SISTEMA DE ILUMINACIÓN Y AISLAMIENTO PARA ADQUISICIÓN IMÁGENES DE CONTROL DE CALIDAD DEL JITOMATE (LIGHTING AND INSULATION SYSTEM FOR ACQUISITION QUALITY CONTROL IMAGES OF THE TOMATO)

ResumenEl estudio de calidad de jitomate ha tomado relevancia por su contenido de antioxidantes para combatir enfermedades, esto permite la creación de diferentes productos derivados de esta hortaliza. En este trabajo se presenta una comparación de los sistemas de aislamiento e iluminación para la adquisición de imágenes del jitomate. El método empleado para el desarrollo de este fue integrado por tres fases que son: (1) la de selección de la plataforma de procesamiento, (2) la implementación de los subsistemas de aislamiento e iluminación, (3) la del algoritmo de adquisición de datos. Las dos configuraciones iluminaciones utilizadas en este trabajo fue la de geometría de anillo y difusa. Una parte esencial para una buena operación del sistema de adquisición de imágenes es la configuración de iluminación difusa. Se identificó que la mejor iluminación para la adquisición de imágenes del jitomate fue la de iluminación difusa. Cabe resaltar que la elección de la Raspberry Pi 3 facilitó el desarrollo del algoritmo por el uso del lenguaje de programación Python y las librerías de OpenCV.Palabra(s) Clave: Imagen, Iluminación, Jitomate AbstractThe tomato quality study has become relevant for its antioxidant content to fight diseases, this allows the creation of different products derived from this vegetable. This paper presents a comparison of the isolation and lighting systems for the acquisition of tomato images. The method used for the development of this was integrated by three phases that are: (1) the selection of the processing platform, (2) the implementation of the insulation and lighting subsystems, (3) that of the acquisition algorithm of data. The two lighting configurations used in this work were that of ring and diffuse geometry. An essential part for a good operation of the image acquisition system is the diffuse lighting configuration. It was identified that the best lighting for the acquisition of tomato images was diffuse lighting. It should be noted that the choice of the Raspberry Pi 3 facilitated the development of the algorithm by the use of the Python programming language and OpenCV libraries.Keywords: Image, Lighting, Tomat

Current Status of Optical Systems for Measuring Lycopene Content in Fruits: Review

Optical systems are used for analysing the internal composition and the external properties in food. The measurement of the lycopene content in fruits and vegetables is important because of its benefits to human health. Lycopene prevents cardiovascular diseases, cataracts, cancer, osteoporosis, male infertility, and peritonitis. Among the optical systems focused on the estimation and identification of lycopene molecule are high-performance liquid chromatography (HPLC), the colorimeter, infrared near NIR spectroscopy, UV-VIS spectroscopy, Raman spectroscopy, and the systems of multispectral imaging (MSI) and hyperspectral imaging (HSI). The main objective of this paper is to present a review of the current state of optical systems used to measure lycopene in fruits. It also reports important factors to be considered in order to improve the design and implementation of those optical systems. Finally, it was observed that measurements with HPLC and spectrophotometry present the best results but use toxic solvents and require specialized personnel for their use. Moreover, another widely used technique is colorimetry, which correlates the lycopene content using color descriptors, typically those of CIELAB. Likewise, it was identified that spectroscopic techniques and multispectral images are gaining importance because they are fast and non-invasive

A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

Sweet bell peppers are a Solanaceous fruit belonging to the Capsicum annuum L. species whose consumption is popular in world gastronomy due to its wide variety of colors (ranging green, yellow, orange, red, and purple), shapes, and sizes and the absence of spicy flavor. In addition, these fruits have a characteristic flavor and nutritional attributes that include ascorbic acid, polyphenols, and carotenoids. A quality criterion for the harvest of this fruit is maturity; this attribute is visually determined by the consumer when verifying the color of the fruit’s pericarp. The present work proposes an artificial vision system that automatically describes ripeness levels of the bell pepper and compares the Fuzzy logic (FL) and Neuronal Networks for the classification stage. In this investigation, maturity stages of bell peppers were referenced by measuring total soluble solids (TSS), ° Brix, using refractometry. The proposed method was integrated in four stages. The first one consists in the image acquisition of five views using the Raspberry Pi 5 Megapixel camera. The second one is the segmentation of acquired image samples, where background and noise are removed from each image. The third phase is the segmentation of the regions of interest (green, yellow, orange and red) using the connect components algorithm to select areas. The last phase is the classification, which outputs the maturity stage. The classificatory was designed using Matlab’s Fuzzy Logic Toolbox and Deep Learning Toolbox. Its implementation was carried out onto Raspberry Pi platform. It tested the maturity classifier models using neural networks (RBF-ANN) and fuzzy logic models (ANFIS) with an accuracy of 100% and 88%, respectively. Finally, it was constructed with a content of ° Brix prediction model with small improvements regarding the state of art

