Goenaga Uceda, Irantzu
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Goenaga Uceda
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Irantzu
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Agronomía, Biotecnología y Alimentación
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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 Authentication of cattle finishing diets (conventional vs. vegetable by-products) using near-infrared spectroscopy(Hellenic Society of Agricultural Engineers, 2025) León Ecay, Sara; López Maestresalas, Ainara; Goenaga Uceda, Irantzu; Mendizábal Aizpuru, José Antonio; Insausti Barrenetxea, Kizkitza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaThis study explored the potential of a portable near-infrared (NIR) spectrophotometer (1200 - 2200 nm; 2-nm bandwidth) to discriminate meat and carcasses from cattle fed two different diets: a control of 90% barley and 10% straw (C, n = 12) and, a ration including 37.5% of vegetable by-products (VBPR, n = 11). At 24 h postmortem spectra were collected on the exterior surface of the left carcass, first, between the 5th and 6th ribs, and afterwards, on the 12th and 13th ribs. After fabrication and 7 d of aging, spectral data was acquired from the intact steaks while keeping muscle integrity. When spectra were collected from the carcasses, partial least squares-discriminant analysis (PLS-DA) correctly classified (%CC) >66.67% in both Train and cross-validation (CV) whereas radial basis function-support vector machine (RBF-SVM) discriminated 100 - 83.33% in Train and CV, respectively, using the full spectrum. Reducing the initial matrix ((λ=501) by interval-PLS (iPLS) led into a >75% of well-sorted carcasses by finishing diet while RBFSVM increased the %CC up to >83.33%. Using both discriminant approaches, carcasses were authenticated with a subtle improvement over intact meat (>90% vs. >75% in Train and >80% vs. >65% in CV for C and VBPR, respectively). Variable importance in projection (VIP) scores showed how variables >1592 nm had higher weight in the discrimination process. The results achieved showed the potential of NIR technology as a sustainable, fast and chemical-free tool to assist the integration of the meat industry into the digital age of connectivity and digitization.