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León Ecay, Sara

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https://academica-e.unavarra.es/handle/2454/50608

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León Ecay

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Sara

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Agronomía, Biotecnología y Alimentación

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0000-0002-4868-7177

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751799

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812234

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  • Thumbnail Image
    PublicationOpen Access
    Imágenes hiperespectrales como herramienta no destructiva para evaluar la calidad de la carne y de los productos cárnicos
    (Estrategias alimentarias, 2023) León Ecay, Sara; Insausti Barrenetxea, Kizkitza; López Maestresalas, Ainara; 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
    Apostar por la digitalización del sector cárnico resulta imprescindible para mantener su competitividad. Por ello, es preciso introducir nuevas tecnologías emergentes no destructivas, objetivas y rápidas que puedan satisfacer las necesidades básicas de las modernas plantas de procesado de alimentos. En este artículo, se muestran diferentes aplicaciones de técnicas espectroscópicas haciendo especial hincapié en las imágenes hiperespectrales a través de un caso práctico.
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    PublicationOpen Access
    The water footprint of Spanish Ternera de Navarra PGI beef: conventional versus novel feeding based on vegetable by-products from the local food industry
    (Elsevier, 2024) González-Martínez, Pablo; Goenaga Uceda, Irantzu; León Ecay, Sara; Heras Rojo, Joana de las; Aldai Elkoro-Iribe, Noelia; Insausti Barrenetxea, Kizkitza; Martínez Aldaya, Maite; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ciencias; Zientziak; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    CONTEXT: In recent years, livestock farming has been in the spotlight. Meat production is blamed for the pollution of aquifers and rivers, as well as for the large amount of water required to feed livestock. This has highlighted the need to find alternative feeding systems for cattle breeding able to reduce food/feed competition. OBJECTIVE: In this context, the present study compares the water footprint (WF) of conventionally fed beef versus beef fed with vegetable by-products from the local agri-food industry. METHODS: Twenty-four entire male young bulls were reared under the Ternera de Navarra Protected Geographic Identification (PGI) in the town of Azoz, in Navarra, Spain. Twelve calves were fattened on a diet based on vegetable by-products and fodder and grain to complement the ration (VBP diet) and the remaining animals were fattened with a traditional diet based on concentrate and straw (conventional or control diet). RESULTS AND CONCLUSIONS: Once the fattening was finished and animals were slaughtered, the results showed a larger green, blue and grey WF in terms of m3 per beef cattle for conventionally fed animals compared to those fed with VBP. However, when looking at the efficiency, the results were mixed. Conventionally fed cattle exhibited lower green and grey WFs but a higher blue WF compared to VBP-fed cattle, with values of 9955 l/kg, 1577 l/kg and 1731 l/kg versus 10,147 l/kg, 1457 l/kg and 1831 l/kg of carcass beef, respectively. SIGNIFICANCE: This means that a by-product-based calf diet can reduce blue water use. However, further research is needed on the indirect water pollution associated with animal-fed crop production.
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    PublicationOpen 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 - ISFOOD
    Esca 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.
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    PublicationOpen 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.
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    PublicationOpen Access
    Vegetable by-products as alternative and sustainable raw materials for ruminant feeding: nutritive evaluation and their inclusion in a novel ration for calf fattening
    (MDPI, 2023) Goenaga Uceda, Irantzu; García-Rodríguez, Aser; Goiri, Idoia; León Ecay, Sara; Heras Rojo, Joana de las; Aldai Elkoro-Iribe, Noelia; Insausti Barrenetxea, Kizkitza; 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
    This research aimed to evaluate the nutritional composition, in vitro digestibility, and gas production kinetics of 15 vegetable by-products generated by the agri-food industry compared with corn silage as a reference raw material. Nutritional characterization and in vitro ruminal fermentation tests were performed to determine in vitro organic matter digestibility and digestible energy values, short-chain fatty acids, and the gas production profile. Results indicate that vegetable by-products were more degradable, more extensively fermented, and fermented at a faster rate than corn silage. Going one step further in the valorization of these by-products in animal feed, the second part of the research aimed to compare the novel ration designed for calf fattening with a conventional one. An artificial rumen unit was used to obtain nutrient disappearance, rumen fermentation parameters, and gas production of rumen digesta. Very slight differences were observed between both experimental rations, with their composition being the main difference. Most of the unitary vegetable by-products and all mixes, as real examples of by-product generation in the agri-food industry, have higher digestibility and a greater nutritional value than corn silage. These by-products showed the potential to be used in ruminant-ensiled rations and could replace part of the ingredients in conventional diets.
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    PublicationEmbargo
    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 Gobernua
    Meat 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.
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    PublicationOpen 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 - ISFOOD
    Esca 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.
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    PublicationOpen 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 Unibertsitate
    Nowadays, 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.