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

Abstract

A new, rapid Fourier transform near infrared (FT-NIR) spectroscopic procedure is described to screen for the authenticity of extra virgin olive oils (EVOO) and to determine the kind and amount of an adulterant in EVOO. To screen EVOO, a partial least squares (PLS1) calibration model was developed to estimate a newly created FT-NIR index based mainly on the relative intensities of two unique carbonyl overtone absorptions in the FT-NIR spectra of EVOO and other mixtures attributed to volatile (5280 cm(-1)) and non-volatile (5180 cm(-1)) components. Spectra were also used to predict the fatty acid (FA) composition of EVOO or samples spiked with an adulterant using previously developed PLS1 calibration models. Some adulterated mixtures could be identified provided the FA profile was sufficiently different from those of EVOO. To identify the type and determine the quantity of an adulterant, gravimetric mixtures were prepared by spiking EVOO with different concentrations of each adulterant. Based on FT-NIR spectra, four PLS1 calibration models were developed for four specific groups of adulterants, each with a characteristic FA composition. Using these different PLS1 calibration models for prediction, plots of predicted vs. gravimetric concentrations of an adulterant in EVOO yielded linear regression functions with four unique sets of slopes, one for each group of adulterants. Four corresponding slope rules were defined that allowed for the determination of the nature and concentration of an adulterant in EVOO products by applying these four calibration models. The standard addition technique was used for confirmation.

Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near‐Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses

“…The comparison of FT‐NIR spectra for authentic EVOO and adulterant oils in the 5150 to 5350 cm −1 range revealed the presence of significant spectral differences (Figure ), as was first reported by Azizian and others (). In particular, significant intensity variation was observed for the weak feature near 5280 cm −1 attributed to the second overtone of the C═O stretching vibration.…”

“…No 25 Global Method for the detection of extraneous oils in olive oils (IOC ). In addition, all the reference EVOO products used in the present study were found to be authentic based on our published FT‐NIR and PLS procedure (Azizian and others ). The CI analysis and the SIMCA classification model were validated using 3 sample sets consisted of the following: (1) 10 Adulterant oils consisting of 9 vegetable oils obtained from local grocery stores, and a palm olein sample that was purchased from a local store in Indonesia, (2) 20 Test mixtures prepared with one of the authentic EVOO, namely California sample S9, that was spiked with 10% and 20% (w/w) of each of the 10 adulterants, and (3) an additional set of 7 randomly selected authentic EVOO samples acquired from California.…”

“…However, for commercial EVOO products where the identity of a potential adulterant is unknown, the authors proposed that calibration models would have to be developed for each pair of olive oil and suspected adulterant oil (Christy and others ). Recently, a novel FT‐NIR and partial least squares (PLS) methodology was proposed for the rapid prediction of the identity and concentration of a refined olive oil or an adulterant oil (such as an oil high in oleic acid or linoleic acid, or consisting of palm olein) mixed with an EVOO (Azizian and others ).…”

Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions

“…However, due to it was local specialty, few reports about the detection of camellia oil adulteration by NIR were found. Some adulteration detection for olive oil could be treated as references, [19][20][21] as the similarities of camellia oil with olive oil in physical and chemical constants.…”

Characterising variances of milk powder and instrumentation for the development of a non-targeted, Raman spectroscopy and chemometrics detection method for the evaluation of authenticity

“…Raman spectroscopy and near-infrared spectroscopy (NIR) are two promising analytical techniques being investigated as screening tools to identify EMA in food products (Baeten et al 2005; Kasemsumran et al 2007; Cheng et al 2010; Moore et al 2012b; Azizian et al 2015). Common benefits of these two methods, compared with other technologies such as LC-MS/MS (MacMahon et al 2012), ELISA (Moatsou & Anifantakis 2003) and GC-MS/MS (Hong et al 2009), include simplicity, limited need for sample pre-treatment, rapid measurements, rugged instrumentation, portable systems availability and high-throughput analysis.…”