López Maestresalas, Ainara
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López Maestresalas
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Ainara
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IS-FOOD. Research Institute on Innovation & Sustainable Development in Food Chain
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Publication Open Access On-site identification of esca-affected vines using hyperspectral imaging(Hellenic Society of Agricultural Engineers, 2025) León Ecay, Sara; Ruiz de Gauna González, Jon; López Maestresalas, Ainara; Jarén Ceballos, Carmen; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Institute on Innovation and Sustainable Development in Food Chain - ISFOODEsca represents one of the greatest threats to modern viticulture as it causes large annual economic losses. At present, there is a lack of effective strategies for disease control, so a technique capable of detecting affected vines would allow annual monitoring of disease incidence in the vineyard leading to a better crop management and decision making. This study evaluates close-range hyperspectral imaging for the detection of esca naturally infected vines. Images of 11 vines of the Tempranillo variety grown on plots in Bodegas Otazu, in Etxauri (Navarre, Spain) were acquired. A Specim IQ snapshot hyperspectral camera was used to record the images on August, 21 2023 on the field under natural light conditions. The camera has a spectral resolution of 7 nm (204 wavelengths) and a spatial resolution of 512 x 512 in the 400 ¿ 1000 nm spectral range (Vis-NIR). An individual image was acquired for each vine, of which 9 were symptomatic and 2 asymptomatic. Three classes were analysed: asymptomatic leaves of asymptomatic vines (Class 1), asymptomatic leaves of symptomatic vines (Class 2) and asymptomatic areas of symptomatic leaves of symptomatic vines (Class 3). A total of 300 pixels were randomly selected, 100 per class, for further analysis. Partial Least Square Discriminant Analysis (PLSDA) was used to classify the pixels into the three categories. An accuracy of 86% was achieved in the cross-validation dataset. Models were externally validated using an image of an asymptomatic vine and an image of a symptomatic vine. The visualisation of the images showed that the majority of the pixels of the asymptomatic vine image were classified as class 1, while most of the pixels of the symptomatic vine image were classified as either class 2 or class 3. Hence, this study demonstrated the potential of close-range HSI for the on-site detection of esca.Publication Open Access Análisis espacio-temporal de los accidentes mortales con tractor en España durante el período 2010-2019(Interempresas Media, 2023) Arazuri Garín, Silvia; Ibarrola, Alicia; Mangado Ederra, Jesús; Adin Urtasun, Aritz; Arnal Atarés, Pedro; López Maestresalas, Ainara; Jarén Ceballos, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Ingeniería; IngeniaritzaEl sector agrario y el de la construcción son los que presentan los índices de incidencia de accidentes de trabajo mortales más altos de nuestro país, según los datos recogidos por el Instituto Nacional de Seguridad y Salud en el Trabajo (INSST) (2021) dependiente del Ministerio de Trabajo y Economía Social (Cirauqui, 2022). Si tenemos en cuenta la evolución de estos índices, el sector agrario es el único que no ha mejorado dicho índice desde la aparición de la Ley 31/1995 de prevención de riesgos laborales y su siniestralidad continúa aumentando (Fundación Mapfre 2020). Pero, ¿qué ocurre cuando el accidente lo sufren personas que no encajan en la definición legal de trabajador? Estos accidentes no son considerados 'accidente de trabajo' y, por tanto, escapan a todas las estadísticas y datos oficiales del INSST. Este suele ser el caso de muchos accidentes que sufren personas jubiladas, menores de 16 años, familiares colaboradores, etc. que no son personas vinculadas a la actividad laboral tal y como se define en la legislación. Según Arana et al. (2010) de un total de 388 accidentes mortales ocurridos en España con maquinaria agrícola durante los años 2004-2008, solamente el 61,85% de ellos tuvieron carácter oficial. Las personas mayores fueron el sector de la población con un mayor riesgo, seguidos de los niños y las personas ajenas al sector agrario. La mayoría de las muertes fueron debidas al vuelco de tractores sin estructuras de protección.Publication Restricted Análisis de una explotación de vacuno de carne de Etxauri y estudio de diferentes alternativas de producción.