Dpto. Ingeniería - Ingeniaritza Saila [desde mayo 2018 / 2018ko maiatzetik]
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Publication 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 PublikoaThe 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.Publication Open Access Classification of beef longissimus thoracis muscle tenderness using hyperspectral imaging and chemometrics(MDPI, 2022) León Ecay, Sara; López Maestresalas, Ainara; Murillo Arbizu, María Teresa; Beriain Apesteguía, María José; Mendizábal Aizpuru, José Antonio; Arazuri Garín, Silvia; Jarén Ceballos, Carmen; Bass, Phillip D.; Colle, Michael J.; García, David; Romano Moreno, Miguel; Insausti Barrenetxea, Kizkitza; Agronomia, Bioteknologia eta Elikadura; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Agronomía, Biotecnología y Alimentación; Ingeniería; Gobierno de Navarra / Nafarroako Gobernua Universidad Pública de Navarra / Nafarroako UnibertsitateNowadays, the meat industry requires non-destructive, sustainable, and rapid methods that can provide objective and accurate quality assessment with little human intervention. Therefore, the present research aimed to create a model that can classify beef samples from longissimus thoracis muscle according to their tenderness degree based on hyperspectral imaging (HSI). In order to obtain different textures, two main strategies were used: (a) aging type (wet and dry aging with or without starters) and (b) aging times (0, 7, 13, 21, and 27 days). Categorization into two groups was carried out for further chemometric analysis, encompassing group 1 (ngroup1 = 30) with samples with WBSF < 53 N whereas group 2 (ngroup2 = 28) comprised samples with WBSF values 53 N. Then, classification models were created by applying the partial least squares discriminant analysis (PLS-DA) method. The best results were achieved by combining the following pre-processing algorithms: 1st derivative + mean center, reaching 70.83% of correctly classified (CC) samples and 67.14% for cross validation (CV) and prediction, respectively. In general, it can be concluded that HSI technology combined with chemometrics has the potential to differentiate and classify meat samples according to their textural characteristics.Publication 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 PublikoaThis 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.Publication 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 PublikoaThe 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.Publication Open Access Early detection of Esca disease in grapevines using in-field hyperspectral proximal sensing(Hellenic Society of Agricultural Engineers, 2025) López Maestresalas, Ainara; Ruiz de Gauna González, Jon; Jarén Ceballos, Carmen; León Ecay, Sara; Arazuri Garín, Silvia; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOODEsca is one of the most destructive vine diseases in the world. It causes significant economic losses, mainly due to reduced grape yield and quality. Currently, the approved methods of controlling esca include preventive methods such as the use of fungicides on plant wounds or the use of planting systems that do not require intensive pruning, among others. It is therefore advisable to monitor the crop to identify those vines that are susceptible to the disease. For this reason, in this study a proximal hyperspectral camera was used for early detection of esca presence in asymptomatic grapevine leaves. Images of 11 vines of the Tempranillo variety grown in Etxauri (Navarre, Spain) were analysed. Hyperspectral images were acquired using a Specim IQ snapshot camera, mounted on a tripod, working in the range of 400¿1000 nm with a spectral resolution of 7 nm (204 bands), and an image resolution of 512 × 512 pixel including an RGB camera (5 Mpix). The images were taken under natural ambient light conditions on August 21, 2023. From the 11 vines selected, 9 showed visual symptoms of esca and the remaining 2 were asymptomatic to the naked eye. A total of 200 pixels were randomly selected from the dataset, 100 from asymptomatic leaves of asymptomatic vines (class 1) and 100 from asymptomatic leaves of symptomatic vines (class 2). Partial Least Square Discriminant Analysis (PLS-DA) was performed to classify the leaves into the two classes. Classification rates of 97% were achieved in the cross-validation dataset. Models were externally validated at pixel-level using one image of an asymptomatic vine and another of a symptomatic vine. The visualisation of the images confirmed the correct classification of the pixels into the two classes, indicating that by using proximal hyperspectral sensing an early identification of the disease is possible.Publication Open Access Effects of innovative long-term soil and crop management on topsoil properties of a mediterranean soil based on detailed water retention curves(European Geosciences Union, 2022) Aldaz Lusarreta, Alaitz; Giménez Díaz, Rafael; Campo-Bescós, Miguel; Arregui Odériz, Luis Miguel; Virto Quecedo, Íñigo; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ciencias; Zientziak; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Gobierno de Navarra / Nafarroako GobernuaThe effectiveness of conservation agriculture (CA) and other soil management strategies implying a reduction of tillage has been shown to be site-dependent (crop, clime and soil), and thus any new soil and crop management should be rigorously evaluated before its implementation. Moreover, farmers are normally reluctant to abandon conventional practices if this means putting their production at risk. This study evaluates an innovative soil and crop management (including no-tillage, cover crops and organic amendments) as an alternative to conventional management for rainfed cereal cropping in a calcareous soil in a semi-arid Mediterranean climatic zone of Navarra (Spain), based on the analysis of soil water retention curves (SWRCs) and soil structure. The study was carried out in a small agricultural area in the municipality of Garínoain (Navarre, Spain) devoted to rainfed cereal cropping. No other agricultural area in the whole region of Navarre exists where soil and crop management as proposed herein is practiced. Climate is temperate Mediterranean, and the dominant soil is Fluventic Haploxerept. Within the study area there is a subarea devoted to the proposed soil and crop management (OPM treatment), while there is another subarea where the soil and crop management is conventional in the zone (CM treatment). OPM includes no-tillage (18 years continuous) after conventional tillage, crop rotation, use of cover crops and occasional application of organic amendments. CM involves continuous conventional tillage (chisel plow), mineral fertilization, no cover crops and a lower diversity of crops in the rotation. Undisturbed soil samples from the topsoil and disturbed samples from the tilled layer were collected for both systems. The undisturbed samples were used to obtain the detailed SWRCs in the low suction range using a HYPROP©device. From the SWRCs, different approaches found in the literature to evaluate soil physical quality were calculated. The pore-size distribution was also estimated from the SWRCs. Disturbed samples were used in the laboratory to assess soil structure by means of an aggregate-size fractionation and to perform complementary analysis from which other indicators related to soil functioning and agricultural sustainability were obtained. The approaches evaluated did not show clear differences between treatments. However, the differences in soil quality between the two forms of management were better observed in the pore size distributions and by the analysis of the size distribution and stability of soil aggregates. There was an overabundance of macropores under CM, while the amount of mesopores (available water) and micropores were similar in both treatments. Likewise, more stable macroaggregates were observed in OPM than in CM, as well as more organic C storage, greater microbial activity, and biomass. The proposed management system is providing good results regarding soil physical quality and contributing also to the enhancement of biodiversity, as well as to the improvement in water-use efficiency. Finally, our findings suggest that the adoption of the proposed practice would not result in a loss in yields compared to conventional management.Publication Open Access Evaluation of nitrate soil probes for a more sustainable agriculture(MDPI, 2022) Bellosta Diest, Amelia; Campo-Bescós, Miguel; Zapatería Miranda, Jesús; Casalí Sarasíbar, Javier; Arregui Odériz, Luis Miguel; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Gobierno de Navarra / Nafarroako GobernuaSynthetic nitrogen (N) fertilizers and their increased production and utilization have played a great role in increasing crop yield and in meeting the food demands resulting from population growth. Nitrate (NO3−) is the common form of nitrogen absorbed by plants. It has high water solubility and low retention by soil particles, making it prone to leaching and mobilization by surface water, which can seriously contaminate biological environments and affect human health. Few methods exist to measure nitrate in the soil. The development of ion selective sensors provides knowledge about the dynamics of nitrate in the soil in real time, which can be very useful for nitrate management. The objective of this study is to analyze the performance of three commercial probes (Nutrisens, RIKA and JXCT) under the same conditions. The performance was analyzed with respect to electrical conductivity (EC) (0–50 mS/cm) and nitrate concentration in aqueous solution and in sand (0–180 ppm NO3−) at 35% volumetric soil moisture. Differences were shown among probes when studying their response to variations of the EC and, notably, only the Nutrisens probe provided coherent accurate measurements. In the evaluation of nitrate concentration in liquid solution, all probes proved to be highly sensitive. Finally, in the evaluation of all probes’ response to modifications in nitrate concentration in sand, the sensitivity decreased for all probes, with the Nutrisens probe the most sensitive and the other two probes almost insensitive.Publication Open Access Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields(Elsevier, 2023) Arias Cuenca, María; Notarnicola, Claudia; Campo-Bescós, Miguel; Arregui Odériz, Luis Miguel; Álvarez-Mozos, Jesús; 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 PublikoaSoil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE ~ 0.05 m3 /m3 ) and some others rather poor ones (ubRMSE > 0.12 m3 / m3 ). