2019
DOI: 10.3390/foods8060206
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An Original Methodology for the Selection of Biomarkers of Tenderness in Five Different Muscles

Abstract: For several years, studies conducted for discovering tenderness biomarkers have proposed a list of 20 candidates. The aim of the present work was to develop an innovative methodology to select the most predictive among this list. The relative abundance of the proteins was evaluated on five muscles of 10 Holstein cows: gluteobiceps, semimembranosus, semitendinosus, Triceps brachii and Vastus lateralis. To select the most predictive biomarkers, a multi-block model was used: The Data-Driven Sparse Partial Least S… Show more

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Cited by 5 publications
(3 citation statements)
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“…Interestingly, SOD1 expression was higher in the GM than the TB. Similarly, the semimembranosus, which contains a low proportion of type I and type IIa muscle fibers, had the highest expression of SOD1 in a study of beef cattle 25 . These data in conjunction with our results suggest that SOD1 expression is higher in less oxidative muscle groups.…”
Section: Discussionmentioning
confidence: 94%
“…Interestingly, SOD1 expression was higher in the GM than the TB. Similarly, the semimembranosus, which contains a low proportion of type I and type IIa muscle fibers, had the highest expression of SOD1 in a study of beef cattle 25 . These data in conjunction with our results suggest that SOD1 expression is higher in less oxidative muscle groups.…”
Section: Discussionmentioning
confidence: 94%
“…In the third topic that groups papers dealing with statistical approaches for meat quality prediction/management, three original papers are published [11][12][13] and they are complimentary to the previous topic by addressing some of the objectives reported by Berri et al [9] for the development of prediction/management tools of beef qualities. The first study by Ellies-Oury et al [11] presenta a new methodology for the selection of protein biomarkers of tenderness in five different bovine muscles using a multi-block model: the data-driven sparse partial least square. In the same context, Gagaoua et al [12] present an innovative approach for the prediction of beef tenderness by a combination of statistical methods that are "chemometrics" and "supervised learning" to manage the integrated data of the continuum from the farm to fork and select the potential predictors of beef tenderness.…”
mentioning
confidence: 99%
“…In the third topic that groups papers dealing with statistical approaches for meat quality prediction/management, three original papers are published [11][12][13] and they are complimentary to the previous topic by addressing some of the objectives reported by Berri et al [9] for the development of prediction/management tools of beef qualities. The first study by Ellies-Oury et al [11] presenta a new methodology for the selection of protein biomarkers of tenderness in five different bovine muscles using a multi-block model: the data-driven sparse partial least square.…”
mentioning
confidence: 99%