(2006) López Maestresalas, Ainara; Escuela Técnica Superior de Ingenieros Agrónomos; Nekazaritza Ingeniarien Goi Mailako Eskola TeknikoaPublication Open Access Imágenes hiperespectrales para el estudio de la respuesta a los estreses abióticos (deficiencia de riego y abonado) de distintos cultivares de patata(Ediciones de Horticultura, 2021) López Maestresalas, Ainara; Jarén Ceballos, Carmen; Pérez Roncal, Claudia; Ruiz de Galarreta, José Ignacio; Álvarez, Alba; Barandalla, Leire; Arazuri Garín, Silvia; Ingeniería; IngeniaritzaEl objetivo de este trabajo fue evaluar el potencial de las imágenes hiperespectrales para clasificar tubérculos sometidos a estreses abióticos controlados.Publication Open Access Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging(Elsevier, 2016) López Maestresalas, Ainara; Keresztes, Janos C.; Goodarzi, Mohammad; Arazuri Garín, Silvia; Jarén Ceballos, Carmen; Saeys, Wouter; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaBlackspot is a subsurface potato damage resulting from impacts during harvesting. This type of bruising represents substantial economic losses every year. As the tubers do not show external symptoms, bruise detection in potatoes is not straightforward. Therefore, a nondestructive and accurate method capable of identifying bruised tubers is needed. Hyperspectral imaging (HSI) has been shown to be able to detect other subsurface defects such as bruises in apples. This method is nondestructive, fast and can be fully automated. Therefore, its potential for non-destructive detection of blackspot in potatoes has been investigated in this study. Two HSI setups were used, one ranging from 400 to 1000 nm, named VisibleNear Infrared (Vis-NIR) and another covering the 1000e2500 nm range, called Short Wave Infrared (SWIR). 188 samples belonging to 3 different varieties were divided in two groups. Bruises were manually induced and samples were analyzed 1, 5, 9 and 24 h after bruising. PCA, SIMCA and PLS-DA were used to build classifiers. The PLS-DA model performed better than SIMCA, achieving an overall correct classification rate above 94% for both hyperspectral setups. Furthermore, more accurate results were obtained with the SWIR setup at the tuber level (98.56 vs. 95.46% CC), allowing the identification of early bruises within 5 h after bruising. Moreover, the pixel based PLS- DA model achieved better results in the SWIR setup in terms of correctly classified samples (93.71 vs. 90.82% CC) suggesting that it is possible to detect blackspot areas in each potato tuber with high accuracy.Publication Embargo Using portable visible and near-infrared spectroscopy to authenticate beef from grass, barley, and corn-fed cattle(Elsevier, 2024-12-01) León Ecay, Sara; López-Campos, Óscar; López Maestresalas, Ainara; Insausti Barrenetxea, Kizkitza; Schmidt, Bryden; Prieto, Nuria; 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; Gobierno de Navarra / Nafarroako GobernuaMeat product labels including information on livestock production systems are increasingly demanded, as consumers request total traceability of the products. The aim of this study was to explore the potential of visible and near-infrared spectroscopy (Vis-NIRS) to authenticate meat and fat from steers raised under different feeding systems (barley, corn, grass-fed). In total, spectra from 45 steers were collected (380-2,500 nm) on the subcutaneous fat and intact longissimus thoracis (LT) at 72 h postmortem and, after fabrication, on the frozen-thawed ground longissimus lumborum (LL). In subcutaneous fat samples, excellent results were obtained using partial least squares-discriminant analysis (PLS-DA) with the 100 % of the samples in external Test correctly classified (Vis, NIR or Vis-NIR regions); whereas linear-support vector machine (L-SVM) discriminated 75-100 % in Test (Vis-NIR range). In intact meat samples, PLS-DA segregated 100 % of the samples in Test (Vis-NIR region). A slightly lower percentage of meat samples were correctly classified by L-SVM using the NIR region (75-100 % in Train and Test). For ground meat, 100 % of correctly classified samples in Test was achieved using Vis, NIR or Vis-NIR spectral regions with PLS-DA and the Vis with L-SVM. Variable importance in projection (VIP) reported the influence of fat and meat pigments as well as fat, fatty acids, protein, and moisture absorption for the discriminant analyses. From the results obtained with the animals and diets used in this study, NIRS technology stands out as a reliable