Using offline HPLC-GC-FID 4-Desmethylsterols Concentration Profiles, Combined with Chemometric Tools, to Discriminate Different Vegetable Oils

Simultaneous Analysis of Free/Combined Phytosterols in Rapeseed and Their Dynamic Changes during Microwave Pretreatment and Oil Processing

Here, a simple, efficient, and rapid solid phase extraction-gas chromatography (SPE–GC) method was developed for the simultaneous analysis of free/combined phytosterols in rapeseed and their dynamic changes during microwave pretreatment and oil processing. First, by comparing different methods for extracting free/combined phytosterols from rapeseed and rapeseed cake, the Folch method was considered to be the optimal method and was selected in subsequent experiments. Subsequently, the extraction method was validated by determining the recoveries of standards (brassinosterol, campesterol, β-sitosterol and cholesteryl oleate) spiked in rapeseed and rapeseed oil samples, and the recoveries were in the range from 82.7% to 104.5% and 83.8% to 116.3%, respectively. The established method was applied to study the dynamic changes of the form and content of phytosterols in rapeseed and its products (rapeseed oil and cake) during rapeseed microwave pretreatment and the oil production process. Additionally, the results showed that more than 55% of the free/combined phytosterols in rapeseed were transferred to rapeseed oil during the oil processing, and this proportion will increase after microwave pretreatment of rapeseed. This work will provide analytical methods and data support for a comprehensive understanding of phytosterols in rapeseed and its products during oil processing.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Discrimination and classification of extra virgin olive oil using a chemometric approach based on TMS-4,4'-desmetylsterols GC(FID) fingerprints of edible vegetable oils

A single out-line HPLC-GC (FID) analytical method is applied to acquire the chromatographic fingerprint characteristic of the TMS-4,4'-desmetylsterol derivative fraction of several marketed edible vegetable oils in order to identify and discriminate the most valuable extra-virgin olive oils from the other vegetal oils (canola, corn, grape seed, linseed, olive pomace, peanut, rapeseed, soybean, sesame, seeds (non-specified composition but usually a blend of corn and sunflower) and sunflower). The natural structure of the preprocessed data undergoes a preliminary exploration using principal component analysis and heat map-based cluster analysis. A partial least squares-discriminant model is first trained from 53 oil samples (only 3 latent variables) and externally validated from 18 test oil samples. No classification errors are found and all the test samples are correctly classified. Additional classification models are also built in order to discriminate among vegetables-oil families and excellent results have been also achieved.

Determination of free steroidal compounds in vegetable oils by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry

A method based on comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOF/MS) was developed to analyze steroidal compounds in vegetable oils, which could provide better separation and higher sensitivity than conventional one dimensional gas chromatography, and allowed determination of 31 sterols and triterpene alcohols in one injection. Furthermore, the approach also permitted separation and detection of small amounts of other compounds (may be steroidal compounds whose molecular structures have not been confirmed), which were obscured in the lower-resolution single-column technique. With the help of the GC × GC system, a more elaborate and complete information regarding the distributions and concentrations of free phytosterols and triterpene alcohols in safflower seed oil, soybean oil, rapeseed oil, sunflower seed oil and peanut oil were obtained. The proposed method could potentially open a new opportunity for the more in-depth knowledge of the steroidal compounds of vegetable oils.

Using the liquid-chromatographic-fingerprint of sterols fraction to discriminate virgin olive from other edible oils

A method to discriminate virgin olive oil from other edible vegetable oils such as, sunflower, pomace olive, rapeseed, canola, corn and soybean, applying chemometric techniques to the liquid chromatographic representative fingerprint of sterols fraction, is proposed. After a pre-treatment of the LC chromatogram data - including baseline correction, smoothing signal and mean centering - different unsupervised and supervised pattern recognition procedures, such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares-discriminant analysis (PLSDA), have been applied. From the information obtained from PCA and HCA, two groups can be clearly distinguished (virgin olive and the rest of vegetable oils tested) which have been used to discriminate between two defined classes by means of a PLSDA model. Five latent variables (LVs) explained 76.88% of X-block variance and 95.47% of the defined classes block (γ-block) variance. A root mean square error for calibration and cross validation of 0.10 and 0.22 respectively, confirmed these results and a root mean square error for prediction of 0.15 evidences that the classification model proposed presents an adequate prediction capability. The contingency table also shows the good performance of the model, proving the capability of the LC-R-FpM, to discriminate virgin olive from other vegetable edible oils.