Near-infrared spectroscopy detects woody breast syndrome in chicken fillets by the markers protein content and degree of water binding

Abstract

The muscle syndrome woody breast (WB) impairs quality of chicken fillets and is a challenge to the poultry meat industry. There is a need for online detection of affected fillets for automatic quality sorting in process. Near-infrared spectroscopy (NIRS) is a promising method, and in this study we elucidate the spectral properties of WB versus normal fillets. On a training set of 50 chicken fillets (20 normal, 30 WB), we measured NIR, nuclear magnetic resonance (NMR) T2 relaxation distributions, and crude chemical composition. NIRS could estimate protein in the fillets with an accuracy of ±0.64 percentage points. T2 distributions showed that there was a larger share of free water in WB fillets. This difference in water binding generated a shift and narrowing of the water absorption peak in NIR around 980nm, quantified by a bound water index (BWI). The correlation between BWI and T2 distributions was 0.78, indicating that NIRS contains information about degree of water binding. Discriminant analysis showed that NIRS obtained 100% correct classification of normal versus WB on the training set, and 96% correct classification on a test set of 52 fillets. The main reason for why NIRS can successfully discriminate between WB and normal fillets is the methods sensitivity to both protein content and degree of water binding in the muscle, both established markers for WB. The classification model can be based on NIR spectra only, calibration against protein is not needed. The affected muscle tissue associated with the WB syndrome is unevenly distributed in the fillets, and this heterogeneity was characterized by NIRS and NMR. Clear differences in water binding properties were found between the superficial 1cm layer and the deeper layer at 1 to 2cm depth. Significant differences in protein estimates by NIRS at different measurement points along the chicken fillets were obtained for WB fillets. The findings suggest how to obtain optimal sampling with NIRS for best possible discrimination between WB and normal breast fillets.