Impregnación en CO2 supercrítico de diferentes sustratos poliméricos, como alternativa a los tratamientos textiles, comúnmente empleados en el Mediterráneo

En el siguiente trabajo se realiza la impregnación de diferentes sustratos poliméricos con agentes biocidas y con un colorante textil, comúnmente empleados en los procesos de acabados textiles. En este estudio se realiza la selección del colorante Disperse Red 167 (DR167), mediante la comparación de solubilidad en CO2 supercrítico (scCO2) entre varios colorantes dispersos. Los agentes biocidas seleccionados han sido; esencia de clavo (eugenol) y aceite esencial de orégano. Se ha realizado la impregnación de diferentes sustratos poliméricos; poliéster (PES), polipropileno (PP), y algodón (CO), en diferentes condiciones. En total se realizaron impregnaciones utilizando diez concentraciones relativas del DR167. El objetivo principal es determinar las condiciones óptimas de procesado para cada sustrato. Para determinar el rendimiento de la tintura en scCO2 se han representado los diagramas cromáticos de las muestras tintadas en diferentes condiciones. Las muestras de PES son las que presentan mayor rendimiento de color, sabiendo que esta es la única fibra que presenta afinidad con el DR167. Para determinar el efecto de inhibición de las bacterias se han realizado ensayos de actividad antimicrobiana y actividad fungicida. Puede indicarse que sí se observó cierta actividad inhibitoria frente algunos microorganismos, como Staphylococcus aureus, mientras que no se observó una actividad inhibitoria importante frente a otros como Pseudomonas aeruginosa.

Sentiment classification for early detection of health alerts in the chemical textile domain

In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.

Towards the Design of a Textile Chemical Ontology

The main goal of this paper is to present the initial version of a Textile Chemical Ontology, to be used by textile professionals with the purpose of conceptualising and representing the banned and harmful chemical substances that are forbidden in this domain. After analysing different methodologies and determining that “Methontology” is the most appropriate for the purposes, this methodology is explored and applied to the domain. In this manner, an initial set of concepts are defined, together with their hierarchy and the relationships between them. This paper shows the benefits of using the ontology through a real use case in the context of Information Retrieval. The potentiality of the proposed ontology in this preliminary evaluation encourages extending the ontology with a higher number of concepts and relationships, and validating it within other Natural Language Processing applications.