Spectroscopic and Chemometric Analysis of Binary and Ternary Edible Oil Mixtures: Qualitative and Quantitative Study

Abstract

The aim of this study was to investigate the feasibility of FTIR-ATR spectroscopy coupled with the multivariate numerical methodology for qualitative and quantitative analysis of binary and ternary edible oil mixtures. Four pure oils (extra virgin olive oil, high oleic sunflower oil, rapeseed oil, and sunflower oil), as well as their 54 binary and 108 ternary mixtures, were analyzed using FTIR-ATR spectroscopy in combination with principal component and discriminant analysis, partial least-squares, and principal component regression. It was found that the composition of all 166 samples can be excellently represented using only the first three principal components describing 98.29% of total variance in the selected spectral range (3035-2989, 1170-1140, 1120-1100, 1093-1047, and 930-890 cm(-1)). Factor scores in 3D space spanned by these three principal components form a tetrahedral-like arrangement: pure oils being at the vertices, binary mixtures at the edges, and ternary mixtures on the faces of a tetrahedron. To confirm the validity of results, we applied several cross-validation methods. Quantitative analysis was performed by minimization of root-mean-square error of cross-validation values regarding the spectral range, derivative order, and choice of method (partial least-squares or principal component regression), which resulted in excellent predictions for test sets (R(2) > 0.99 in all cases). Additionally, experimentally more demanding gas chromatography analysis of fatty acid content was carried out for all specimens, confirming the results obtained by FTIR-ATR coupled with principal component analysis. However, FTIR-ATR provided a considerably better model for prediction of mixture composition than gas chromatography, especially for high oleic sunflower oil.

Attenuated Total Reflectance–Fourier Transform Infrared (ATR–FTIR) Spectroscopy Combined with Chemometrics for Rapid Determination of Cold-Pressed Wheat Germ Oil Adulteration

“…The ATR-FTIR spectra were divided into the 28 different wavenumber regions given in Table 2. The selected regions reported in previous studies (Vlachos et al 2006;Lerma-Garcia et al 2010;Rohman and Che Man 2010;Saucedo-hern et al 2011;Zhang et al 2012;Jovic et al 2016) correspond to a peak or a shoulder, signifying structural or functional group information of several bioactive compounds in vegetable oils. Three different chemometric tools have been applied for classification, including soft independent modeling of class analogies (SIMCA), linear discriminant analysis (LDA), and principal component analysis (PCA), and the classification results from each model have been assessed on the basis of several quality metrics.…”

Strategies for determining the bioactive ingredients of honey‐processed Astragalus by serum pharmacochemistry integrated with multivariate statistical analysis

“…PCA aims to transform multiple indicators into simplified ones through dimensionality reduction while loses a little information. Simultaneously, the sample with many complex variables can be minimized and the main contradictions of complex thing can be grasped [10,11]. The raw Astragalus group, the honey‐processed Astragalus group, and the control group were analyzed by PCA analysis and the result showed raw and honey‐processed Astragalus group were overlapped (Figure 2A and B).…”

Conformational Analysis of Cinhonine and Cinhonidine by Tensor Decomposition of Molecular Dynamics Trajectories

“…Principal component analysis was used as a dimensionality reduction tool and performed using a NIPALS algorithm implemented in our own program moonee. [28][29][30] Probability distributions were generated in a reduced space using the n-dimensional parallelepiped and counting all the points that belong in it. These probability distributions were then subjected to a procedure for finding all local maxima that correspond to local minima on the coresponding potential energy surface.…”