Insausti Barrenetxea, Kizkitza
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Insausti Barrenetxea
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Kizkitza
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Agronomía, Biotecnología y Alimentación
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IS-FOOD. Research Institute on Innovation & Sustainable Development in Food Chain
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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 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.