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE ~0.07 m3 /m3 ) whereas STCD produced similar results with and without vegetation correction (ubRMSE ~0.08 m3 /m3 ). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management.Publication 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 PublikoaPrecise 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.Publication 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.Publication 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 - ISFOODLos 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.Publication Open Access New methodology for wheat attenuation correction at C-Band VV-polarized backscatter time series(IEEE, 2022) Arias Cuenca, María; Campo-Bescós, Miguel; Arregui Odériz, Luis Miguel; González de Audícana Amenábar, María; Álvarez-Mozos, Jesús; Agronomia, Bioteknologia eta Elikadura; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Agronomía, Biotecnología y Alimentación; IngenieríaWheat is one of the most important crops worldwide, and thus the use of remote sensing data for wheat monitoring has attracted much interest. Synthetic Aperture Radar (SAR) observations show that, at C-band and VV polarization, wheat canopy attenuates the surface scattering component from the underlying soil during a significant part of its growth cycle. This behavior needs to be accounted for or corrected before soil moisture retrieval is attempted. The objective of this paper is to develop a new method for wheat attenuation correction (WATCOR) applicable to Sentinel-1 VV time series and based solely on the information contained in the time series itself. The hypothesis of WATCOR is that without attenuation, VV backscatter would follow a stable long-term trend during the agricultural season, with short-term variations caused by soil moisture dynamics. The method relies on time series smoothing and changing point detection, and its implementation follows a series of simple steps. The performance of the method was compared by evaluating the correlation between backscatter and soil moisture content in six wheat fields with available soil moisture data. The Water Cloud Model (WCM) was also applied as a benchmark. The results showed that WATCOR successfully removed the attenuation in the time series, and achieved the highest correlation with soil moisture, improving markedly the correlation of the original backscatter. WATCOR can be easily implemented, as it does not require parameterization or any external data, only an approximate indication of the period where attenuation is likely to occur.Publication Open Access Non-destructive spectroscopy-based technologies for meat and meat product discrimination: a review(Elsevier, 2025-10-01) León Ecay, Sara; Insausti Barrenetxea, Kizkitza; López Maestresalas, Ainara; Prieto, Nuria; Ingeniería; Ingeniaritza; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa; Gobierno de Navarra / Nafarroako GobernuaConsumers' confidence in products of animal origin is highly subjected to the quality guarantees offered by the manufacturing and retail industries. Traditionally, meat quality evaluation has been conducted through destructive, time-consuming and chemical-dependent protocols. Smart methodologies based on the non-destructiveness and/or non-contact with the samples, such as spectroscopy-based technologies, arise as an alternative promising tool. This comprehensive overview includes literature published in the last decade applying spectroscopy-based techniques in the Visible (Vis) and near-infrared (NIR) regions of the spectrum (Vis-NIR), either individually or combined with imaging (hyperspectral imaging, HSI), to classify meat and meat products based on ante- or postmortem factors. First, a brief introduction to the fundamentals of Vis-NIRS and HSI is included. Secondly, the main applications of Vis-NIRS and HSI technologies for meat qualitative purposes only are discussed. The Vis-NIRS and HSI have been successfully used in lab scale studies (> 90 % overall accuracy) to discriminate meat and meat products according to antemortem (feeding system, species, origin and breed) and postmortem (freshness, meat quality, label claims) factors. Recently, spectral data collected with handheld Vis-NIR equipment have become more frequent, although the use of portable HSI has not been widely explored. From the studies reviewed, the main concern regarding spectral data is to shorten modelling handling times, including strategies to both extract optimal wavelengths from NIR and compress spectral data from HSI. Despite the efforts made to overcome instrumentation and data processing challenges, a gap remains to be covered up to a real-time implementation in industrial line quality control.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 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.