Xiao Li1, Fang Feng1, Runze Gao1, Lu Wang1, Ye Qian1, Chunbao Li1, Guanghong Zhou1. 1. Key Laboratory of Meat Processing and Quality Control, MOE; Key Laboratory of Animal Products Processing, MOA; Jiangsu Synergetic Innovation Center of Meat Production, Processing and Quality Control, College of Food Science and Technology, Nanjing Agricultural University, Nanjing, 210095, P.R. China.
Abstract
BACKGROUND: Pale, soft and exudative (PSE) meat is a quality problem that causes a large economic loss to the pork industry. In the present work, near infrared (NIR) quantification and identification methods were used to investigate the feasibility of differentiating potential PSE meat from normal meat. RESULTS: NIR quantification models were developed to estimate meat pH and colour attributes (L*, a*, b*). Promising results were reported for prediction of muscle pH (R(2) CV = 70.10%, RPDCV = 1.83) and L* (R(2) CV = 77.18%, RPDCV = 1.91), but it is still hard to promote to practical application at this level. The Factorisation Method applied to NIR spectra could differentiate potential PSE meat from normal meat at 3 h post-mortem. Correlation analysis showed significant relationship between NIR data and LF-NMR T2 components that were indicative of water distribution and mobility in muscle. PSE meat had unconventionally faster energy metabolism than normal meat, which caused greater water mobility. CONCLUSION: NIR spectra coupled with the Factorisation Method could be a promising technology to identify potential PSE meat. The difference in the intensity of H2 O absorbance peaks between PSE and normal meat might be the basis of this identification method.
BACKGROUND: Pale, soft and exudative (PSE) meat is a quality problem that causes a large economic loss to the pork industry. In the present work, near infrared (NIR) quantification and identification methods were used to investigate the feasibility of differentiating potential PSE meat from normal meat. RESULTS: NIR quantification models were developed to estimate meat pH and colour attributes (L*, a*, b*). Promising results were reported for prediction of muscle pH (R(2) CV = 70.10%, RPDCV = 1.83) and L* (R(2) CV = 77.18%, RPDCV = 1.91), but it is still hard to promote to practical application at this level. The Factorisation Method applied to NIR spectra could differentiate potential PSE meat from normal meat at 3 h post-mortem. Correlation analysis showed significant relationship between NIR data and LF-NMR T2 components that were indicative of water distribution and mobility in muscle. PSE meat had unconventionally faster energy metabolism than normal meat, which caused greater water mobility. CONCLUSION: NIR spectra coupled with the Factorisation Method could be a promising technology to identify potential PSE meat. The difference in the intensity of H2 O absorbance peaks between PSE and normal meat might be the basis of this identification method.