Literature DB >> 27677754

Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils.

Hormoz Azizian1, Magdi M Mossoba2, Ali Reza Fardin-Kia3, Sanjeewa R Karunathilaka3, John K G Kramer4.   

Abstract

It was previously demonstrated that Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares (PLS1) were successfully used to assess whether an olive oil was extra virgin, and if adulterated, with which type of vegetable oil and by how much using previously developed PLS1 calibration models. This last prediction required an initial set of four PLS1 calibration models that were based on gravimetrically prepared mixtures of a specific variety of extra virgin olive oil (EVOO) spiked with adulterants. The current study was undertaken after obtaining a range of EVOO varieties grown in different countries. It was found that all the different types of EVOO varieties investigated belonged to four distinct groups, and each required the development of additional sets of specific PLS1 calibration models to ensure that they can be used to predict low concentrations of vegetable oils high in linoleic, oleic, or palmitic acid, and/or refined olive oil. These four distinct sets of PLS1 calibration models were required to cover the range of EVOO varieties with a linoleic acid content from 1.3 to 15.5 % of total fatty acids. An FT-NIR library was established with 66 EVOO products obtained from California and Europe. The quality and/or purity of EVOO were assessed by determining the FT-NIR Index, a measure of the volatile content of EVOO. The use of these PLS1 calibration models made it possible to predict the authenticity of EVOO and the identity and quantity of potential adulterant oils in minutes.

Entities:  

Keywords:  Extra virgin olive oil; FA markers; FT-NIR; PLS; Volatile compounds

Mesh:

Substances:

Year:  2016        PMID: 27677754     DOI: 10.1007/s11745-016-4195-0

Source DB:  PubMed          Journal:  Lipids        ISSN: 0024-4201            Impact factor:   1.880


  2 in total

1.  A rapid method for the quantification of fatty acids in fats and oils with emphasis on trans fatty acids using Fourier Transform near infrared spectroscopy (FT-NIR).

Authors:  Hormoz Azizian; John K G Kramer
Journal:  Lipids       Date:  2005-08       Impact factor: 1.880

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

Authors:  Hormoz Azizian; Magdi M Mossoba; Ali Reza Fardin-Kia; Pierluigi Delmonte; Sanjeewa R Karunathilaka; John K G Kramer
Journal:  Lipids       Date:  2015-06-07       Impact factor: 1.880

  2 in total
  6 in total

1.  First Application of Newly Developed FT-NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA.

Authors:  Magdi M Mossoba; Hormoz Azizian; Ali Reza Fardin-Kia; Sanjeewa R Karunathilaka; John K G Kramer
Journal:  Lipids       Date:  2017-04-11       Impact factor: 1.880

2.  Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS-PLS Algorithm.

Authors:  Hui Jiang; Quansheng Chen
Journal:  Molecules       Date:  2019-06-06       Impact factor: 4.411

3.  Detection of olive oil adulteration with vegetable oils by ultra-performance convergence chromatography-quadrupole time-of-flight mass spectrometry (UPC2-QTOF MS) coupled with multivariate data analysis based on the differences of triacylglycerol compositions.

Authors:  Yinghua Luo; Boyan Gao; Yaqiong Zhang; Liangli Lucy Yu
Journal:  Food Sci Nutr       Date:  2020-05-25       Impact factor: 2.863

4.  Redundancy Analysis to Reduce the High-Dimensional Near-Infrared Spectral Information to Improve the Authentication of Olive Oil.

Authors:  María Isabel Sánchez-Rodríguez; Elena Sánchez-López; Alberto Marinas; José María Caridad; Francisco José Urbano
Journal:  J Chem Inf Model       Date:  2022-09-21       Impact factor: 6.162

5.  Near Infrared Spectroscopy as a Green Technology for the Quality Prediction of Intact Olives.

Authors:  Silvia Grassi; Olusola Samuel Jolayemi; Valentina Giovenzana; Alessio Tugnolo; Giacomo Squeo; Paola Conte; Alessandra De Bruno; Federica Flamminii; Ernestina Casiraghi; Cristina Alamprese
Journal:  Foods       Date:  2021-05-11

6.  Rapid Analytical Method to Characterize the Freshness of Olive Oils Using Fluorescence Spectroscopy and Chemometric Algorithms.

Authors:  Aimen El Orche; Mustapha Bouatia; Mohamed Mbarki
Journal:  J Anal Methods Chem       Date:  2020-07-11       Impact factor: 2.193

  6 in total

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