Clarifications of the Carbonyl and Water Absorptions in Fourier Transform Near Infrared Spectra from Extra Virgin Olive Oil

2021 ◽  
pp. 2000288
Author(s):  
Hormoz Azizian ◽  
Michael E. R. Dugan ◽  
John K.G. Kramer
Keyword(s):  
Fourier Transform ◽  
Olive Oil ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Near Infrared Spectra

FT-NIR the method of choice to verify the authenticity of extra virgin olive oils

NIR news ◽  
2017 ◽  
Vol 28 (4) ◽  
pp. 6-9 ◽  
Author(s):  
John KG Kramer ◽  
Hormoz Azizian
Keyword(s):  
Fourier Transform ◽  
Olive Oil ◽  
Near Infrared ◽  
Edible Oils ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Volatile Fraction ◽  
Olive Oils ◽  
Calibration Models ◽  
A Minor

Fourier transform near infrared spectroscopy was recently demonstrated to be an excellent method to evaluate the authenticity and adulteration of extra virgin olive oil. Since this method is matrix dependent, it takes a chemical fingerprint of all the components which sets it apart from the targeted methods. Careful examinations of the Fourier transform near infrared spectra lead to the identification of a minor carbonyl overtone absorption at 5269 cm−1 associated with the volatile fraction in extra virgin olive oil that appears to be a reliable indicator of authenticity. The same spectra were used to identify the fatty acids present in the oil using models based on comparison to accurate GC data. Gravimetric mixtures of extra virgin olive oil with refined edible oils were then prepared to develop PLS1 calibration models to identify possible adulterants and by how much. The great varietal difference in olive oils made it necessary to develop four unique sets of PLS1 calibration models for each extra virgin olive oil variety. As a result, an extra virgin olive oil acceptance specification was established.

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

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.

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

Near infrared Spectroscopy for discrimination between Extra Virgin Olive Oil and Lampante Olive Oil.

2015 ◽  
Author(s):  
Irina Torres Rodríguez ◽  
Maria-Teresa Sánchez ◽  
Maria-José de la Haba ◽  
DOLORES CATALINA PÉREZ MARÍN ◽  
JOSÉ EMILIO GUERRERO GINEL ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Olive Oil ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil

A predictive artificial neural network model as a simulator of the extra virgin olive oil elaboration process

2017 ◽  
Vol 25 (4) ◽  
pp. 278-285 ◽  
Author(s):  
Estrella Funes ◽  
Yosra Allouche ◽  
Gabriel Beltrán ◽  
M Paz Aguliera ◽  
Antonio Jiménez
Keyword(s):  
Olive Oil ◽  
Real Time ◽  
Near Infrared ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Prediction Errors ◽  
Process Variables ◽  
Virtual Plant ◽  
Infrared Spectral ◽  
Artificial Neural

Nine neural models were created to predict the characteristics of the extra virgin olive oil developed as a quality objective and by-products. These models are designed with the help of data of process variables from physical sensors such as temperature, flows, current intensity, etc. and physicochemical ones like the near infrared spectrum of the olive mass. The results obtained for the extractability of the process (fatty content and moisture) were highly significant correlations (r2≥0.90) and with similar prediction errors (root mean of squared error of prediction) relative to other analysis techniques which measure the by-product directly. For prediction the models gave correlations above 0.94, with the exception of ultraviolet absorption coefficients (0.72–0.84), with small prediction errors and the quality indicator relative error range with values above the optimal 10. The set of developed artificial neural networks models constitute the basis of the global ‘simulator’ tool of the extra virgin olive oil process. This simulator can perform a predictive optimization of the process to pre-adjust the process variables according to the goals marked in productivity or quality, from an near infrared spectral database or by real-time scanning. This simulator could be integrated into a control system that performs the function of a ‘virtual plant’ that allows the said system to adjust in real time the appropriate variables to meet the objectives.

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