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|metadata.artigo.dc.title:||Classification of pork quality by hierarchical cluster analysis|
|metadata.artigo.dc.creator:||Torres Filho, Robledo de Almeida|
Silva, Vanelle Maria da
Rodrigues, Lorena Mendes
Fontes, Paulo Rogério
Ramos, Alcinéia de Lemos Souza
Ramos, Eduardo Mendes
|metadata.artigo.dc.publisher:||MCB University Press|
|metadata.artigo.dc.identifier.citation:||TORRES FILHO, R. de A. et al. Classification of pork quality by hierarchical cluster analysis. British Food Journal, [S.l.], v. 120, n. 7, 2018.|
|metadata.artigo.dc.description.abstract:||Purpose The purpose of this paper is to evaluate the classification ability of pork quality by cluster analysis in relation to reference criteria proposed in the literature. Verify if clusters were theoretically significant with major pork quality categories. Verify if classificatory parameter values of quality attributes determined “a posteriori” may be used for following categorization. Design/methodology/approach In total, 60 pork loins were classified into pale, soft and exudative, reddish-pink, soft and exudative, RFN and dark, firm and dry by reference criteria and hierarchical cluster analyses were performed to identify groups of samples with different attributes, based on only pH45min and on pHu, L* and drip loss. Findings Cluster analysis divided total samples into different (p<0.05) smaller groups. Two groups were formed based on only pH45min and five groups were formed based on pHu, L* and drip loss. By these five groups, L* of 44 and 52 distinguished between dark, reddish-pink and pale meat colors and drip loss of 2 and 6 percent distinguished between dry, non-exudative and exudative meats. Cluster analyses identify pork groups with different attributes and the proposed parameters can be used to distinguish between groups theoretically similar to major pork quality categories. Originality/value To decide the best destination to pork carcass and to reduce economic losses, the correctly classify of the pork quality is decisive. This study proves that cluster analysis is able to classify pork into groups with significantly different quality attributes, which are significant with major pork quality categories, without unclassified samples.|
|Appears in Collections:||DCA - Artigos publicados em periódicos|
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