¡Pide mi vino!: análisis con eye tracking del etiquetado de botellas de vino en una feria nacional del vino

En una sociedad saturada e hipercomunicada, un diseño eficaz es clave para el éxito de un producto. Las técnicas neurocientíficas al servicio de la publicidad y el marketing constituyen herramientas efectivas a la hora de predecir el comportamiento del consumidor. Teniendo en cuenta este contexto y la gran diversidad de vinos que se comercializan en el mercado español, se pretende analizar el etiquetado de una selección de marcas y envases de vino. El objetivo principal de la investigación fue demostrar a las bodegas participantes cuales eran las zonas de sus etiquetados en los envases que despertaban mayor interés o engagementcon su público objetivo. El estudio emplea un Eye Tracker de sobremesa y se desarrolló en la feria nacional de vino (FENAVIN) celebrada en Ciudad Real (Castilla La-Mancha) en 2019. La muestra objeto de análisis está integrada por especialistas del sector asistentes a la feria. Los resultados demostraron que las zonas superiores y centrales del etiquetado despertaban mayor interés, identificando las áreas ciegas que no reciben atención por parte de los sujetos analizados. El estudio concluye que el grado de atractivo y originalidad en el diseño de las etiquetas son factores determinantes clave en los procesos de percepción y la selección de los consumidores

Software using the Gröbner Cover for geometrical loci computation and classification

We describe here a properly recent application of the Gröbner Cover algorithm (GC) providing an algebraic support to Dynamic Geometry computations of geometrical loci. It provides a complete algebraic solution of locus computation as well as a suitable taxonomy allowing to distinguish the nature of the different components. We included a new algorithm Locus into the Singular grobcov.lib library for this purpose. A web prototype has been implemented using it in Geogebra

Migration strategies toward all optical metropolitan access rings

This paper was published in Journal of Lightwave Technology and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the IEEE website: http://dx.doi.org/10.1109/JLT.2007.901325. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.Nowadays, network operators are steadily deploying optical circuit switching (OCS) equipment in their metropolitan networks in order to cope with traffic increase and, most importantly, in order to reduce capital expenditures and operational expenditures of existing active technologies. On the other hand, optical burst switching (OBS) technology is expected to become mature in the medium term, and it may be used as an alternative to current OCS networks due to its potential advantages in terms of bandwidth allocation granularity. While OBS is being extensively studied in the literature, little attention has been paid in conducting a comparative analysis of OBS versus OCS, especially concerning cost analysis. In this paper, we provide a comparative analysis of OBS versus OCS as an evolutionary technology for all-optical rings in the metropolitan-access network. This paper is specifically targeted toward optimizing the number of optoelectronic receivers and wavelengths with real traffic matrices from the metropolitan rings in Madrid, Spain. Such matrices also include traffic projections of foreseeable broadband services, which are based on a market analysis from the largest operator in Spain. Our findings show that OCS might be more efficient than OBS in the metro-access segment, which is characterized by a highly centralized traffic pattern. However, the more distributed the traffic is, the more efficient the OBS is as well. Consequently, OBS might be better suited to metro-core networks, which show a more distributed and dynamic traffic pattern.The authors would like to thank the e-Photon/ONe+ network of excellenc

Extraction of Anthocyanins and Total Phenolic Compounds from Açai (Euterpe oleracea Mart.) Using an Experimental Design Methodology. Part 3: Microwave-Assisted Extraction

In this work, two methods based on microwave-assisted extraction techniques for the extraction of both anthocyanins and total phenolic compounds from acai have been developed. For that, a full factorial design (Box-Behnken design) has been used to optimize the following four variables: solvent composition (25-75% methanol in water), temperature (50-100 degrees C), pH (2-7), and sample/solvent ratio (0.5 g: 10 mL-0.5 g: 20 mL). The anthocyanins and total phenolic compounds content have been determined by ultra high-pressure liquid chromatography and Folin-Ciocalteu method, respectively. The optimum conditions for the extraction of anthocyanins were 38% MeOH in water, 99.63 degrees C, pH 3.00, at 0.5 g: 10 mL of ratio, while for the extraction of total phenolic compounds they were 74.16% MeOH in water, 99.14 degrees C, pH 5.46, at 0.5 g: 20 mL of ratio. Both methods have shown a high repeatability and intermediate precision with a relative standard deviation lower than 5%. Furthermore, an extraction kinetics study was carried out using extraction periods ranging from 2 min until 25 min. The optimized methods have been applied to acai-containing real samples. The results with such real samples have confirmed that both methods are suitable for a rapid and reliable extraction of anthocyanins and total phenolic compounds

Extraction of anthocyanins and total phenolic compounds from açai (euterpe oleracea mart.) using an experimental design methodology. part 1: Pressurized liquid extraction

Currently, açai is one of the most important fruits present in the world. Several studies have demonstrated its high content in phenolic compounds and anthocyanins. Both of them are responsible of interesting properties of the fruit such as anti-inflammatory, antioxidant or anticancer. In the present study, two optimized pressurized liquid extraction (PLE) methods have been developed for the extraction of anthocyanins and total phenolic compounds from açai. A full factorial design (Box-Behnken design) with six variables (solvent composition (25%-75% methanol-in-water), temperature (50-100°C), pressure (100-200 atm), purge time (30-90 s), pH (2-7) and flushing (50%-150%)) were employed. The percentage of methanol in the extraction solvent was proven to be the most significant variable for the extraction of anthocyanins. In the case of total phenolic compounds, the extraction temperature was the most influential variable. The developed methods showed high precision, with relative standard deviations (RSD) of less than 5%. The applicability of the methods was successfully evaluated in real samples. In conclusion, two rapid and reliable PLE extraction methods to be used for laboratories and industries to determine anthocyanins and total phenolic compounds in açai and its derived products were developed in this work

