A. Alcázar

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.

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.

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques.

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

Characterization of aniseed-flavoured spirit drinks by headspace solid-phase microextraction gas chromatography-mass spectrometry and chemometrics

The volatile composition of aniseed-flavoured spirit drinks was studied by headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). The influence of the time, temperature, volume of sample and ionic strength on the extraction were considered. Several aniseed-flavoured spirit drinks, such as pastis, sambuca, anis and raki were analyzed. The major compounds found were trans-anethole (41.22-98%), cis-anethole (0.77-18.65%) and estragole (0.1-17.96%). gamma-Himachalene (0-28.07%) and alpha-himachalene (0-4.8%) were also present in anis and raki beverages. The compounds determined were used as chemical descriptors to differentiate the four types of beverages considered. Principal component analysis (PCA), linear discriminant analysis (LDA) and artificial neural networks (ANN) were used as chemometric tools for characterization purposes.

Classification of aniseed drinks by means of cluster, linear discriminant analysis and soft independent modelling of class analogy based on their Zn, B, Fe, Mg, Ca, Na and Si content.

Zinc, boron, iron, magnesium, calcium, sodium and silicon were determined in aniseed drinks by inductively coupled plasma-atomic emission spectrometry (ICP-AES). These elements were considered as chemical descriptors to characterise Spanish-certified aniseed drinks brands of origin. Different pattern recognition (PR) procedures were applied for these purposes. This chemometric study have included methods like principal component analysis (PCA), non-supervised PR techniques such as cluster analysis (CA), supervised PR methods of hard modelling like linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA).

Geographical characterization of Spanish PDO paprika by multivariate analysis of multielemental content

A multielemental analytical method has been proposed to determine the contents of Al, B, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, Sr and Zn in paprika samples from the two Protected Designations of Origin recognized in Spain, such as Murcia and La Vera (Extremadura). The samples are mineralized by acid wet digestion using a mixture of perchloric and nitric acids and analyzed by means of inductively coupled plasma atomic emission spectroscopy. The method performance has been checked studying the absence of matrix effect, trueness, precision, linearity, limit of detection and limit of quantification. The proposed method has been applied to analyze samples of sweet, hot and hot/sweet paprika from the considered production areas. Differences between paprika samples from Murcia and Extremadura were found and pattern recognition methods, such as linear discriminant analysis, linear support vector machines, soft independent modeling of class analogy and multilayer perceptrons artificial neural networks, has been used to obtain classification models. Sweet and hot/sweet paprika types were differentiated by means of linear models and hot paprika was differentiated by using artificial neural networks. A model based on artificial neural networks is proposed to differentiate the geographical origin of paprika, with independence of the type, leading to an overall classification performance of 99%.

Classification of Spanish DO white wines according to their elemental profile by means of support vector machines.

Spanish white wines from four production areas protected by Appellation Control laws have been analysed by inductively coupled plasma optical emission spectrometry to determine the contents of aluminium, barium, boron, calcium, chromium, copper, iron, magnesium, manganese, nickel, phosphorous, potassium, silicon, sodium, strontium, sulphur and zinc. These elements were used as chemical descriptors in order to differentiate wines from different brands certified of origin. Kruskal-Wallis test was applied to highlight significant differences between the four considered classes and pattern recognition methods were applied to construct classification models. In this way, principal component analysis was used to visualise data trends and backward stepwise linear discriminant analysis was applied in order to reduce the number of input variables. The concentrations of chromium, manganese, silicon, sodium and strontium were used to construct a support vector machine classification model, obtaining a 100% of classification performance.

Geographical Authentication of Tequila According to its Mineral Content by Means of Support Vector Machines

The elemental profile of tequila samples from the three main production areas of the State of Jalisco, namely Amatitlan, Guadalajara, and Tequila, was used to carry out their geographical characterization. With this aim, the concentration of Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, S, Sr, and Zn was determined by inductively coupled plasma atomic emission spectroscopy. Principal component analysis was addressed to visualize data trends. The number of input variables was reduced by means of backward stepwise linear discriminant analysis and support vector machines were used to construct an adequate classification model. The best classification performance was obtained by a linear support vector machine model with 100% of prediction ability.

Characterisation of tequila according to their major volatile composition using multilayer perceptron neural networks.

Differentiation of silver, gold, aged and extra-aged tequila using 1-propanol, ethyl acetate, 2-methyl-1-propanol, 3-methyl-1-butanol and 2-methyl-1-butanol and furan derivatives like 5-(hydroxymethyl)-2-furaldehyde and 2-furaldehyde has been carried out. The content of 1-propanol, ethyl acetate, 2-methyl-1-propanol, 3-methyl-1-butanol and 2-methyl-1-butanol was determined by means of head space solid phase microextraction gas chromatography mass-spectrometry. 5-(Hydroxymethyl)-2-furaldehyde and 2-furaldehyde were determined by high performance liquid chromatography with diode array detection. Kruskal-Wallis test was used to highlight significant differences between types of tequila. Principal component analysis was applied as visualisation technique. Linear discriminant analysis and multilayer perceptron artificial neural networks were used to construct classification models. The best classification performance was obtained when multilayer perceptron model was applied.

Enzymatic-spectrophotometric determination of sucrose in coffee beans

A spectrophotometric method for determining sucrose is proposed. Sucrose is hydrolyzed by invertase into glucose and fructose. Then, glucose is oxidized in presence of glucose oxidase and the produced hydrogen peroxide reacts with phenol-4-sulfonic acid sodium salt and 4-aminoantipyrine in presence of peroxidase, yielding a pink dye with an absorption maximum at 505 nm. This method was validated following the EURACHEM and VAM project guidelines for method validation. Trueness, precision, robustness, sensitivity and linearity were considered. The method was applied to the determination of sucrose in green and roasted coffee beans. A comparison with the HPLC method with pulsed amperometric detection was carried out.