Quantitative determination of peroxide value of edible oil by algorithm-assisted liquid interfacial surface enhanced Raman spectroscopy. (15th May 2021)

• Record Type: Journal Article
• Authors: Jiang, Yifan; Su, Mengke; Yu, Ting; Du, Shanshan; Liao, Lingling; Wang, Hongyan; Wu, Yiping; Liu, Honglin
• Is Part Of: Food chemistry. Volume 344(2021)
• Publication Date: 2021-05-15
• Subjects: Edible oil -- Lipid oxidation -- Surface-enhanced Raman spectrum -- PML-SERS -- Principal component analysis; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.128709
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A rapid accurate SERS analyzer for monitoring the quality of edible oil in storage. Direct observation of oil samples through liquid-liquid interface without pretreatment. Establishing the relationship between SERS fingerprints and the peroxide value (POV). Algorithm-assisted rapid spectral recognition for automatic discrimination of POV. Less than 10% deviation between the SERS quantitation and national standard method. Abstract: Edible oil is an indispensable food in daily life but early detection of its lipid oxidation is difficult. Developing new, rapid and accurate screening technique is urgently needed for oil quality control. Here we developed a surface-enhanced Raman spectroscopy analyzer based on plasmonic metal liquid-like platform (PML-SERS), which could directly analyze the oil sample in ca . 3 min. This analyzer has the ability and sensitivity to identify fingerprint peak changes. Moreover, the relative Raman intensity, I1265/1436, has a good correlation with peroxide value (POV), which is used for quantitative detection. The fitting model combined with principal component analysis (PCA) realized rapid spectral recognition for determining POV in edible oil oxidation. The relative deviation between the POV measured by PML-SERS and the national standard method (NSM) was less than 10%. Our platform provided a practical solution for ultra-sensitive and fast analysis of POV in oil oxidation.