López Maestresalas, Ainara

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López Maestresalas

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Ainara

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Ingeniería

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IS-FOOD. Research Institute on Innovation & Sustainable Development in Food Chain

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0000-0001-6636-0752

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750902

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810716

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Now showing 1 - 10 of 37

Open Access
Proyecto Agroinc: prevención del impacto ambiental de incendios provocados por cosechadoras
(Interempresas Media, 2022) Arazuri Garín, Silvia; Mangado Ederra, Jesús; López Maestresalas, Ainara; López Molina, Carlos; Angulo Muñoz, Blanca; Arnal Atarés, Pedro; Jarén Ceballos, Carmen; Ingeniería; Ingeniaritza; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Gobierno de Navarra / Nafarroako Gobernua
Las cosechadoras de cereales, por las condiciones ambientales en las que trabajan, alta temperatura y baja humedad, tanto ambiental como del producto que están cosechando, pueden provocar accidentalmente incendios durante la época de recolección. Los daños económicos y medioambientales que estos incendios suponen pueden ser muy importantes, ya que las condiciones de propagación del fuego son óptimas. Los principales objetivos de este proyecto han sido evaluar el impacto ambiental de los incendios producidos en Navarra en los últimos años y establecer una guía de buenas prácticas para su prevención.
Open Access
Combination of spectral and textural features of hyperspectral imaging for the authentication of the diet supplied to fattening cattle
(Elsevier, 2024) León Ecay, Sara; Insausti Barrenetxea, Kizkitza; Arazuri Garín, Silvia; Goenaga Uceda, Irantzu; López Maestresalas, Ainara; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
This study explored the potential of hyperspectral imaging in the near infrared region (NIR-HSI) as a non-destructive and rapid tool to discriminate among two beef fattening diets. For that purpose, a feeding trial was carried out with a total of 24 purebred Pirenaica calves. Twelve of them were fed barley and straw (BS) while 11 animals were finished on vegetable by-products (VBPR). When comparing the reference measurements of the meat coming from those animals, only the total collagen ratio expressed the feeding effect (p-value<0.05). To undertake the authentication procedure, two discrimination approaches were run: partial least squares discriminant analysis (PLS-DA) and radial basis function-support vector machine (RBF-SVM). To precisely extract spectral and textural information from the lean portion of the meat steaks, various techniques were executed, such as principal component (PC) images, competitive adaptive reweighted sampling (CARS) for selecting optimal wavelengths, and gray-level-co-occurrence matrix (GLCM). After hyperspectral imaging and the combination of their own texture features, samples were classified according to feeding diet with an overall accuracy of 72.92% for PLS-DA and 80.56% for RBF-SVM. So, the potential of using HSI technology to authenticate the meat obtained from beef supplied a diet based on circular economy techniques was made in evidence.
Open Access
Analysis of fire services coverage in Spain
(DYNA, 2018) Echeverría Iriarte, Francisco Javier; González de Audícana Amenábar, María; López Maestresalas, Ainara; Arazuri Garín, Silvia; Ciriza Labiano, Raquel; Jarén Ceballos, Carmen; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Previous analysis of the locations of fire stations in Spain and the extent of the areas they cover revealed significant deficiencies with regard to the proportion of communities who would not receive fire service intervention within a reasonable time period. This article discusses and describes the use of Geographic Information Systems and related tools to determine the areas and population covered by existing fire services within a specific response time. This response time by road, is based on a survey of fire service interventions in other European countries. The analysis compares data from a statistical study with georeferenced ones and demonstrates that the areas and communities not covered within this response time is greater than previously believed. The article then describes an analysis an alternative solution to reinforce the current fire stations network with part-time firefighters to cover the areas not covered mainly in rural and remote locations.
Open Access
A multi-year analysis of traffic accidents involving agricultural tractors
(AIDIC, 2017) Arnal Atarés, Pedro; López Maestresalas, Ainara; Arazuri Garín, Silvia; Mangado Ederra, Jesús; Jarén Ceballos, Carmen; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
