Monitoring the lipid oxidation and fatty acid profile of oil using algorithm-assisted surface-enhanced Raman spectroscopy

IF 8.5 1区农林科学 Q1 CHEMISTRY, APPLIED

Food Chemistry Pub Date : 2023-06-26 DOI:10.1016/j.foodchem.2023.136746

Tanya Nagpal , Vikas Yadav , Sunil K. Khare , Soumik Siddhanta , Jatindra K. Sahu

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引用次数: 0

Abstract

Deep-fat frying of food develops lipid oxidation products that deteriorate oil and pose a health risk. This necessitates the development of a rapid and accurate oil quality and safety detection technique. Herein, surface-enhanced Raman spectroscopy (SERS) and sophisticated chemometric techniques were used for rapid and label-free determination of peroxide value (PV) and fatty acid composition of oil in-situ. In the study, plasmon-tuned and biocompatible Ag@Au core–shell nanoparticle-based SERS substrates were used to obtain optimum enhancement despite matrix interference to efficiently detect the oil components. The potent combination of SERS and the Artificial Neural Network (ANN) method could determine the fatty acid profile and PV with upto 99% accuracy. Moreover, the SERS-ANN method could quantify the low level of trans fats, i.e., < 2%, with 97% accuracy. Therefore, the developed algorithm-assisted SERS system enabled the sleek and rapid monitoring and on-site detection of oil oxidation.

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使用算法辅助表面增强拉曼光谱监测油的脂质氧化和脂肪酸谱

油炸食品会产生油脂氧化产物，使油脂变质，对健康构成威胁。这就要求开发一种快速、准确的油品质量安全检测技术。本文采用表面增强拉曼光谱(SERS)和复杂的化学计量技术，对油品的过氧化值(PV)和脂肪酸组成进行了快速、无标记的原位测定。在这项研究中，利用等离子体调谐和生物相容性Ag@Au核壳纳米颗粒基SERS底物，在基质干扰的情况下获得最佳增强，以有效检测油成分。SERS和人工神经网络(ANN)方法的有效结合可以测定脂肪酸谱和PV，准确率高达99%。此外，SERS-ANN方法可以量化低水平的反式脂肪，即<2%，准确率为97%。因此，所开发的算法辅助SERS系统可以方便、快速地监测和现场检测油氧化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Food Chemistry 工程技术-食品科技

CiteScore

16.30

自引率

10.20%

发文量

3130

审稿时长

122 days

期刊介绍： Food Chemistry publishes original research papers dealing with the advancement of the chemistry and biochemistry of foods or the analytical methods/ approach used. All papers should focus on the novelty of the research carried out.