Objectively Predicting Ultimate Quality of Post-Rigor Pork Musculature: II. Practical Classification Method on the Cutting-Line

S. T. Joo, R. G. Kauffman, R. D. Warner, C. Borggaard, J. M. Stevenson-Barry, M. S. Rhee, G. B. Park, B. C. Kim

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

To investigate the practical assessing method of pork quality, 302 carcasses were selected randomly to represent commercial conditions and were probed at 24 hr postmortem (PM) by Danish Meat Quality Marbling (MQM), Hennessy Grading Probe (HGP), Sensoptic Resistance Probe (SRP) and NWK pH-K21 meter (NpH). Also, filter paper wetness (FPW), lightness (L*), ultimate pH (pHu), subjective color (SC), firmness/wetness (SF) and marbling scores (SM) were recorded. Each carcass was categorized as either PSE (pale, soft and exudative), RSE (Reddish-pink, soft and exudative), RFN (reddish-pink, firm and non-exudative) or DFD (dark, firm and dry). When discriminant analysis was used to sort carcasses into four quality groups the highest proportion of correct classes was 65% by HOP 60% by MQM 52% by NpH and 32% by SRP. When independent variables were combined to sort carcasses into groups the success was only 67%. When RSE and RFN groups were merged so that there were only three groups (PSE, RSE+RFN, DFD) differentiating by color MQM was able to sort the same set of data into the new set of three groups with 80% accuracy. The proportions of correct classifications for HGP, NpH and SRP were 75%, 61% and 35% respectively. There was a decline in predication accuracy when only two groups, exudative (PSE and RES) and non exudative (RFN and DFD) were sorted. However when two groups designated PSE and non-PSE (RSE, RFN, and DFD) were sorted then the proportion of correct classification by MQM, HGP, SRP and NpH were 87%, 81%, 71% and 66% respectively. Combinations of variables only increased the prediction accuracy by 1 or 2% over prediction by MQM alone. When the data was sorted into three marbling groups based on SM this was not well predicted by any of the probe measurements. The best prediction accuracy was 72% by a combination of MQM and NpH.

Original languageEnglish
Pages (from-to)77-85
Number of pages9
JournalAsian-Australasian Journal of Animal Sciences
Volume13
Issue number1
DOIs
Publication statusPublished - 2000 Jan

Keywords

  • Marbling
  • Objective Prediction
  • PSE
  • Pork Quality
  • RSE

ASJC Scopus subject areas

  • Food Science
  • Animal Science and Zoology
  • Engineering(all)

Fingerprint Dive into the research topics of 'Objectively Predicting Ultimate Quality of Post-Rigor Pork Musculature: II. Practical Classification Method on the Cutting-Line'. Together they form a unique fingerprint.

  • Cite this