Aykas Didem Peren, Karaman Ayse Demet, Keser Burcu, Rodriguez-Saona Luis
Department of Food Science and Technology, The Ohio State University, 100 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USA.
Department of Food Engineering, Faculty of Engineering, Adnan Menderes University, Aydin 09100, Turkey.
Foods. 2020 Feb 20;9(2):221. doi: 10.3390/foods9020221.
The aim of this study is to develop a non-targeted approach for the authentication of extra virgin olive oil (EVOO) using vibrational spectroscopy signatures combined with pattern recognition analysis. Olive oil samples ( = 151) were grouped as EVOO, virgin olive oil (VOO)/olive oil (OO), and EVOO adulterated with vegetable oils. Spectral data was collected using a compact benchtop Raman (1064 nm) and a portable ATR-IR (5-reflections) units. Oils were characterized by their fatty acid profile, free fatty acids (FFA), peroxide value (PV), pyropheophytins (PPP), and total polar compounds (TPC) through the official methods. The soft independent model of class analogy analysis using ATR-IR spectra showed excellent sensitivity (100%) and specificity (89%) for detection of EVOO. Both techniques identified EVOO adulteration with vegetable oils, but Raman showed limited resolution detecting VOO/OO tampering. Partial least squares regression models showed excellent correlation (Rval ≥ 0.92) with reference tests and standard errors of prediction that would allow for quality control applications.
本研究的目的是开发一种非靶向方法,利用振动光谱特征结合模式识别分析来鉴定特级初榨橄榄油(EVOO)。将151个橄榄油样品分为特级初榨橄榄油、初榨橄榄油(VOO)/橄榄油(OO)以及掺有植物油的特级初榨橄榄油。使用紧凑型台式拉曼光谱仪(1064纳米)和便携式衰减全反射红外光谱仪(5次反射)收集光谱数据。通过官方方法对油类进行脂肪酸组成、游离脂肪酸(FFA)、过氧化值(PV)毛脱镁叶绿素(PPP)和总极性化合物(TPC)的表征。使用衰减全反射红外光谱的类类比分析软独立模型对特级初榨橄榄油的检测显示出优异的灵敏度(100%)和特异性(89%)。两种技术都能识别出特级初榨橄榄油掺有植物油的情况,但拉曼光谱在检测初榨橄榄油/橄榄油掺假方面分辨率有限。偏最小二乘回归模型与参考测试显示出极好的相关性(Rval≥0.92)以及预测标准误差,可用于质量控制应用。