A Comparison of the Stability of Refined Edible Vegetable Oils under Frying Conditions: Multivariate Fingerprinting Approach.

The stability of highly consumed vegetable refined oils after discontinuous frying of potatoes was compared. Both those vegetable oils containing additives and those that did not were considered. Vegetable oil samples were evaluated using refractive index, anisidine and peroxide values, UV absorbance and dielectric constant-based determination of the content of total polar compounds.

Monitoring the Shelf Life of Refined Vegetable Oils under Market Storage Conditions-A Kinetic Chemofoodmetric Approach.

Most physicochemical and sensory properties of edible vegetable oils are not stable over time. One of the main causes of quality depletion of vegetable oils is oxidation, which influences sensory acceptability and nutritional value, and could even lead to toxic compounds. That negative influence affects international refined oil prices and the variety of its culinary applications.

Instrument-Agnostizing Methodology for Liquid Chromatography-Mass Spectrometry Systems.

Mass spectrometry is a powerful analytical technique used to identify unknown compounds, to quantify known compounds, and to elucidate the structure and chemical properties of molecules. Nevertheless, the transfer of data from one instrument to another is one of the main problems, and obtaining the same or similar information from an analogous instrument but from a different manufacturer or even with the same instrument after carrying out the analyses in different times spacing is not possible. Hence, a general methodology to provide a chromatographic signal (or chromatogram) independent of the instrument is needed.

Applying an instrument-agnostizing methodology for the standardization of pesticide quantitation using different liquid chromatography-mass spectrometry platforms: A case study.

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful technique commonly used for pesticide residue analysis in agri-food matrices. Despite the fact it has several advantages, one of the main problems is the transferability of the data from one analytical equipment to another for identification and quantitation purposes. In this study, instrument-agnostizing methodology was used to set standard retention scores (SRSs), which was utilized as a parameter for the identification of 74 targeted compounds when different instruments are used.

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges.

Chromatograms are a valuable source of information about the chemical composition of the food being analyzed. Sometimes, this information is not explicit and appears in a hidden or not obvious way. Thus, the use of chemometric tools and data-mining methods to extract it is required.

Standardization of chromatographic signals - Part I: Towards obtaining instrument-agnostic fingerprints in gas chromatography.

One of the main causes for the sparse use of multivariate analytical methods in routine laboratory work is the dependency on the measuring instrument from which the analytical signal is acquired. This issue is especially critical in chromatographic equipment and results in limitations of their applicability. The solution to this problem is to obtain a standardized instrument-independent signal -or instrument-agnostic signal- regardless of the measuring instrument or of the state of the same instrument from which it has been acquired.

Standardization of chromatographic signals - Part II: Expanding instrument-agnostic fingerprints to reverse phase liquid chromatography.

There is a large amount of literature relating to multivariate analytical methods using liquid chromatography together with multivariate chemometric/data mining methods in the food science field. Nevertheless, dating the obtained results cannot be compared as they are based on data acquired by a particular analytical instrument, thus they are instrument-dependant. Therefore, this creates difficulties in generating a database large enough to gather together all the variability of the samples.

PLS-DA vs sparse PLS-DA in food traceability. A case study: Authentication of avocado samples.

Conventional and sparse partial least squares-discriminant analysis (PLS-DA and sPLS-DA) have been successfully tested in order to authenticate avocado samples in terms of three different geographical origins and six kinds of cultivar. For this, lipid chromatographic fingerprints of different avocado fruits have been acquired using gas chromatography coupled with flame ionization detector (GC-FID) and employed for building classification models. In addition, classification models concatenating strategy has been applied, which has proved to be successful to resolve multiclass problems in food authentication.