Direct Discrimination of Edible Oil Type, Oxidation, and Adulteration by Liquid Interfacial Surface-Enhanced Raman Spectroscopy.

Abstract

The quality and safety of edible oils is a momentous but formidable challenge, especially regarding identification of oil type, oxidation, and adulteration. Most conventional analytical methods have bottlenecks in sensitivity, specificity, accessibility, or reliability. Surface-enhanced Raman spectroscopy (SERS) is promising as an unlabeled and ultrasensitive technique but limited by modification of inducers or surfactants on metal surfaces for oil analysis. Here, we develop a quantitative SERS analyzer on two-liquid interfacial plasmonic arrays for direct quality classification of edible oils by a portable Raman device. The interfacial plasmonic array is self-assembled through mixing the gold nanoparticle (GNP) sols and oil sample dissolved in chloroform without any surfactants or pretreatments. Different kinds of edible oils dissolved in chloroform directly participate in self-assembly of plasmonic arrays that finally localizes onto a three-dimensional (3D) oil/water interface. The 3D plasmonic array is self-healing, shape adaptive, and can be transferred to any glass containers as a substrate-free SERS analyzer for direct Raman measurements. It produces sensitive responses of SERS on different kinds of edible oils. By virtue of principal component analysis (PCA), this analyzer is able to quickly distinguish six edible oils, oxidized oils, and adulterated oils. Moreover, the solvent chloroform generates unique and stable SERS bands that can utilized as an inherent internal standard (IIS) to calibrate SERS fluctuation and greatly improve quantitation accuracy. Compared to conventional lab methods, this analyzer avoids complex and time-consuming preprocessing and provides significant advantages in cost, speed, and utility. Our study illuminates a facile way to determine edible oil quality and promises great potential in food quality and safety analysis.