Microwave-assisted methanolysis of green coffee oil.

Optimisation of a microwave-assisted methanolysis was performed to obtain cafestol and kahweol directly from green coffee oil (Coffea arabica). A two-factor (the methanolysis period and temperature), three-level, factorial experimental design (3(2)) was adopted. The methanolysis procedure was performed under microwave irradiation, using closed vessel and accurate fast responding internal fibre-optic temperature probe. The effects on the responses were measured by HPLC. After 3 min of microwave irradiation (hold time) at 100°C, with 500 mg of green coffee oil, a yield higher than 99% was obtained. The yield of this reaction is 26% after 2h when working under conventional heating. The methods described in the literature lead to long reaction times, poor yields and formation of side products. The microwave-assisted technique proved to be faster, avoided undesired side products and gave better conversion, when compared to conventional heating process.

Evaluation of alignment methods and data pretreatments on the determination of the most important peaks for the discrimination of coffee varieties Arabica and Robusta using gas chromatography-mass spectroscopy.

Coffee samples were analyzed by GC/MS in order to determine the most important peaks for the discrimination of the varieties Arabica and Robusta. The resulting peak tables from chromatographic analysis were aligned and pretreated before being submitted to multivariate analysis. A rapid and easy-to-perform peak alignment procedure, which does not require advanced programming skills to use, was compared with the tedious manual alignment procedure. The influence of three types of data pretreatment, normalization, logarithmic and square root transformations and their combinations, on the variables selected as most important by the regression coefficients of partial least squares-discriminant analysis (PLS-DA), are shown. Test samples different from those used in the calibration and comparison with the substances already known as being responsible for Arabica and Robusta coffees discrimination were used to determine the best pretreatments for both datasets. The data pretreatment consisting of square root transformation followed by normalization (RN) was chosen as being the most appropriate. The results obtained showed that the much quicker automated aligned method could be used as a substitute for the manually aligned method, allowing all the peaks in the chromatogram to be used for multivariate analysis.