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

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.