The agricultural sector in Spain is responsible for a high rate of accidents every year, and many of them are traffic accidents. Tractors are a relatively rare sight on roads, meaning that the incidence of accidents involving these vehicles is relatively low, however, an above-average number of people are seriously injured or killed as a result of such accidents. Tractors are considered responsible for the majority of the occupational accidents in agriculture. Moreover, tractor overturns stand out as the principal cause of fatal accidents mainly because those accidents involved tractors without rollover protective structures (ROPS). Despite the obligation for all tractors of having a protective structure, the incidence rate of accidents with sick leave followed a rising line in the last ten years. Thus, in this study an analysis of the data of traffic accidents involving agricultural tractors in Spain, during the 2004-2013 period, is developed in order to identify the main risk factors that influence them. Official data from the “Statistical Yearbook of Accidents” published annually were used. A total of 2892 accidents were analysed. The results obtained showed that the incidence rate of both accidents and deaths were lower in accidents involving tractors than in general ones, but the consequences were more severe. In addition, the majority of accidents producing victims happened in interurban roads involving two or more vehicles. Defects in the lighting and brake systems were identified as risk of producing an accident. In the majority of the cases, the driver was the only victim of the crash. The total number of victims showed a decreasing tendency while the fatality index remained constant. The age of driver was reported to directly influence the number of accidents, with a high proportion of drivers over 45 years old. The main offences committed by drivers were related to inadequate speed and distracted driving. As much as possible we put our findings in an international context.
Open Access
Influencia de factores de cultivo y conservación en el contenido en azúcares reductores en patata
(Universidad de Sevilla, 2023) Jarén Ceballos, Carmen; Peraza Alemán, Carlos Miguel; Mangado Ederra, Jesús; López Maestresalas, Ainara; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
La patata es uno de los alimentos más importante del mundo y una de las formas más habituales de consumirla es como patatas fritas. Al freírla a altas temperaturas, los azúcares reductores y la asparagina de la patata pueden dar lugar a acrilamidas, por medio de la reacción de Maillard. La acrilamida está clasificada como sustancia probablemente cancerígena para el ser humano. Por eso es importante que las patatas destinadas a fritura tengan un bajo contenido en azúcares reductores. Este contenido depende de factores genéticos, medioambientales, culturales y condiciones de almacenamiento. Por ello, en este trabajo se pretende analizar algunos de esos factores en una variedad rica en azúcares reductores como es Jaerla. Los factores analizados fueron el estrés hídrico durante el cultivo, dos temperaturas de almacenamiento (8 y 13ºC) y tiempo de almacenamiento en las anteriores temperaturas, desde 0 hasta 13 semanas. Las muestras de patatas de cada uno de los tratamientos se liofilizaron y se determinó su contenido en azúcares: glucosa, fructosa y sacarosa. Los datos fueron analizados con R-Studio. Solo se encontraron diferencias significativas en el factor temperatura de conservación para los tres azúcares, obteniéndose los valores más altos en las patatas conservadas a 8ºC.
Open Access
Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude
(Frontiers Media, 2022) López Maestresalas, Ainara; López Molina, Carlos; Oliva Lobo, Gil Alfonso; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Peraza Alemán, Carlos Miguel; Arazuri Garín, Silvia; Ingeniaritza; Estatistika, Informatika eta Matematika; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Estadística, Informática y Matemáticas
The potato (Solanum tuberosum L.) is the world's fifth most important staple food with high socioeconomic relevance. Several potato cultivars obtained by selection and crossbreeding are currently on the market. This diversity causes tubers to exhibit different behaviors depending on the processing to which they are subjected. Therefore, it is interesting to identify cultivars with specific characteristics that best suit consumer preferences. In this work, we present a method to classify potatoes according to their cooking or frying as crisps aptitude using NIR hyperspectral imaging (HIS) combined with a Partial Least Squares Discriminant Analysis (PLS-DA). Two classification approaches were used in this study. First, a classification model using the mean spectra of a dataset composed of 80 tubers belonging to 10 different cultivars. Then, a pixel-wise classification using all the pixels of each sample of a small subset of samples comprised of 30 tubers. Hyperspectral images were acquired using fresh-cut potato slices as sample material placed on a mobile platform of a hyperspectral system in the NIR range from 900 to 1,700 nm. After image processing, PLS-DA models were built using different pre-processing combinations. Excellent accuracy rates were obtained for the models developed using the mean spectra of all samples with 90% of tubers correctly classified in the external dataset. Pixel-wise classification models achieved lower accuracy rates between 66.62 and 71.97% in the external validation datasets. Moreover, a forward interval PLS (iPLS) method was used to build pixel-wise PLS-DA models reaching accuracies above 80 and 71% in cross-validation and external validation datasets, respectively. Best classification result was obtained using a subset of 100 wavelengths (20 intervals) with 71.86% of pixels correctly classified in the validation dataset. Classification maps were generated showing that false negative pixels were mainly located at the edges of the fresh-cut slices while false positive were principally distributed at the central pith, which has singular characteristics.
Embargo
Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
(Elsevier, 2025-03-14) Peraza Alemán, Carlos Miguel; López Maestresalas, Ainara; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Barandalla, Leire; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
