的新方法准确量化了油中的3-单二-1,2-二醇 (3-MCPD). 这种方法提供了一个强大的解决方案,用于监测3-MCPD,一个有毒的化合物,在食用油加工.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

3-单-1,2-二醇 (3-MCPD) 是精炼油中的有毒污染物,具有致癌性等健康风险.
准确量化3-MCPD对于食品安全和监管合规性至关重要.
现有的3MCPD检测方法在效率和准确性方面面临挑战.

研究的目的:

开发一种新的,高精度的方法来量化食用油中的3-MCPD.
将富里叶变换红外光谱法 (FTIR) 与卷积神经网络 (CNN) 集成,以改进检测.
为食用油行业建立一个强大的质量监测系统.

主要方法:

使用混合特征选择策略 (SiPLSR-IRIV) 减少了97%的光谱变量.
使用选定的光谱数据构建了一个卷积神经网络 (CNN) 模型.
福里埃变换红外光谱法 (FTIR) 用于光谱数据采集.

主要成果:

开发的CNN模型实现了0.9689.9的高确定系数 (R²_C).
该模型显示了0.0734.4的低根平均平方校准误差 (RMSEC).
检测和量化限制符合欧盟对油中的3-MCPD的监管标准.

结论:

与传统方法相比,混合CNN方法在3-MCPD量化方面提供了卓越的性能.
这种方法为食用油加工的质量控制提供了具有成本效益和效率的解决方案.
该研究为检测食品中的有毒化合物提供了一个强大的框架.

关键词:

3 - - 单二-1,2 - - 二醇卷积神经网络是一种卷积神经网络.选择功能选择功能选择.里埃变换红外光谱法红外光谱法油品样本油品样本

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