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Prediction of Warner-Bratzler shear force, intramuscular fat, drip-loss and cook-loss in beef via Raman spectroscopy and chemometrics.

Authors :
Cama-Moncunill, Raquel
Cafferky, Jamie
Augier, Caroline
Sweeney, Torres
Allen, Paul
Ferragina, Alessandro
Sullivan, Carl
Cromie, Andrew
Hamill, Ruth M.
Source :
Meat Science. Sep2020, Vol. 167, pN.PAG-N.PAG. 1p.
Publication Year :
2020

Abstract

Rapid prediction of beef quality remains a challenge for meat processors. This study evaluated the potential of Raman spectroscopy followed by chemometrics for prediction of Warner-Bratzler shear force (WBSF), intramuscular fat (IMF), ultimate pH, drip-loss and cook-loss. PLS regression models were developed based on spectra recorded on frozen-thawed day 2 longissimus thoracis et lumborum muscle and validated using test sets randomly selected 3 times. With the exception of ultimate pH, models presented notable performance in calibration (R2 ranging from 0.5 to 0.9; low RMSEC) and, despite variability in the results, promising predictive ability: WBSF (RMSEP ranging from 4.6 to 9 N), IMF (RMSEP ranging from 0.9 to 1.1%), drip-loss (RMSEP ranging from 1 to 1.3%) and cook-loss (RMSEP ranging from 1.5 to 2.9%). Furthermore, the loading values indicated that the physicochemical variation of the meat influenced the models. Overall, results indicated that Raman spectroscopy is a promising technique for routine quality assessments of IMF and drip-loss, which, with further development and improvement of its accuracy could become a reliable tool for the beef industry. • Raman spectroscopy was evaluated for prediction of WBSF, IMF, drip-loss and cook-loss. • Models were developed using PLS regression and validated using independent data sets. • IMF, drip-loss and cook-loss calibrations exhibited notable fit; WBSF moderate fit. • Despite variability, in average all models showed promising predictive ability. • The loading values indicated that variation in the physicochemical composition of the meat influenced the models. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03091740
Volume :
167
Database :
Academic Search Index
Journal :
Meat Science
Publication Type :
Academic Journal
Accession number :
143418598
Full Text :
https://doi.org/10.1016/j.meatsci.2020.108157