Comparative study of three fingerprint analytical approaches based on spectroscopic sensors and chemometrics for the detection and quantification of argan oil adulteration

Abstract

BACKGROUND: Argan oil is one of the purest and rarest oils in the world, so that the addition of any further product is strictly prohibited by international regulations. Consequently, it is necessary to establish reliable analytical methods to ensure its authenticity. In this study, three multivariate approaches have been developed and validated using fluorescence, UV-visible, and attenuated total reflectance Fourier transform mid-infrared (FT-MIR) spectroscopies. RESULTS:The application of a partial least squares discriminant analysis model showed an accuracy of 100%. The quantification of adulteration have been evaluated using partial least squares (PLS) regression. The PLS model developed from fluorescence spectroscopy provided the best results for the calibration and cross-validation sets, as it showed the highest R 2 (0.99) and the lowest root mean square error of calibration and cross-validation (0.55, 0.79). The external validation of the three multivariate approaches by the accuracy profile shows that these approaches guarantee reliable and valid results of 0.5-32%, 7-32%, and 10-32% using fluorescence, FT-MIR and UV-visible spectroscopies respectively. CONCLUSION: This study confirmed the feasibility of using spectroscopic sensors (routine technique) for rapid determination of argan oil falsification.

Argan Oil: Chemical Composition, Extraction Process, and Quality Control

“…Other authors have used the content of unsaponifiable matter to detect the adulteration of argan oil by mineral oil (4). Recently, El Orche (78) has used three fingerprint analytical approaches based on spectroscopic sensors and chemometrics for the detection and quantification of argan oil adulteration (78).…”

Innovation of argan (Argania spinosa (L.) Skeels) products and byproducts for sustainable development of rural communities in Morocco. A systematic literature review

“…Although it can also ease the pressure on the argan forest, the falsification of argan oil for alimentary, medical or cosmetic purposes can be a serious problem, as can lead to a change in the organoleptic properties or in the pharmacological effects (Kurkin et al 2021). Various relevant papers focused on proposing and testing different techniques for detection of argan oil adulteration: from spectrometry (Gonzálvez et al 2010;Oussama et al 2012;Stokes et al 2018;Farres et al 2019;El Orche et al 2022), to the use of markers as stigmastadiene (Ourracha et al 2012) or triacylglycerol profiles (Salgh et al, 2014), formation of gold nanoparticles and spectrophotometric analysis (Zougagh et al 2011), or combination of a voltametric e-tongue and an e-nose based on metal oxide semiconductor sensors and pattern recognition techniques (Bougrini et al 2014), to real-time PCR approaches (Amaral et al 2022). Even though different techniques can be applied, authors agree with the need of developing a standardized analytical technique and procedure, fast, simple, with high sensitivity, low costs and with a high potential to be transferred to routine laboratories (Zougagh et al 2011;Oussama et al 2012.…”

Tracing models for checking beef adulterated with pig blood by Fourier transform near-infrared paired with linear and nonlinear chemometrics

“…Performances of PLS, SVM, and ELM for predicting adulteration levels were evaluated by the root mean square error of cross-validation (RMSECV), the root mean squared error of prediction (RMSEP), the correlation coefficients (r) in the training set (r t ) and prediction set (r p ), the ratio performance to deviation (RPD), and the range error ratio (RER) (El Orche et al, 2022;Han et al, 2022b;Mahanti et al, 2020). The RMSECV, RMSEP, r, RPD, and RER were calculated with the following Equations 12 to 16,…”