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The aim of the present study was to classify samples of sugar with different concentrations through a Voltammetric Electronic tongues (VET), with a generic pulse sequence consisted of 22 pulses ranging from –1000 mV to + 1000 mV with a duration of 20 ms/pulse over different samples such as 1.25mM, 2.5mM, 5mM and 10mM, of sucrose concentration, these were measured 4 times each concentration and the test was developed 4 times, giving a total number of 506.880 data supervised learning algorithm using support vector machine was employed, choosing a linear function as a classifying element. In the training, 75% of the data was used to determine the coefficients of the classification function, and the remaining (25%) was used to evaluate the performance of the proposal. The results showed a concordance of more than 80% in the separation of sample, allowing to conclude as acceptable the performance of the classifier and the data acquired through the voltammetric tongue.

Classifiers are tools widely used nowadays to process data and obtain prediction models that are trained through supervised learning techniques; there is a wide variety of sensors that acquire the data to be processed, such as the voltammetric electronic tongue, as a device employed to analyze food compounds. This paper presents a normal and decaffeinated coffee beverage classifier using a Support Vector Machine with a linear separation function, detailing the classification function and the model optimization method; to train the model, the data measured by 4 electrodes of a voltammetric tongue that is excited by a predetermined sequence of positive pulses is used. In addition, the results graphically show the measurements obtained, the support vectors and the evaluation data, the values of the classifier parameters are also presented. Finally, the conclusions establish an acceptable error in the classification of coffee drinks according to caffeine presence at the sample analyzed.

Una parte importante del proceso de panificación es la fermentación. El objetivo fue evaluar el efecto de la temperatura (80, 100 o 130 °C) sobre la capacidad fermentativa de las harinas de avena (HA), maíz (HM) o sorgo (HS) al utilizarse en la sustitución de harina de trigo (HT) a niveles de 10 o 20 %. Se determinó la distribución del tamaño de partícula, capacidad de retención de agua (CRA), y perfil de viscosidad de las harinas utilizando el RVA (Rapid Visco Analyser) y la capacidad fermentativa (medida como volumen de la masa) después de una hora de fermentación. La CRA se vio favorecida cuando se utilizó HA al 20 % tratada térmicamente a 100 °C. Parámetros de viscosidad: temperatura de empaste, viscosidad pico y final fueron afectados al tratar térmicamente las harinas. Estos efectos fueron más notorios al ser sometidas a 130 °C. La capacidad fermentativa mejoró considerablemente cuando se utilizó HA al 20 % (163.93 cm3) tratada a 100 °C, incluso mayor a la alcanzada por HT (136.32 cm3). La utilización de HM y HS al 10% tratada a 130 °C también favoreció este parámetro, sin embargo, no al nivel alcanzado por HT.

espanolSe evaluo el efecto de la sustitucion del 2.5, 5, 7.5 y 10% de harina de trigo (HT) con harina de avena (HA), de maiz (HM) y de sorgo (HS) sobre las propiedades reologicas de la masa, texturales y sensoriales del pan. Se determino la capacidad de retencion de agua (CRA), temperatura de empaste, viscosidad pico y viscosidad final en harinas. En masa, se determino fuerza (F) y volumen a 1 h de fermentacion. Se realizo un analisis sensorial en pan para determinar las preferencias de los panelistas, asi como el analisis del perfil de textura (TPA). Los resultados mostraron diferencias significativas en las mediciones realizadas. El volumen de la masa obtenido al utilizar HA en los cuatro diferentes niveles no mostro diferencias con HT (136.32 cm3 ± 4.62). El analisis sensorial no mostro diferencias cuando el pan se elaboro con HA o HM a 10% y el obtenido solo de HT. EnglishThe effect of substitution of 2.5, 5, 7.5 and 10% of wheat flour (HT) with oat flour (HA), corn (HM) and sorghum (HS) on the rheological properties of the dough, textural and sensorial bread properties was evaluated. The water holding capacity (CRA), pasting temperature, peak viscosity and final viscosity in flour was determined. In dough, force (F) and volume within an hour of fermentation was measured. A sensory analysis of bread to determine the preferences of the panelists and a texture profile analysis (TPA) were also carried out. The results showed significant differences (p ˂ 0.05) among measurements. The volume of the dough using HA at the four levels did not show significant differences compared with HT (136.32 cm3 ± 4.62). Sensory analysis of bread showed no difference (p ˃ 0.05) between the bread made with 10% HA or HM and the HT only bread.

[EN] A non-destructive Vis-NIR spectroscopy (400–1000 nm) method was developed to evaluate the loss of freshness of sliced and commercially packaged cooked ham and turkey ham without any sample manipulation. The spectra were recorded at 0, 30, 40, and 60 days using a camera, spectral filter (400–1000 nm) and a halogen floodlighting system which had been were developed and calibrated for the purpose. Physico-chemical, biochemical, and microbiological properties such as pH, total volatile basic nitrogen (TVB-N), ATP breakdown compounds, and colony-forming units were determined to predict the degradation of freshness. The image spectra obtained from visible and SW-NIR spectroscopy were related to the storage time of the samples. A PLS-DA model was developed independently for packaged or unpackaged samples using the second derivative of the spectra. Mean R2 prediction obtained for cooked ham was 0.915 and 0.949 for Turkey ham. The technique developed could be applied to monitoring the freshness of commercial packed cooked ham and turkey ham as a non-destructive technique. Further studies will be needed to check the spectra obtained from samples of different commercial brands in order to evaluate more precisely the efficiency of the method., Supported by the Polytechnic University of Valencia and Generalitat Valenciana, Projects No. PAID-06-08-3251 and GVPRE/2008/170.