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 - 8 of 8

Open Access
Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
(Elsevier, 2022) Pérez Roncal, Claudia; Arazuri Garín, Silvia; López Molina, Carlos; Jarén Ceballos, Carmen; Santesteban García, Gonzaga; López Maestresalas, Ainara; Ingeniaritza; Estatistika, Informatika eta Matematika; Agronomia, Bioteknologia eta Elikadura; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Estadística, Informática y Matemáticas; Agronomía, Biotecnología y Alimentación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Precise and reliable identification of specific plant diseases is a challenge within precision agriculture nowadays. This is the case of esca, a complex grapevine trunk disease, that represents a major threat to modern viticulture as it is responsible for large economic losses annually. The lack of effective control strategies and the complexity of esca disease expression make essential the identification of affected plants, before symptoms become evident, for a better management of the vineyard. This study evaluated the suitability of a near-infrared hyperspectral imaging (HSI) system to detect esca disease in asymptomatic grapevine leaves of Tempranillo red-berried cultivar. For this, 72 leaves from an experimental vineyard, naturally infected with esca, were collected and scanned with a lab-scale HSI system in the 900-1700 nm spectral range. Then, effective image processing and multivariate analysis techniques were merged to develop pixel-based classification models for the distinction of healthy, asymptomatic and symptomatic leaves. Automatic and interval partial least squares variable selection methods were tested to identify the most relevant wavelengths for the detection of esca-affected vines using partial least squares discriminant analysis and different pre-processing techniques. Three-class and two-class classifiers were carried out to differentiate healthy, asymptomatic and symptomatic leaf pixels, and healthy from asymptomatic pixels, respectively. Both variable selection methods performed similarly, achieving good classification rates in the range of 82.77-97.17% in validation datasets for either three-class or two-class classifiers. The latter results demonstrated the capability of hyperspectral imaging to distinguish two groups of seemingly identical leaves (healthy and asymptomatic). These findings would ease the annual monitoring of disease incidence in the vineyard and, therefore, better crop management and decision making.
Open Access
Detection of minced lamb and beef fraud using NIR spectroscopy
(Elsevier, 2019) López Maestresalas, Ainara; Insausti Barrenetxea, Kizkitza; Jarén Ceballos, Carmen; Pérez Roncal, Claudia; Urrutia Vera, Olaia; Beriain Apesteguía, María José; Arazuri Garín, Silvia; Ingeniaritza; Agronomia, Bioteknologia eta Elikadura; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Agronomía, Biotecnología y Alimentación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The aim of this work was to investigate the feasibility of near-infrared spectroscopy (NIRS), combined with chemometric techniques, to detect fraud in minced lamb and beef mixed with other types of meats. For this, 40 samples of pure lamb and 30 samples of pure beef along with 160 samples of mixed lamb and 156 samples of mixed beef at different levels: 1-2-5-10% (w/w) were prepared and analyzed. Spectral data were pre-processed using different techniques and explored by a Principal Component Analysis (PCA) to find out differences among pure and mixed samples. Moreover, a PLS-DA was carried out for each type of meat mixture. Classification results between 78.95 and 100% were achieved for the validation sets. Better rates of classification were obtained for samples mixed with pork meat, meat of Lidia breed cattle and foal meat than for samples mixed with chicken in both lamb and beef. Additionally, the obtained results showed that this technology could be used for detection of minced beef fraud with meat of Lidia breed cattle and foal in a percentage equal or higher than 2 and 1%, respectively. Therefore, this study shows the potential of NIRS combined with PLS-DA to detect fraud in minced lamb and beef.
Open Access
Identificación de síntomas previsuales de salinidad mediante imágenes hiperespectrales infrarrojas en vid
(Sociedad Española de Ciencias Hortícolas, 2022) Arazuri Garín, Silvia; Pérez Roncal, Claudia; Jarén Ceballos, Carmen; Santesteban García, Gonzaga; Marín Ederra, Diana; Miranda Jiménez, Carlos; López Maestresalas, Ainara; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ingeniería; Ingeniaritza; Institute for Multidisciplinary Research in Applied Biology - IMAB; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
Los niveles altos de salinidad que se pueden producir en un viñedo, asociados generalmente al uso de aguas de baja calidad genera un tipo de estrés abiótico que limita la producción de la uva y afecta a la calidad de los vinos. Teniendo en cuenta la importancia de la monitorización de los cultivos en la toma de decisiones para una buena gestión del viñedo, se plantea como objetivo de este trabajo la identificación previsual de síntomas de estrés abiótico en viña por medio de la tecnología de imágenes hiperespectrales en el infrarrojo cercano (HSI-NIR). Para llevar a cabo este objetivo, se realizó un ensayo en maceta en la Finca de Prácticas de la Escuela Técnica Superior de Ingeniería Agronómica y Biociencias de la UPNA. El ensayo se realizó con plantas de un año de la variedad Monastrell sobre dos portainjertos 110R y 1103P. Se establecieron dos tratamientos: control (regado con agua de riego no salina) y salinidad (agua de riego con una concentración de sal común de 1,6 g/l). Entre finales de agosto y principios de septiembre se realizaron tres muestreos de hojas, analizando un total de 600 hojas (100 hojas/tratamiento y día). Las imágenes se tomaron con una cámara hiperespectral Xeva 1.7-320-100Hz, con rango espectral 900-1700nm. Una vez procesadas las imágenes se realizó una clasificación mediante un análisis discriminante por mínimos cuadrados parciales (PLS-DA) obteniéndose un porcentaje de muestras correctamente clasificadas en su grupo de origen (control o salinidad) del 82 % el primer día de muestreo, y del 87 % a partir del segundo día. A partir de estos datos podemos concluir que es posible identificar, mediante la tecnología HSI-NIR, síntomas en plantas sometidas a un tratamiento de riego con agua salina antes de que aparezcan síntomas en las hojas.
