Geographical Classification of Authentic Moroccan Argan Oils and the Rapid Detection of Soya and Sunflower Oil Adulteration with ATR-FTIR Spectroscopy and Chemometrics

Taous, Fouad; Ghali, Tibari El; Marah, Hamid; Laraki, Khalid; Islam, Marivil; Cannavan, Andrew; Kelly, Simon

doi:10.1007/s12161-022-02333-z

Cited by 12 publications

(3 citation statements)

References 41 publications

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“…In a study, argan oil samples from ve different regions were categorized based on their geographic origin utilizing FTIR spectral data in conjunction with PCA and SIMCA. 87 The outcome revealed a general grouping of oil samples according to their geographical origins for all ve classes in both the calibration and validation sets. Nonetheless, the proximity observed between certain oils was attributed to the fact that these regions are neighboring and consequently share similar climates.…”

Section: Identication Of the Designated Origin Of Oilsmentioning

confidence: 96%

Trends in authentication of edible oils using vibrational spectroscopic techniques

Ozen,

Cavdaroglu,

Tokatli

2024

Anal. Methods

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Section: Identication Of the Designated Origin Of Oilsmentioning

confidence: 96%

Trends in authentication of edible oils using vibrational spectroscopic techniques

Ozen,

Cavdaroglu,

Tokatli

2024

Anal. Methods

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“…In another study, Hilali et al [ 55 ] determined the fatty-acids, tocopherol, and triglyceride composition as well as quality parameters in Argan oils obtained from different extraction methods, mechanical pressing from roasted or unroasted kernels, and from different origins (Tidzi, Beniznassen, Ait mzal, and Ighrem). They used GC-FID on a capillary column, HPLC on a silica column with a fluorimetric detection and on a reversed phase C18 column with refractometric detection for fatty-acids, tocopherols and triglycerides analysis, respectively.…”

Section: Argan Oil Quality Control and Authentication Assessmentmentioning

confidence: 99%

Quality Control and Authentication of Argan Oils: Application of Advanced Analytical Techniques

et al. 2023

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In addition to the nutritional and therapeutic benefits, Argan oil is praised for its unique bio-ecological and botanic interest. It has been used for centuries to treat cardiovascular issues, diabetes, and skin infections, as well as for its anti-inflammatory and antiproliferative properties. Argan oil is widely commercialized as a result of these characteristics. However, falsifiers deliberately blend Argan oil with cheaper vegetable oils to make economic profits. This reduces the quality and might result in health issues for consumers. Analytical techniques that are rapid, precise, and accurate are employed to monitor its quality, safety, and authenticity. This review provides a comprehensive overview of studies on the quality assessment of Moroccan Argan oil using both untargeted and targeted approaches. To extract relevant information on quality and adulteration, the analytical data are coupled with chemometric techniques.

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“…Their findings also revealed that samples from the Tiznit and Taroudant regions are somewhat closer, which was explained by the similarity of the geographical parameters. Taous et al [36] used a non-destructive sampling approach to distinguish the geographic origin of Argan oil with MIR spectroscopy and chemometrics. Similar outcomes were found, confirming the link between the geographic origin and group distribution in the PCA plots.…”

Section: Pca Analysismentioning

confidence: 99%

Evaluation of Multivariate Filters on Vibrational Spectroscopic Fingerprints for the PLS-DA and SIMCA Classification of Argan Oils from Four Moroccan Regions

et al. 2023

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This study aimed to develop an analytical method to determine the geographical origin of Moroccan Argan oil through near-infrared (NIR) or mid-infrared (MIR) spectroscopic fingerprints. However, the classification may be problematic due to the spectral similarity of the components in the samples. Therefore, unsupervised and supervised classification methods—including principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA)—were evaluated to distinguish between Argan oils from four regions. The spectra of 93 samples were acquired and preprocessed using both standard preprocessing methods and multivariate filters, such as External Parameter Orthogonalization, Generalized Least Squares Weighting and Orthogonal Signal Correction, to improve the models. Their accuracy, precision, sensitivity, and selectivity were used to evaluate the performance of the models. SIMCA and PLS-DA models generated after standard preprocessing failed to correctly classify all samples. However, successful models were produced after using multivariate filters. The NIR and MIR classification models show an equivalent accuracy. The PLS-DA models outperformed the SIMCA with 100% accuracy, specificity, sensitivity and precision. In conclusion, the studied multivariate filters are applicable on the spectroscopic fingerprints to geographically identify the Argan oils in routine monitoring, significantly reducing analysis costs and time.

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Geographical Classification of Authentic Moroccan Argan Oils and the Rapid Detection of Soya and Sunflower Oil Adulteration with ATR-FTIR Spectroscopy and Chemometrics

Cited by 12 publications

References 41 publications

Trends in authentication of edible oils using vibrational spectroscopic techniques

Trends in authentication of edible oils using vibrational spectroscopic techniques

Quality Control and Authentication of Argan Oils: Application of Advanced Analytical Techniques

Evaluation of Multivariate Filters on Vibrational Spectroscopic Fingerprints for the PLS-DA and SIMCA Classification of Argan Oils from Four Moroccan Regions

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