This study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in two seasons and processed under the same frying conditions were analysed. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR), combined with a logarithmic transformation of the acrylamide levels, were applied to develop predictive models. The most optimal outcomes for PLSR yielded R2 p: 0.85, RMSEP: 201 μg/kg and RPD: 2.53, while for SVMR yielded R2 p: 0.80, RMSEP: 229 μg/kg and RPD: 2.22. Furthermore, the selection of significant wavelengths enabled an 87.95 % reduction in variables without affecting the model’s accuracy. Finally, spatial mapping of acrylamide content was conducted on all chips in the external validation set. This method provides both quantification and visualization capabilities, thus enhancing quality control for acrylamide identification in processed potatoes.
Open Access
Applications of sensing for disease detection
(Springer, 2021) Castro, Ana Isabel de; Pérez Roncal, Claudia; Thomasson, J. Alex; Ehsani, Reza; López Maestresalas, Ainara; Yang, Chenghai; Jarén Ceballos, Carmen; Wang, Tianyi; Cribben, Curtis; Marín Ederra, Diana; Isakeit, Thomas; Urrestarazu Vidart, Jorge; López Molina, Carlos; Wang, Xiwei; Nichols, Robert L.; Santesteban García, Gonzaga; Arazuri Garín, Silvia; Peña, José Manuel; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Ingeniería; Ingeniaritza; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The potential loss of world crop production from the effect of pests, including weeds, animal pests, pathogens and viruses has been quantifed as around 40%. In addition to the economic threat, plant diseases could have disastrous consequences for the environment. Accurate and timely disease detection requires the use of rapid and reliable techniques capable of identifying infected plants and providing the tools required to implement precision agriculture strategies. The combination of suitable remote sensing (RS) data and advanced analysis algorithms makes it possible to develop prescription maps for precision disease control. This chapter shows some case studies on the use of remote sensing technology in some of the world’s major crops; namely cotton, avocado and grapevines. In these case studies, RS has been applied to detect disease caused by fungi using different acquisition platforms at different scales, such as leaf-level hyperspectral data and canopy-level remote imagery taken from satellites, manned airplanes or helicopter, and UAVs. The results proved that remote sensing is useful, effcient and effective for identifying cotton root rot zones in cotton felds, laurel wilt-infested avocado trees and escaaffected vines, which would allow farmers to optimize inputs and feld operations, resulting in reduced yield losses and increased profts.
Open Access
Convolutional neural networks for the detection of esca disease complex in asymptomatic grapevine leaves
(Springer, 2024-01-24) Carraro, Alberto; Saurio, Gaetano; López Maestresalas, Ainara; Scardapane, Simone; Marinello, Francesco; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
The Esca complex is a grapevine trunk disease that significantly threatens modern viticulture. The lack of effective control strategies and the intricacy of Esca disease manifestation render essential the identification of affected plants before symptoms become evident to the naked eye. This study applies Convolutional Neural Networks (CNNs) to distinguish, at the pixel level, between healthy, asymptomatic and symptomatic grapevine leaves of a Tempranillo red-berried cultivar using Hyperspectral imaging (HSI) in the 900¿1700 nm spectral range. We show that a 1D CNN performs semantic image segmentation (SiS) with higher accuracy than PLS-DA, one of HSI data¿s most widely used classification algorithms.
Open Access
Imágenes hiperespectrales para el estudio de la respuesta a la deficiencia de nitrógeno de distintos cultivares de patata
(Sociedad Española de Ciencias Hortícolas, 2021) López Maestresalas, Ainara; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Álvarez Morezuelas, Alba; Barandalla, Leire; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza
El cambio climático es uno de los mayores retos de la agricultura moderna. El aumento del rendimiento de los cultivos en el futuro sólo será posible si pueden hacer frente a las consecuencias del cambio climático causado por el aumento de CO2 en la atmósfera. En el cultivo de la patata es muy probable que los estreses abióticos se incrementen considerablemente comprometiendo la sostenibilidad de su producción. A largo plazo, las condiciones de elevado CO2 podrían alterar la toma y transporte de nutrientes, particularmente del nitrógeno (N). Esto conlleva la necesidad de seleccionar cultivares que por sus características genéticas, fisiológicas y agronómicas se adapten mejor a las condiciones del cambio climático global, particularmente a la eficiencia en el uso del N. Para ello, en este estudio, se ha empleado la tecnología de imágenes hiperespectrales con el objetivo de desarrollar modelos de clasificación de variedades más eficientes en el uso del N. Se han muestreado plantas de dos campos experimentales: control y con una reducción del 75% de aporte de N. Se han adquirido imágenes hiperespectrales de 120 hojas de las plantas control y 120 de plantas sometidas a una reducción del 75% de aporte de N. Se han aplicado métodos multivariantes de clasificación para comprobar el potencial de las imágenes hiperespectrales en la identificación de cultivares de patata mejor adaptados a una deficiencia de N, con resultados prometedores. Además, para evaluar la respuesta de las plantas a las diferentes dosis de N, se analizará el contenido total de N, lo que permitirá evaluar la eficiencia en el uso del N en función de la productividad, así como la concentración de metabolitos nitrogenados.