The effect of breed-production systems on the myosin heavy chain 1, the biochemical characteristics and the colour variables of Longissimus thoracis from seven Spanish beef cattle breeds

Gil, Marta; Serra, X.; Gispert, M.; Oliver, M.A.; Sañudo, C.; Doblado, Begoña Panea; Olleta, J. L.; Campo, M. M.; Oliván, Mamen; Osoro, K.; García-Cachán, M. D.; Cruz-Sagredo, Rosario; Izquierdo, M.; Espejo, Manuel Beato; Martı́n, Marga; Piedrafita, J.

doi:10.1016/s0309-1740(00)00150-9

Cited by 67 publications

(42 citation statements)

References 19 publications

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“…The first one comes from a study carried out to determine the features that entail consumer acceptance of beef meat from seven Spanish breeds [16]. Each piece of meat was described by: weight of the animal, aging time, breed, 6 physical features describing its texture and 12 sensory characteristics rated by 11 different experts (132 ratings).…”

Section: Resultsmentioning

confidence: 99%

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Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection

Luaces

Bayón

Quevedo

et al. 2004

Machine Learning: ECML 2004

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The quality of food can be assessed from different points of view. In this paper, we deal with those aspects that can be appreciated through sensory impressions. When we are aiming to induce a function that maps object descriptions into ratings, we must consider that consumers' ratings are just a way to express their preferences about the products presented in the same testing session. Therefore, we postulate to learn from consumers' preference judgments instead of using an approach based on regression. This requires the use of special purpose kernels and feature subset selection methods. We illustrate the benefits of our approach in two families of real-world data bases.

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Section: Resultsmentioning

confidence: 99%

“…We would like to thank Carlos Sañudo (Animal Production at the University of Zaragoza) for providing us with the beef meat data sets [16]; we also thank Anna Picinelli (Cider group at SERIDA, Asturias) for allowing us to use cider data sets [17].…”

Section: Acknowledgementsmentioning

confidence: 99%

Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection

Luaces

Bayón

Quevedo

et al. 2004

Machine Learning: ECML 2004

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“…The dataset used in this paper comes from a study carried out to determine the features that entail consumer acceptance of beef meat from seven Spanish breeds (Gil et al, 2001;Sañudo et al, 2004;del Coz et al, 2005;Díez et al, 2005Díez et al, , 2006Bahamonde et al, 2007). Each piece of meat was described by: the weight of the animal, ageing time, breed, 6 physical features describing its texture and 12 sensory characteristics rated by 11 different experts (132 ratings).…”

Section: Datasetsmentioning

confidence: 99%

“…According to the literature about sensory preferences of beef meat, (Gil et al, 2001;Sañudo et al, 2004;del Coz et al, 2005;Díez et al, 2005Díez et al, , 2006Bahamonde et al, 2007), the most important features that explain the preferences of consumers are ageing and intramuscular fat (intrafat for short). These are discrete features.…”

Section: Visualization Of Preferencesmentioning

confidence: 99%

Mapping preferences into Euclidean space

Luaces

Díez

Joachims

et al. 2015

Expert Systems with Applications

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Understanding and modeling human preferences is one of the key problems in applications ranging from marketing to automated recommendation. In this paper, we focus on learning and analyzing the preferences of consumers regarding food products. In particular, we explore machine learning methods that embed consumers and products in an Euclidean space such that their relationship to each other models consumer preferences. In addition to predicting preferences that were not explicitly stated, the Euclidean embedding enables visualization and clustering to understand the overall structure of a population of consumers and their preferences regarding the set of products.Notice that consumers' clusters are market segments, and products clusters can be seen as groups of similar items with respect to consumer tastes. We explore two types of Euclidean embedding of preferences, one based on inner products and one based on distances. Using a real world dataset about consumers of beef meat, we find that both embeddings produce more accurate models than a tensorial approach that uses a SVM to learn preferences. The * Corresponding author: Tel: +34 985 182 588Email addresses: oluaces@uniovi.es (Oscar Luaces), jdiez@uniovi.es (Jorge Díez), tj@cs.cornell.edu (Thorsten Joachims), abahamonde@uniovi.es (Antonio Bahamonde) Preprint submitted to Expert Systems with ApplicationsJuly 15, 2015 reason is that the number of parameters to learned in embeddings can be considerably lower than in the tensorial approach. Furthermore, we demonstrate that the visualization of the learned embeddings provides interesting insights into the structure of the consumer and product space, and that it provides a method for qualitatively explaining consumer preferences. Additionally, it is important to emphasize that the approach presented here is flexible enough to allow its use with different levels of knowledge about consumers or products; therefore the application field is very wide to grasp an accurate understanding of consumers' preferences.

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“…Among beef-eating quality attributes, tenderness is one of the most important beef quality attributes and is influenced by several factors including muscle fibre type profile, sarcomere length, pH, intra-muscular fat (IMF), rate of tenderization (Shackelford et al, 1997;Gil et al, 2001) and total amount and chemical composition of collagen (Chriki et al, 2012). Nutritional status before slaughter can influence collagen content and its solubility and consequently meat tenderness (Thénard et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Repercussions of growth path on carcass characteristics, meat colour and shear force in Alentejana bulls

Costa

Simões

Costa

et al. 2015

Animal

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The aim of this study was to evaluate the carcass and meat characteristics of eight muscles from bulls with distinct growth paths. A total of 40 Alentejana male calves were allocated to two distinct feeding regimes. In the continuous growth (CG) system, the animals were fed concentrates plus hay and were slaughtered at 18 months of age. On the other hand, in the discontinuous growth (DG) system, the animals were fed hay until 15 months of age; the cattle were then fed the same diet provided to the CG group from 15 to 24 months of age. The DG reduced hot carcass weight, fatness and dressing %, but the proportions of fat, bone and muscle tissues in the leg were not affected. In contrast, there was a positive impact of compensatory growth on supraspinatus, triceps brachii, semitendinosus, biceps femoris muscle tenderness, overcoming the negative effects of age at slaughter. The reasons for such improvement in meat tenderness were not related to intra-muscular fat content or myofibrillar protein degradation values. An association between tenderness and muscle collagen properties was not established. The results indicate that the compensatory growth has a muscle-dependent effect.

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The effect of breed-production systems on the myosin heavy chain 1, the biochemical characteristics and the colour variables of Longissimus thoracis from seven Spanish beef cattle breeds

Cited by 67 publications

References 19 publications

Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection

Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection

Mapping preferences into Euclidean space

Repercussions of growth path on carcass characteristics, meat colour and shear force in Alentejana bulls

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