使用算法辅助的表面增强拉曼光谱仪监测油的脂氧化和脂肪酸概况

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Tanya Nagpal¹, Vikas Yadav², Sunil K Khare³

¹Nanoscopic Imaging and Sensing Lab, Department of Chemistry, Indian Institute of Technology Delhi, New Delhi 110 016, India; Food Customization and Research Lab, Centre for Rural Development and Technology, Indian Institute of Technology Delhi, New Delhi 110 016, India; Enzyme and Microbial Biochemistry Lab, Department of Chemistry, Indian Institute of Technology Delhi, New Delhi 110 016, India.

|July 8, 2023

概括

这项研究引入了一种快速,无标签的方法,使用表面增强拉曼光谱 (SERS) 和人工神经网络 (ANN) 来评估油质. 该技术精确检测脂质氧化和脂肪酸概况,确保食品安全.

科学领域:

分析化学分析化学
食品科学与技术食品科学与技术
频谱学是一种光谱学.

背景情况:

深油炸产生脂质氧化产物,降低油质,并对健康构成风险.
目前的石油分析方法通常很慢,需要实验室设置.
需要快速,准确和现场油品质和安全检测技术.

研究的目的:

开发一种快速,无标签的方法,以在现场确定油的过氧化物值 (PV) 和脂肪酸成分.
为了利用表面增强的拉曼光谱法 (SERS) 和先进的化学测量技术进行石油分析.
建立一个有效的系统来监测油氧化,并确保食品安全.

主要方法:

采用基于等离子调节的,生物相容的Ag@Au核心外纳米颗粒的SERS基板来增强信号检测.
将复杂的化学测量技术,特别是人工神经网络 (ANN) 分析,应用于SERS数据.
在现场进行油品质量参数的无标签确定.

主要成果:

联合SERS和ANN方法在确定脂肪酸概况和PV时达到高达99%的准确性.
该SERS-ANN方法准确量化了低水平的反式脂肪 (<2%),准确度为97%.
开发的系统证明了尽管存在矩阵干扰,但可以有效检测油成分.

结论:

算法辅助的SERS系统为现场油氧化监测提供了光滑快速的解决方案.
这种技术为确保食用油的质量和安全提供了一个有前途的方法.
这项研究强调了SERS与化学测量相结合的潜力,用于实时食品分析.

关键词:

人工神经网络的人工神经网络可食用的食用油.脂质氧化氧化方式主要组件分析的主要组件分析.表面增强的拉曼光谱学

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