Extraction of Anthocyanins and Total Phenolic Compounds from Acai (Euterpe oleracea Mart.) Using an Experimental Design Methodology. Part 2: Ultrasound-Assisted Extraction

Two optimized methods for ultrasound-assisted extraction were evaluated for the extraction of two types of acai bioactive compounds: Total anthocyanins (TAs) and total phenolic compounds (TPCs). For the extraction optimization, a Box Behnken factorial design of different variables in the following intervals was used: Methanol-water (25%-75%) for solvent composition, temperatures between 10 and 70 degrees C, amplitude in the range between 30% and 70% of the maximum amplitude -200 W), extraction solvent pH (2-7), the ratio for sample-solvent (0.5 g:10 mL-0.5 g:20 mL), and cycle between 0.2 and 0.7 s. The extraction kinetics were studied using different periods between 5 and 30 min. TA and TPC were analyzed by UHPLC and the Folin-Ciocalteu method, respectively. Optimized conditions for TA were: 51% MeOH in water, 31 degrees C temperature, pH 6.38, cycle 0.7 s, 65% amplitude, and 0.5 g:10 mL of sample-solvent ratio. Optimized conditions for the TPC were: 49% MeOH in water, 41 degrees C temperature, pH 6.98, cycle 0.2 s, 30% amplitude, and 0.5 g:10 mL of sample-solvent ratio. Both methods presented a relative standard deviation below 5% in the precision study. The suitability of the methods was tested in real samples. It was confirmed that these methods are feasible for the extraction of the studied bioactive compounds from different acai matrices

Photovoltaic Energy Harvesting System Adapted for Different Environmental Operation Conditions: Analysis, Modeling, Simulation and Selection of Devices

The present research work proposes a photovoltaic energy harvester and an appropriate direct current (DC)/DC converter for a harvesting system after the study of the devices and taking the operation conditions. Parameters such as power, efficiency and voltage are taken into account under different environment conditions of illumination and temperature in order to obtain the best possible response. For this reason, suitable metal-oxide semiconductor field-effect transistor (MOSFET), diode, coil, frequency, duty-cycle and load are selected and analyzed for a DC/DC converter with boost architecture.This work has been supported by IK4-TEKNIKER research institute own funds and the joint work of Electronics and Communications and Intelligent Information System units and by the Department of Education of the Basque Government within the fund for research groups of the Basque university system [IT978-16]

Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics

Interpretation of data from fire debris is considered as one of the most challenging steps in fire investigation. Forensic analysts are tasked to identify the presence or absence of ignitable liquid residues (ILRs) which may indicate whether a fire was started deliberately. So far, data analysis is subjected to human interpretation following the American Society for Testing and Materials' guidelines (ASTM E1618) based on gas chromatography-mass spectrometry data. However, different factors such as interfering pyrolysis compounds may hinder the interpretation of data. Some substrates release compounds that are in the range of common ignitable liquids, which interferes with accurate determination of ILRs. The aim of the current research is to investigate whether headspace-mass spectroscopy electronic nose (HS-MS eNose) combined with pattern recognition can be used to classify different ILRs from fire debris samples that contain a complex matrix (petroleum-based substrates or synthetic fibers carpet) that can strongly interfere with their identification. Six different substrates-four petroleum-derived substrates (vinyl, linoleum, polyester, and polyamide carpet), as well as two different materials for comparison purposes (cotton and cork) were used to investigate background interferences. Gasoline, diesel, ethanol, and charcoal starter with kerosene were used as ignitable liquids. In addition, fire debris samples were taken after different elapsed times. A total of 360 fire debris samples were analyzed. The obtained total ion mass spectrum was combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) as well as supervised linear discriminant analysis (LDA). The results from HCA show a strong tendency to group the samples according to the ILs and substrate used, and LDA allowed for a full identification and discrimination of every ILR regardless of the substrate.Consejería de Economía, Conocimiento, Empresas y Universidad. Junta de Andalucía; 2014-2020 ERDF Operational Programm

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices

Optimization through a Box-Behnken Experimental Design of the Microwave-Assisted Extraction of the Psychoactive Compounds in Hallucinogenic Fungi (Psylocibe cubensis)

Hallucinogenic fungi, mainly those from the Psilocybe genus, are being increasingly consumed even though there is no control on their culture conditions. Due to the therapeutic potential as antidepressants and anxiolytics of the alkaloids that they produce (psilocin and psilocybin), some form of control on their production would be highly recommended. Prior to identifying their optimal culture condition, a methodology that allows their study is required. Microwave-assisted extraction method (MAE) is a technique that has proven its efficiency to extract different compounds from solid matrices. For this reason, this study intends to optimize a MAE method to extract the alkaloids found in Psylocibe cubensis. A surface-response Box-Behnken design has been employed to optimize such extraction method and significantly reduce time and other resources in the extraction process. Based on the Box-Behnken design, 50 degrees C temperature, 60% methanol as extraction solvent, 0.6 g:10 mL sample mass:solvent ratio and 5 min extraction time, were established as optimal conditions. These mild conditions, combined with a rapid and efficient UHPLC analysis result in a practical and economical methodology for the extraction of psilocin and psilocybin from Psylocibe cubensis