and green analytical tool to authenticate fat and meat from different livestock production systems.Publication Open Access Prediction of main potato compounds by NIRS(AIDIC, 2017) López Maestresalas, Ainara; Pérez Roncal, Claudia; Tierno, Roberto; Arazuri Garín, Silvia; Ruiz de Galarreta, José Ignacio; Jarén Ceballos, Carmen; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaPotato (Solanum tuberosum, L) compounds are generally determined by analytical methods including gasliquid chromatography (GLC), HPLC and UV-VIS spectrophotometry. These methods require a lot of time and are destructive. Therefore, they seem to be not suitable for in-line applications in the food industry. Nearinfrared spectroscopy (NIRS) is a technique that presents some advantages over reference methods for quantitative analysis of agricultural and food products since it is fast, reliable and non-destructive. For this reason, in this study, quantitative analyses were carried out to determine main compounds in potatoes using NIRS. Potato tubers grown in two consecutive years were used for the analyses. NIR spectral acquisition was acquired on lyophilized samples. In year 1, a total of 135 samples were used while 228 samples were used in year 2. Lyophilized samples were also scanned by NIRS, two replicates per samples were acquired and the mean spectrum of each sample was used for the analysis. Different chemical analyses were carried out each year. Thus, in year 1 the following parameters were quantified: reducing sugars (RS) and nitrogen (N), whereas in year 2, total soluble phenolics (TSP) and hydrophilic antioxidant capacity (HAC) were extracted and quantified. Then, chemometric analyses were performed using Unscrambler X (version 10.3, CAMO software AS, Oslo, Norway) to correlate wet chemical analysis with spectral data. Quantitative analyses based on PLS regression models were developed in order to predict the above chemical compounds of tubers in a non-destructive manner. Good PLS regression models were obtained for the prediction of nitrogen and TSP with coefficients of determination (R2) above 0.83. Moreover, PLS models obtained for the estimation of HAC could be used for screening and approximate calibrations.Publication Open Access Hyperspectral imaging using notions from type-2 fuzzy sets(Springer, 2019) López Maestresalas, Ainara; Miguel Turullols, Laura de; López Molina, Carlos; Arazuri Garín, Silvia; Bustince Sola, Humberto; Jarén Ceballos, Carmen; Ingeniería; Ingeniaritza; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaFuzzy set theory has developed a prolific armamentarium of mathematical tools for each of the topics that has fallen within its scope. One of such topics is data comparison, for which a range of operators has been presented in the past. These operators can be used within the fuzzy set theory, but can also be ported to other scenarios in which data are provided in various representations. In this work, we elaborate on notions for type-2 fuzzy sets, specifically for the comparison of type-2 fuzzy membership degrees, to create function comparison operators. We further apply these operators to hyperspectral imaging, in which pixelwise data are provided as functions over a certain energy spectra. The performance of the functional comparison operators is put to the test in the context of in-laboratory hyperspectral image segmentation.Publication Open Access Análisis de la siniestralidad por vuelco de tractor en el período 2017-2021(Interempresas Media, 2023) Casuso, G.; Arazuri Garín, Silvia; Mangado Ederra, Jesús; López Maestresalas, Ainara; Arnal Atarés, Pedro; Jarén Ceballos, Carmen; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOODDesde que en 1995 se promulgó la Ley 31/1995 (BOE nº269/1995) de Prevención de Riesgos Laborales se ha observado una disminución importante de la siniestralidad laboral en todos los sectores. Sin embargo, en el sector agrario esta disminución no ha sido del calado de otros sectores y, por ejemplo, los índices de mortalidad prácticamente se mantienen en el tiempo. Tal es así, que el sector agrario ha sido calificado por el Observatorio Estatal de Condiciones de Trabajo (OECT) como prioritario para el desarrollo de políticas preventivas eficientes que permitan reducir la siniestralidad en el sector en base al índice de accidentes graves y mortales ajustado por la población afiliada.Publication Open Access 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 - ISFOODThis 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.