Open Access
Potential of NIRS technology for the determination of cannabinoid content in industrial hemp (Cannabis sativa L.)
(MDPI, 2022) Jarén Ceballos, Carmen; Zambrana, P.; Pérez Roncal, Claudia; López Maestresalas, Ainara; Ábrego Arlegui, Andrés; Arazuri Garín, Silvia; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
Industrial hemp (Cannabis sativa L.) is a plant native to Asia, and is considered to be a primary source of food, textile fiber, and medicines. It is characterized by containing minimal concentrations of delta-9 tetrahydrocannabidol (THC), which is the main psychoactive chemical component, and cannabidiol (CBD), a non-psychoactive substance. In most European countries, the maximum concentration legally allowed for cultivation is 0.2% of THC, and it is currently under debate whether to increase this level to 0.3%. Moreover, in many countries its production is being regularized and legalized, increasing the need for a rapid analysis method. The present work evaluated the cannabinoid content in hemp (Cannabis sativa L.) using near infrared spectroscopy (NIRS) technology in combination with chemometric techniques. For this, several samples of the Kompolti variety were analyzed. Samples were dried and ground, and the content of total THC (%) and total CBD (%) was determined by high performance liquid chromatography (HPLC) with a diode array detector as reference measurements, and then the spectra were collected by NIRS. Principal component analysis and partial least square regression models were developed. Good coefficients of determination of cross-validation of 0.77 for THC and CBD, and a ratio of prediction to deviation >2 for total THC and CBD, were achieved. The results obtained show that NIRS technology has potential for the quantitative determination of cannabinoids. Therefore, this analytical method would allow a simpler, more robust, precise, and sustainable estimation than the current HPLC approach.
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
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 Publikoa
Potato (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.
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; Ingeniaritza
El objetivo de este trabajo fue evaluar el potencial de las imágenes hiperespectrales para clasificar tubérculos sometidos a estreses abióticos controlados.
Open Access
Hyperspectral imaging to assess the presence of powdery mildew (Erysiphe necator) in cv. Carignan Noir grapevine bunches
(MDPI, 2020) Pérez Roncal, Claudia; López Maestresalas, Ainara; López Molina, Carlos; Jarén Ceballos, Carmen; Urrestarazu Vidart, Jorge; Santesteban García, Gonzaga; Arazuri Garín, Silvia; Ingeniería; Estadística, Informática y Matemáticas; Agronomía, Biotecnología y Alimentación; Ingeniaritza; Estatistika, Informatika eta Matematika; Agronomia, Bioteknologia eta Elikadura; Gobierno de Navarra / Nafarroako Gobernua, Proyecto DECIVID (Res.104E/2017); Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, FPI-UPNA-2017 (Res.654/2017)
Powdery mildew is a worldwide major fungal disease for grapevine, which adversely affects both crop yield and produce quality. Disease identification is based on visible signs of a pathogen once the plant has already been infected; therefore, techniques that allow objective diagnosis of the disease are currently needed. In this study, the potential of hyperspectral imaging (HSI) technology to assess the presence of powdery mildew in grapevine bunches was evaluated. Thirty Carignan Noir grape bunches, 15 healthy and 15 infected, were analyzed using a lab-scale HSI system (900–1700 nm spectral range). Image processing was performed to extract spectral and spatial image features and then, classification models by means of Partial Least Squares Discriminant Analysis (PLS-DA) were carried out for healthy and infected pixels distinction within grape bunches. The best discrimination was achieved for the PLS-DA model with smoothing (SM), Standard Normal Variate (SNV) and mean centering (MC) pre-processing combination, reaching an accuracy of 85.33% in the cross-validation model and a satisfactory classification and spatial location of either healthy or infected pixels in the external validation. The obtained results suggested that HSI technology combined with chemometrics could be used for the detection of powdery mildew in black grapevine bunches.