Diet quality index as a predictor of treatment efficacy in overweight and obese adolescents: The EVASYON study

The EVASYON Study Group.[Background & aim]: A diet quality index (DQI) is a tool that provides an overall score of an individual's dietary intake when assessing compliance with food-based dietary guidelines. A number of DQIs have emerged, albeit their associations with health-related outcomes are debated. The aim of the present study was to assess whether adherence to dietary intervention, and the overall quality of the diet, can predict body composition changes.[Methods]: To this purpose, overweight/obese adolescents (n = 117, aged: 13–16 years; 51 males, 66 females) were recruited into a multi-component (diet, physical activity and psychological support) family-based group treatment programme. We measured the adolescents' compliance and body composition at baseline and after 2 months (intensive phase) and 13 months (extensive phase) of follow-up. Also, at baseline, after 6 months, and at the end of follow-up we calculated the DQI.[Results]: Global compliance with the dietary intervention was 37.4% during the intensive phase, and 14.3% during the extensive phase. Physical activity compliance was 94.1% at 2-months and 34.7% at 13months and psychological support compliance were growing over the intervention period (10.3% intensive phase and 45.3% during extensive phase). Adolescents complying with the meal frequency criteria at the end of the extensive phase had greater reductions in FMI z-scores than those did not complying (Cohen's d = 0.53). A statistically significant association was observed with the diet quality index. DQI-A variation explained 98.1% of BMI z-score changes and 95.1% of FMI changes.[Conclusions]: We conclude that assessment of changes in diet quality could be a useful tool in predicting body composition changes in obese adolescents involved in a diet and physical activity intervention programme backed-up by psychological and family support.The study was supported by the Ministry of Health, Social Services and Equality via the Carlos III Institute of Health (FIS Grant PI051080, PI051579). The EVASYON study received the award for the best applied research project in 2009 from AESAN (Spanish Agency for Food Safety and Nutrition from the Spanish Ministry of Health and Consumer Affairs. The study was supported by Aragon's Regional Government (DGA, Diputación General de Aragón) and European Regional Development Fund.Peer reviewe

Combined dark matter searches towards dwarf spheroidal galaxies with Fermi-LAT, HAWC, H.E.S.S., MAGIC, and VERITAS

Cosmological and astrophysical observations suggest that 85\% of the total matter of the Universe is made of Dark Matter (DM). However, its nature remains one of the most challenging and fundamental open questions of particle physics. Assuming particle DM, this exotic form of matter cannot consist of Standard Model (SM) particles. Many models have been developed to attempt unraveling the nature of DM such as Weakly Interacting Massive Particles (WIMPs), the most favored particle candidates. WIMP annihilations and decay could produce SM particles which in turn hadronize and decay to give SM secondaries such as high energy $\gamma$ rays. In the framework of indirect DM search, observations of promising targets are used to search for signatures of DM annihilation. Among these, the dwarf spheroidal galaxies (dSphs) are commonly favored owing to their expected high DM content and negligible astrophysical background. In this work, we present the very first combination of 20 dSph observations, performed by the Fermi-LAT, HAWC, H.E.S.S., MAGIC, and VERITAS collaborations in order to maximize the sensitivity of DM searches and improve the current results. We use a joint maximum likelihood approach combining each experiment's individual analysis to derive more constraining upper limits on the WIMP DM self-annihilation cross-section as a function of DM particle mass. We present new DM constraints over the widest mass range ever reported, extending from 5 GeV to 100 TeV thanks to the combination of these five different $\gamma$-ray instruments