Beriain Apesteguía, María José
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Beriain Apesteguía
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María José
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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 Effects of chitosan coating with green tea aqueous extract on lipid oxidation and microbial growth in pork chops during chilled storage(MDPI, 2020) Montaño Sánchez, Eduardo; Torres Martínez, Brisa del Mar; Vargas Sánchez, Rey David; Huerta Leidenz, Nelson; Beriain Apesteguía, María José; Institute on Innovation and Sustainable Development in Food Chain - ISFOODLipid oxidation and microbial growth are the major causes of meat quality deterioration. Natural ingredients in meat products have been proposed as a strategy to prevent quality deterioration during cold storage. This study aimed to assess the effects of added chitosan coating, alone and in combination with green tea water extract (GTWE), on the quality of pork chops during prolonged cold storage. For evaluating oxidative and antimicrobial stabilities, 72 fresh pork samples were subjected to four treatments (n = 18 per treatment): T0 (non-coated chops without GTWE); T1 (chitosan-coated chops without GTWE); T2 (chitosan-coated chops plus 0.1% of GTWE); and T3 (chitosan-coated chops plus 0.5% of GTWE). Pork samples were stored at 0 °C and subjected to physicochemical evaluation (pH, colour, and lipid oxidation) and microbiological analyses (mesophilic and pyschrotrophic counts) at 0, 5, 10, 15, 20 and 25 days of storage. GTWE presented high total phenolic content (> 500 mg gallic acid equivalents/g); the incorporation of chitosan coatings increased (p < 0.05) free radical scavenging activity (FRSA, >90% of inhibition) and microbial growth inhibition (>50% for all tested pathogens), depending on the concentration. Further, GTWE inclusion in pork samples (T2 and T3) reduced (p < 0.05) pH, lipid oxidation and microbial counts, as well as colour loss in meat and bone throughout storage. Chitosan coating with GTWE could be used as an additive for the preservation of pork meat products.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.