Service-learning by PhD students to aid socially neglected people

In recent years, there have been calls for change in higher education to meet the needs of today's society. A higher education that enables our students to offer solutions to struggling areas of our society. Innovative and differentiating solutions from what we have been used to until now. In view of these needs, it is necessary to unite the society, which reveals its main needs, and the university community, which offers solutions on the knowledge acquired. One of the ways to carry out this integration is based on developing a methodology called "Service-Learning" (SL). This learning method is based on a strategy of collaboration between educational centers and society itself. At present, this methodology is spreading within higher education institutions worldwide. This learning strategy emerged as a learning methodology in America, to be later extended to Europe, from the United Kingdom to the rest of the continent, and from there, reaching a global impact. Throughout this long road, this methodology has been improving, encouraging the creation of increasingly strong links between educational institutions and universities, and society, by promoting the improvement of student training as well as the development of certain areas of society. This paper presents a SL project where two apparently disparate areas are related, such as doctoral students in the area of chemical engineering and sectors of society at risk of exclusion. Specifically, the objective is for the students to present some of the technological developments they have achieved to a neglected sector of society, which should participate not only in the developments, but also learning about the technical base of such technologies.This work has been carried out with the financial support of the SL UCM 2018/19_16 project and the Madrid City Council.Torrecilla, J.; Buitrón Ruiz, S.; Sánchez, M.; Cancilla, JC.; Pradana López, S.; Perez Calabuig, AM. (2020). Service-learning by PhD students to aid socially neglected people. En 6th International Conference on Higher Education Advances (HEAd'20). Editorial Universitat Politècnica de València. (30-05-2020):831-837. https://doi.org/10.4995/HEAd20.2020.11153OCS83183730-05-202

Service-learning - Diagnostic technologies presented by Ph.D. students to help socially neglected people during the SARS-CoV-2 pandemic

[EN] Innovation is becoming a fundamental part of many sectors and environments, including universities, not only regarding their research but also how education is approached. In this line, one of the ways to achieve these objectives is based on a collaboration between society, which presents a need to be solved, and the University. This society-university integration is achievable via the Service-Learning methodology (SLM), which has always been based on establishing strong links between the educational world and society. This paper presents a SLM-based project linked to technological development for the health sector during the SARS-CoV-2 pandemic. Specifically, undergraduate chemistry students, and Ph.D. students led by professors have presented part of their technological developments to people in society who are at risk of exclusion to help them understand aspects of infection as well as diagnosis of diseases. In short, the objective is to transfer in an enjoyable fashion, a set of technological developments and knowledge that can help society understand aspects related to the COVID-19 pandemic.This work has been carried out with the financial support of the SL UCM 2018/19_16 and SL UCM 2019/21_14 project and the Madrid City Council.Torrecilla, J.; Cancilla, J.; Pradana López, S.; Pérez Calabuig, A.; Izquierdo, M.; García Rodríguez, Y.; Reyes Ramirez, E.... (2021). Service-learning - Diagnostic technologies presented by Ph.D. students to help socially neglected people during the SARS-CoV-2 pandemic. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 1141-1147. https://doi.org/10.4995/HEAd21.2021.13056OCS1141114

Imágenes ópticas y su tratamiento en el control de calidad de alimentos

Los controles y la legislación que actualmente garantizan una seguridad alimentaria a los consumidores se hallan en continua evolución y desarrollo, buscando siempre nuevas formas de detectar actividades fraudulentas que pueden suponer un riesgo para la salud de las personas. Ello es debido a que estas actividades, que buscan un beneficio económico sin reparar en los daños sociales que pueden causar, también buscan nuevas maneras de esquivar estos controles. Los fraudes cometidos afectan, sobre todo, a la composición de los alimentos, de manera que puedan contener agentes, materiales o productos químicos capaces de ocasionar, a corto o largo plazo, daños en los consumidores finales del producto. A pesar de que no todos los fraudes suponen un riesgo para la salud, los consumidores exigen que la calidad de los productos que adquieren tenga unos estándares elevados. Es en este punto donde se producen multitud de fraudes, que juegan con la calidad de los productos que se hallan en el mercado. Así, por ejemplo, es posible que un producto en cuya etiqueta se indique la marca “Denominación de Origen” realmente no se adapte a las características que esta marca exige..

Intelligent photo processing to detect food adulterations

Many consumers suffer adverse reactions to foods, which can be defined as abnormal responses, known as food intolerances or food allergies, to their ingestion. While intolerances affect a high percentage of the population, allergies are suffered by only 5-10% of consumers, and their results can be fatal (Onyimba et al., 2021). Although the food industry indicates on its products the possible allergens that can be found in a particular food, it is possible that fraud, adulteration, or contamination may occur, endangering the health of the consumer (Alves et al., 2016; Wen & Kwon, 2016). Therefore, it is necessary to have equipment and techniques that allow a fast and reliable detection of these allergens in food.In this work, two independent studies have been carried out with the same basic food, lentil flour (gluten-free). For this purpose, samples of this food have been adulterated with two of the most common adulterants that usually appear in its labeling: nuts (pistachio powder) and gluten (wheat flour). Samples for both studies were prepared at concentrations ranging from 5 to 50 ppm. Subsequently, they have been photographed, adopting different shapes, by means of a reflex camera, obtaining a total of 1100 photographs with each adulterant. The photographs were analyzed with mathematical algorithms specialized in image processing. Specifically, residual neural networks consisting of 34 layers (ResNet34) were used for each study. Initially, the images were randomly and approximately divided into 90% to train the algorithms and 10% for validation. In other words, in the final validation of the models, 113 and 116 images were allocated for the study carried out with pistachio powder and wheat flour, respectively. After designing and training the models for each study, their validation was performed. At this point, in the study with pistachio powder the network obtained an accuracy of 99.12%, failing to classify one photograph; while in the study with wheat flour it obtained an accuracy of 96.55%, incorrectly classifying four images.In view of the results obtained in both studies, it can be concluded that the combination of optical imaging together with convolutional neural networks constitutes a simple, reliable, economical, and promising method for the detection of traces of nuts and, indirectly, gluten in foods

