F. Pablos

HPLC analysis of tocopherols and triglycerides in coffee and their use as authentication parameters

The triglyceride and tocopherol contents of green and roasted coffee beans belonging to the arabica and robusta varieties were determined by reversed phase and normal phase high resolution liquid chromatography, respectively. Refractive index detector was used in the case of the triglycerides and fluorescence for tocopherols. Coffee oil was Soxhlet extracted with hexane. By considering the triglyceride and tocopherol profiles as chemical descriptors, a chemometric study with authentication purposes was performed to differentiate coffee varieties. Pattern recognition techniques like principal component analysis and linear discriminant analysis were carried out. Discrimination between arabica and robusta coffees was achieved with both profiles, but only tocopherols also allow the differentiation between green and roasted coffees.

Multivariate characterisation of beers according to their mineral content

In the present paper, a study on the characterisation of beer samples according to their mineral content has been carried out. Zn, P, B, Mn, Fe, Mg, Al, Sr, Ca, Ba, Na and K were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES) in 32 beer samples. Lager, dark and low alcoholic content beers were considered. The analysed elements were considered as chemical descriptors in order to apply pattern recognition procedures, including display and supervised learning methods such as linear discriminant analysis and artificial neural networks.

Discrimination between arabica and robusta green coffee varieties according to their chemical composition

Arabica and robusta green coffee varieties have been differentiated by using pattern recognition procedures. Chlorogenic acid, caffeine, trigonelline, aqueous extract, amino acids and polyphenols have been analysed in 41 samples of green coffee and used as chemical descriptors. Principal component and cluster analysis in addition with the K-nearest neighbours method have been applied.

Simultaneous determination of caffeine and non-steroidal anti-inflammatory drugs in pharmaceutical formulations and blood plasma by reversed-phase HPLC from linear gradient elution.

A reversed-phase HPLC procedure based on methanol-water gradient elution for determining caffeine and non-steroidal anti-inflammatory drugs with UV absorbance detection is proposed. Chromatographic operational conditions were selected by considering the peak resolution and the retention times of the first and last eluted compounds. The method was suitably validated and successfully applied to the determination of: caffeine, indoprofen, ketoprofen, naproxen, fenbufen and ibuprofen in blood plasma samples and several analgesic/antiphlogistic pharmaceutical formulations.

Fatty acid profiles as discriminant parameters for coffee varieties differentiation

The fatty acid contents of coffee lipid extracts have been determined by capillary gas chromatography. Ten fatty acids were considered: myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3), arachidic (C20:0), eicosenoic (C20:1) and behenic acid (C22:0). The analyzed coffee samples belonged to arabica and robusta varieties and were either green or roasted coffee beans. The lipids were Soxhlet extracted from ground coffee beans with hexane, and the fatty acids were determined as their corresponding methyl esters. Fatty acids contents were considered as chemical descriptors to differentiate coffee varieties. Several Pattern Recognition methods, Principal Component Analysis and Linear Discriminant Analysis allowed discrimination between green and roasted arabica and robusta coffees.

Characterization of arabica and robusta roasted coffee varieties and mixture resolution according to their metal content

The metal content of roasted coffee samples belonging to the arabica and robusta varieties and coffee blends has been analysed. Ba, Ca, Cu, Fe, K, Mg, Mn, Na, P, Sr, Zn have been determined by inductively coupled plasma atomic emission spectrometry. Principal component and cluster analysis have been applied to characterize the coffee varieties. P, Mn and Cu have been found to be the most discriminating variables. Partial least squares regression was applied to determine the relative content of each variety in the coffee blends. This method has been applied to determine the percentage of the robusta variety in some commercial roasted coffee samples.

Characterization of green coffee varieties according to their metal content

Eleven metals Zn, P, Mn, Fe, Mg, Ca, Na, K, Cu, Sr and Ba, chosen as chemical descriptors, have been analysed by inductively coupled plasma atomic emission spectrometry. The metal content was studied in 41 samples of green coffee belonging to the varieties arabica and robusta. Pattern recognition techniques such as principal component analysis and cluster analysis were applied in order to characterize the green coffee varieties.

Ion chromatographic determination of some organic acids, chloride and phosphate in coffee and tea

An ion chromatographic method for the simultaneous determination of organic acids and inorganic ions is described. Acetic, malic, ascorbic, citric, malic and succinic acids, chloride and phosphate were determined in coffee and tea samples. The separation is performed on an anion-exchange column operated at 40 degrees C within 25 min by an isocratic elution with 0.6 mM aqueous potassium hydrogenphthalate (pH 4.0) solution containing 4% (v/v) acetonitrile as eluent and determination by conductivity detection. The method does not need a special sample treatment and was successfully applied to the analysis of black, green and oolong tea samples. Also, green and roasted coffee samples from the varieties arabica and robusta were analyzed.

Classification of tea samples by their chemical composition using discriminant analysis.

Multivariate analysis in combination with pattern recognition procedures has been applied to samples of green and black tea. Discriminant analysis has been used for classification purposes. Aqueous extract, polyphenols, amino acids, caffeine, theobromine and theophylline were used as chemical descriptors.

Application of pattern recognition to the discrimination of roasted coffees

Pattern recognition procedures have been applied to samples of roasted coffee. Some of them are torrefacto samples, that is coffee roasted with addition of sugar. Principal component analysis, cluster analysis, linear discriminant analysis, soft independent modelling of class analogies, and Spearman correlation studies have been carried out. Caffeine, aqueous extract, amino acids, polyphenols, 5-(hydroxymethyl)furfural, potassium, sodium, calcium, iron, manganese and magnesium have been used as chemical descriptors.