Quantification and classification of vegetable oils in extra virgin olive oil samples using a portable near-infrared spectrometer associated with chemometrics

2020 ◽  
Vol 159 ◽  
pp. 105544
Author(s):  
Flavia T. Borghi ◽  
Priscilla C. Santos ◽  
Francine D. Santos ◽  
Márcia H.C. Nascimento ◽  
Thayná Corrêa ◽  
...  
Keyword(s):  
Olive Oil ◽  
Vegetable Oils ◽  
Near Infrared ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer

scholarly journals Use of Mass Spectrometry with Electrospray Ionization and Exploratory Analysis for Classification of Extra Virgin Olive Oil Adulterated with Vegetable Oils

2015 ◽  
Vol 7 (6) ◽  
pp. 2180-2189 ◽  
Author(s):  
Hery Mitsutake ◽  
Lucas C. Gontijo ◽  
Felipe B. de Santana ◽  
Eloiza Guimarães ◽  
Lilian Lúcia da Rocha ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Olive Oil ◽  
Electrospray Ionization ◽  
Vegetable Oils ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Exploratory Analysis

Novel, Rapid Identification, and Quantification of Adulterants in Extra Virgin Olive Oil Using Near-Infrared Spectroscopy and Chemometrics

Lipids ◽  
2015 ◽  
Vol 50 (7) ◽  
pp. 705-718 ◽  
Author(s):  
Hormoz Azizian ◽  
Magdi M. Mossoba ◽  
Ali Reza Fardin-Kia ◽  
Pierluigi Delmonte ◽  
Sanjeewa R. Karunathilaka ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Olive Oil ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Rapid Identification ◽  
Identification And Quantification

Quantification of Lard in the Mixture with Olive Oil in Cream Cosmetics Based on FTIR Spectra and Chemometrics for Halal Authentication

2014 ◽  
Vol 69 (1) ◽  
Author(s):  
Abdul Rohman ◽  
Intan Gupitasari ◽  
Purwanto Purwanto ◽  
Kuwat Triyana ◽  
Arieff Salleh Rosman ◽  
...  
Keyword(s):  
Olive Oil ◽  
Ftir Spectroscopy ◽  
Multivariate Calibration ◽  
Virgin Olive Oil ◽  
Sampling Technique ◽  
Extra Virgin Olive Oil ◽  
Ftir Spectra ◽  
Halal Authentication ◽  
Extracting Solvent

The presence of lard (LD) in cosmetics products is a serious matter for certain religion, like Islam. The Muslim community is not allowed to use cosmetics products containing pig derivatives such as LD. Therefore, analysis of LD in cosmetics products is highly needed. The present study highlighted the employment of Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics of multivariate calibration and principle component analysis (PCA) for quantitative analysis and classification of LD in the binary mixture with extra virgin olive oil (EVOO) as oil base in cream formulations for halal authentication. The lipid component in cream was extracted using liquid-liquid extraction using hexane as extracting solvent, and the lipid obtained was subjected to FTIR spectra measurement, using horizontal attenuated total reflectance as sampling technique. The result showed that FTIR spectroscopy in combination with partial least squares can be used to quantify the levels of LD in the mixture with EVOO in cosmetics creams using the combined frequency regions of 1785-702 cm-1 and 3020-2808 cm-1. PCA using absorbance intensities at 1200 – 1000 cm-1 as variables has been successfully used for the classification of cream with and without LD in the formulation. The developed method is rapid and not involving the excessive sample preparation.

EXPRESS: Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils

2020 ◽  
pp. 000370282097470
Author(s):  
Joshua M. Ottaway ◽  
J. Chance Carter ◽  
Kristl L Adams ◽  
Joseph Camancho ◽  
Barry Lavine ◽  
...  
Keyword(s):  
Olive Oil ◽  
Peroxide Value ◽  
Near Infrared ◽  
Edible Oils ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Spectroscopic Techniques ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Calibration Models

The peroxide value (PV) of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine PVs using field portable and process instrumentation; those efforts presented ‘best-case’ scenarios with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique, or combination thereof, is best for predicting PVs. Following PV assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression (PLSR) calibration models to predict the PV of unknown oil samples. A global PV model based on near-infrared (8 mm optical path length – OPL) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm OPL near infrared (5.1), Raman (6.9) and 50 μm OPL mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global PV models based on low-level fusion of the NIR (8 and 24 mm OPL) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes.

Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis

2015 ◽  
Vol 7 (20) ◽  
pp. 8839-8846 ◽  
Author(s):  
Karla Danielle Tavares de Melo Milanez ◽  
Márcio José Coelho Pontes
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Soybean Oil ◽  
Olive Oil ◽  
Digital Images ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Pattern Recognition Methods

This work proposes a new methodology based on digital images and supervised pattern recognition methods for the classification of extra virgin olive oil (EVOO) samples with respect to brand (A, B and C) and verification of adulteration with soybean oil.

scholarly journals Non-Targeted Authentication Approach for Extra Virgin Olive Oil

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 221
Author(s):  
Didem Peren Aykas ◽  
Ayse Demet Karaman ◽  
Burcu Keser ◽  
Luis Rodriguez-Saona
Keyword(s):  
Olive Oil ◽  
Vegetable Oils ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Polar Compounds ◽  
Least Squares Regression ◽  
Excellent Correlation ◽  
Pattern Recognition Analysis ◽  
Targeted Approach ◽  
Independent Model

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 (n = 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.

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