Residual neural networks to quantify traces of melamine in yogurts through image deconvolution

This work combines intelligent algorithms based on transfer learning and residual neural networks (viz., ResNet34) to process and classify optical images of pure and adulterated yogurt samples. This integration aims to detect the presence of melamine in yogurt in concentrations ranging from 1 to 10 ppm. An image database of 1888 images is used to train the ResNet34, and 212 blinded images to test and validate its performance. The optimized intelligent algorithm is able to classify the images into 21 classes considering the yogurt type and melamine content, obtaining an accuracy of over 94%. These encouraging results certify a simple yet powerful real-time quality control method for producers and distributers to ensure food safety for the final consumers, while pinpointing the source of potential fraudulent procedures.This work has been partially funded by the FEI program of the Complutense University of Madrid under the project reference FEI 20/19.Peer reviewe

Is my food safe? – AI-based classification of lentil flour samples with trace levels of gluten or nuts

An artificial intelligence-based method to rapidly detect adulterated lentil flour in real time is presented. Mathematical models based on convolutional neural networks and transfer learning (viz., ResNet34) have been trained to identify lentil flour samples that contain trace levels of wheat (gluten) or pistachios (nuts), aiding two relevant populations (people with celiac disease and with nut allergies, respectively). The technique is based on the analysis of photographs taken by a simple reflex camera and further classification into groups assigned to adulterant type and amount (up to 50 ppm). Two different algorithms were trained, one per adulterant, using a total of 2200 images for each neural network. Using blind sets of data (10% of the collected images; initially and randomly separated) to evaluate the performance of the models led to strong performances, as 99.1% of lentil flour samples containing ground pistachio were correctly classified, while 96.4% accuracy was reached to classify the samples containing wheat flour.Peer reviewe

Single-digit ppm quantification of melamine in powdered milk driven by computer vision

In this work, a method to detect and quantify melamine in three different powdered milks is presented. The goal has been achieved by training convolutional neural networks (CNN) with a photograph database. The three types of milk are of different brands, each with their own fat content and intended final consumer (age-based distinction). The adulterated samples were prepared by weighing and adding trace amounts of melamine, even reaching samples that are considered to be “melamine-free”. A total of 3100 images were taken to develop the CNNs (100 images per group to be classified). Specifically, a ResNet34 model architecture has been used to carry out the classification. For this deep learning approach, the images were randomly divided into two main sets: 90% for the training-validation phase of the CNN and 10% to serve as a blind test. The optimized model showed an overall accuracy of 98.7% during the validation phase, while leading to a 3.0% misclassification rate during blind testing, denoting the effectiveness of the application as a quality and safety control method for the milk industry.This work has been partially funded by the FEI program of the Complutense University of Madrid under the project reference FEI 20/19, FEI 18/10, and FEI-EU-17-03.Peer reviewe

Optical imaging to design convolutional systems to ensure quality and safety food

La adulteración de alimentos es una preocupación universal que afecta seriamente a la calidad y seguridad alimentaria en todo el mundo. Fundamentalmente, esta actividad persigue el beneficio económico. Por lo que los alimentos que sufren en mayor medida este tipo de actividades son aquellos con un precio de venta más elevado.Entre los productos alimentarios más vulnerables se encuentra el azafrán. Esta especia se produce a partir de los estigmas secos de la flor Crocus sativus L., y es considerada una de las especias más caras del mundo. Su precio de venta viene condicionado por la mano de obra que requiere y su limitada producción (Li et al., 2020). Como especia, el azafrán es un producto muy empleado en cocina como colorante y saborizante en alimentos. Además, se utiliza en medicina tradicional, por sus efectos beneficiosos para la salud. Con el fin de reducir el coste de esta especia, y así conseguir un mayor beneficio económico, han aparecido una variedad de productos fraudulentos y de calidad inferior. Estos productos se basan en la sustitución del azafrán por materias vegetales con una apariencia similar.Hasta el momento, se han desarrollado técnicas analíticas, como la cromatografía, y espectroscópicas para detectar la adulteración del azafrán con otras plantas en niveles del orden de 200 mg/g (Shawky et al., 2020). En esta línea, es necesario desarrollar técnicas alternativas fiables, rápidas, baratas, así como no invasivas para medir la autenticidad del azafrán.En este trabajo se presentan dos nuevas herramientas para detectar fraudes en el sector del azafrán. Por un lado, se emplea la espectroscopia UV-VIS, y por otro, la combinación de imágenes ópticas y algoritmos inteligentes. (Pradana-López et al., 2021). Las distintas muestras se prepararon mezclando azafrán puro de temporada con azafrán pasado, en concentraciones del 20% al 40%. Además, se prepararon muestras de azafrán puro mezclado con restos florales de la propia planta en concentraciones del 7,5% al 12,5%. Las muestras ya preparadas se midieron mediante espectroscopia UV-VIS de emisión, obteniendo resultados prometedores en la distinción de azafrán puro del adulterado. Posteriormente, se fotografiaron las muestras preparadas con un microscopio y una cámara réflex para confeccionar los modelos inteligentes. Los resultados en la validación del modelo desarrollado ascienden al 90%. Por todo esto, estas técnicas representan vías prometedoras para la detección de fraudes en el azafrán