TY  - JOUR
AB  - 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.
VL  - 120
IS  - 7
SN  - 0007-070X
DO  - 10.1108/BFJ-10-2017-0589
UR  - https://doi.org/10.1108/BFJ-10-2017-0589
AU  - Torres Filho Robledo de Almeida
AU  - Silva Vanelle Maria da
AU  - Rodrigues Lorena Mendes
AU  - Fontes Paulo Rogério
AU  - Ramos Alcinéia de Lemos Souza
AU  - Ramos Eduardo Mendes
PY  - 2018
Y1  - 2018/01/01
TI  - Classification of pork quality by hierarchical cluster analysis
T2  - British Food Journal
PB  - Emerald Publishing Limited
SP  - 1446
EP  - 1456
Y2  - 2024/04/19
ER  -