Phytochemical Markers Of Different Types Of Red Propolis.

Propolis is a resin that bees collect from different plant sources and use in the defense of the bee community. The intricate composition of propolis varies depending on plant sources from different geographic regions and many types have been reported. Red coloured propolis found in several states in Brazil and in other countries has known antimicrobial and antioxidant activity. Different analytical methods have been applied to studies regarding the chemical composition and plant origins of red propolis. In this study samples of red propolis from different regions have been characterised using direct infusion electrospray ionisation mass spectrometry (ESI(-)-MS) fingerprinting. Data from the fingerprints was extracted and analysed by multivariate analysis to group the samples according to their composition and marker compounds. Despite similar colour, the red coloured propolis samples were divided into three groups due to contrasting chemical composition, confirming the need to properly characterise the chemical composition of propolis.

Petroleomics By Electrospray Ionization Ft-icr Mass Spectrometry Coupled To Partial Least Squares With Variable Selection Methods: Prediction Of The Total Acid Number Of Crude Oils.

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.