José Marcos Jurado

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).

Quantitation of twelve metals in tequila and mezcal spirits as authenticity parameters.

In this paper the differentiation of silver, gold, aged and extra-aged tequila and mezcal has been carried out according to their metal content. Aluminum, barium, calcium, copper, iron, magnesium, manganese, potassium, sodium, strontium, zinc, and sulfur were determined by inductively coupled plasma optical emission spectrometry. The concentrations found for each element in the samples were used as chemical descriptors for characterization purposes. Principal component analysis, linear discriminant analysis and artificial neural networks were applied to differentiate types of tequila and mezcal. Using probabilistic neural networks 100% of success in the classification was obtained for silver, gold, extra-aged tequila and mezcal. In the case of aged tequila 90% of samples were successfully classified. Sodium, potassium, calcium, sulfur, magnesium, iron, strontium, copper and zinc were the most discriminant elements.

Authentication of fattening diet of Iberian pigs according to their volatile compounds profile from raw subcutaneous fat.

AbstractThe composition of volatile components of subcutaneous fat from Iberian pig has been studied. Purge and trap gas chromatography−mass spectrometry has been used. The composition of the volatile fraction of subcutaneous fat has been used for authentication purposes of different types of Iberian pig fat. Three types of this product have been considered, montanera, extensive cebo and intensive cebo. With classification purposes, several pattern recognition techniques have been applied. In order to find out possible tendencies in the sample distribution as well as the discriminant power of the variables, principal component analysis was applied as visualisation technique. Linear discriminant analysis (LDA) and soft independent modelling by class analogy (SIMCA) were used to obtain suitable classification models. LDA and SIMCA allowed the differentiation of three fattening diets by using the contents in 2,2,4,6,6-pentamethyl-heptane, m-xylene, 2,4-dimethyl-heptane, 6-methyl-tridecane, 1-methoxy-2-propanol, isopropyl alcohol, o-xylene, 3-ethyl-2,2-dimethyl-oxirane, 2,6-dimethyl-undecane, 3-methyl-3-pentanol and limonene. FigureIberian pigs in outdoor rearing system (Montanera)

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%.

Supercritical fluid chromatography with photodiode array detection for pesticide analysis in papaya and avocado samples

To improve the analysis of pesticides in complex food matrices with economic importance, alternative chromatographic techniques, such as supercritical fluid chromatography, can be used. Supercritical fluid chromatography has barely been applied for pesticide analysis in food matrices. In this paper, an analytical method using supercritical fluid chromatography coupled to a photodiode array detection has been established for the first time for the quantification of pesticides in papaya and avocado. The extraction of methyl parathion, atrazine, ametryn, carbofuran, and carbaryl was performed through the quick, easy, cheap, effective, rugged, and safe methodology. The method was validated using papaya and avocado samples. For papaya, the correlation coefficient values were higher than 0.99; limits of detection and quantification ranged from 130-380 and 220-640 μg/kg, respectively; recovery values ranged from 72.8-94.6%; precision was lower than 3%. For avocado, limit of detection values were ˂450 μg/kg; precision was lower than 11%; recoveries ranged from 50.0-94.2%. Method feasibility was tested for lime, banana, mango, and melon samples. Our results demonstrate that the proposed method is applicable to methyl parathion, atrazine, ametryn, and carbaryl, toxics pesticides used worldwide. The methodology presented in this work could be applicable to other fruits.

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.