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The potential for predicting purge in packaged meat using low field NMR

Abstract

The ability of NMR to predict purge from vacuum-packed pork that was stored for 9 days was investigated. T2 relaxation was measured at 24 h post mortem (p.m.) and again after 9 days of chilled storage. NMR measurements from day 1 p.m. were limited in predicting day-9 purge (|r| = 0.37–0.52). The root mean square error of linear regression (RMSD) for measuring day-9 purge using the relaxation time of intra-myofibrillar water (T21) measured on day 1 p.m. (r = −0.46) was 1.31% (range: 1.15–7.69% purge), corresponding to ±2.62% (2 × RMSD) prediction error of purge with 95% probability. This indicated that for purge production rate, the distribution and mobility of water in meat on day 1 p.m. may be of little relevance. Further tests were conducted to explain this poor predictability, by taking NMR measurements of water mobility and distribution made on the same meat sample (taken at 96 h p.m.) every day, during a 9-day storage period. By analyzing the T21 and T22 domains every day, it was revealed that during the first 5-day of storage, water (86%) moved from intra-myofibrillar space to extra-myofibrillar space. However, this movement did not result in detectable drip. A major liquid loss followed between days 6 and 7 and ceased day 8. This complexity of the water movement between domains during storage may explain the poor predictability of day-9 purge using NMR measurements from day 1.

Category

Academic article

Client

  • Research Council of Norway (RCN) / 233910
  • Nofima AS / 201701
  • Research Council of Norway (RCN) / 262308
  • Research Council of Norway (RCN) / 229192
  • Agricultural Agreement / 233910
  • Research Council of Norway (RCN) / 262300
  • Nofima AS / 10846

Language

English

Author(s)

  • Han Zhu
  • Marion O Farrell
  • Eddy Walther Hansen
  • Petter Vejle Andersen
  • Per Berg
  • Bjørg Tordis Egelandsdal

Affiliation

  • Norges miljø- og biovitenskapelige universitet
  • Nortura BA
  • SINTEF Digital / Smart Sensor Systems
  • University of Oslo
  • Nofima, The Norwegian Institute of Food, Fisheries and Aquaculture Research

Year

2017

Published in

Journal of Food Engineering

ISSN

0260-8774

Volume

206

Page(s)

98 - 105

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