Improved lipid mixtures profiling by 1H NMR using reference lineshape adjustment and deconvolution techniques.

ANALYSIS: of one-dimensional 1H NMR spectra of complex mixtures, such as lipids from natural extracts, is hampered by the small spectral width leading to a great number of overlapped signals. Additional complications including lineshape broadening and distortion may occur due to magnetic field inhomogeneity. Quantitation of such spectra is therefore challenging. We present in this work a quantitation approach based on deconvolution after correction of spectra by means of reference lineshape adjustment (RLA), also known as reference deconvolution. Spectral fit and precision obtained on deconvoluted peaks were used as indicators to iteratively improve the deconvolution process. This approach was tested on 1H NMR spectra of olive oil samples and allowed extraction of 77 peaks (available as peak intensities or areas), whereas spectral integration afforded 5 variables when only well-resolved signals were considered and 29 variables when a bucket around each discernible peak was integrated. Deconvoluted peak intensities and areas were obtained with improved precision after RLA of raw spectra. The use of these spectral variables as predictors in multivariate statistical analysis enhanced the classification of olive oil samples according to the altitude of the olive field or to the color of the olive drupes. The same variables allowed quantitation of oleic, palmitoleic, and vaccenic acids within triacylglycerols, which was not possible by 1H NMR, and improved quantitation of linoleic and linolenic acids. These results proved the high potential of the presented approach in the characterization and authentication of complex mixtures by 1H NMR spectroscopy.

Olive oil characterization and classification by 13C NMR with a polarization transfer technique: A comparison with gas chromatography and 1H NMR.

In a previous work, we optimized and used a fast adiabatic 13C-INEPT (Insensitive Nuclei Enhanced by Polarization Transfer) experiment for the isotopomic analysis of olive oil samples, which allowed us quantifying individual fatty acids within triacylglycerols through multivariate linear regression models. The goal of this study was to validate these models and to evaluate the power of 13C-INEPT in the authentication of olive oils relative to gas chromatography (GC) and 1H NMR. In this respect, a new set of olive oil samples was analyzed by these three techniques. The analytical variables thus obtained as well as their corresponding long-term repeatability were compared. As a result, the reliability of the fatty acid quantification models was proven and the best classification of olive oils according to the altitude of the olive grove and to the morphological aspect (color) of the olives was achieved by means of 13C-INEPT.

Geoclimatic, morphological, and temporal effects on Lebanese olive oils composition and classification: A (1)H NMR metabolomic study.

Two hundred and thirty-four Lebanese olive samples were collected from different regions and the corresponding oils were analysed by (1)H NMR spectroscopy. The variables obtained, related to fatty acids and minor components, were used as inputs in univariate and multivariate analyses aiming to characterize and classify the oils according to geographical, morphological, and temporal factors. Samples were sorted according to the colour, size, and shape of olives, which allowed statistically significant classifications to be achieved. A sequential strategy was developed to discriminate among samples from different altitudes and latitudes. Following this strategy, obvious trends and classifications were obtained at subregional level. Furthermore, the shift in the harvest date within a range of three weeks was considered and its effect on the classification models was investigated. Likewise, the harvest year effect was evaluated; the precipitation level in April and May had a significant impact on the characteristics of the oils.

A strategy for simultaneous determination of fatty acid composition, fatty acid position, and position-specific isotope contents in triacylglycerol matrices by 13C-NMR.

Triacylglycerols, which are quasi-universal components of food matrices, consist of complex mixtures of molecules. Their site-specific 13C content, their fatty acid profile, and their position on the glycerol moiety may significantly vary with the geographical, botanical, or animal origin of the sample. Such variables are valuable tracers for food authentication issues. The main objective of this work was to develop a new method based on a rapid and precise 13C-NMR spectroscopy (using a polarization transfer technique) coupled with multivariate linear regression analyses in order to quantify the whole set of individual fatty acids within triacylglycerols. In this respect, olive oil samples were analyzed by means of both adiabatic 13C-INEPT sequence and gas chromatography (GC). For each fatty acid within the studied matrix and for squalene as well, a multivariate prediction model was constructed using the deconvoluted peak areas of 13C-INEPT spectra as predictors, and the data obtained by GC as response variables. This 13C-NMR-based strategy, tested on olive oil, could serve as an alternative to the gas chromatographic quantification of individual fatty acids in other matrices, while providing additional compositional and isotopic information. Graphical abstract A strategy based on the multivariate linear regression of variables obtained by a rapid 13C-NMR technique was developed for the quantification of individual fatty acids within triacylglycerol matrices. The conceived strategy was tested on olive oil.

Precise and rapid isotopomic analysis by (1)H-(13)C 2D NMR: Application to triacylglycerol matrices.

An optimized HSQC sequence was tested and applied to triacylglycerol matrices to determine their isotopic and metabolomic profiles. Spectral aliasing and non-uniform sampling approaches were used to decrease the experimental time and to improve the resolution, respectively. An excellent long-term repeatability of signal integrals was achieved enabling to perform isotopic measurements. Thirty-two commercial vegetable oils were analyzed by this methodology. The results show that this method can be used to classify oil samples according to their geographical and botanical origins.