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- Title
Prediction of Trained Panel Sensory Scores for Beef with Non-Invasive Raman Spectroscopy.
- Authors
Cafferky, Jamie; Cama-Moncunill, Raquel; Sweeney, Torres; Allen, Paul; Cromie, Andrew; Hamill, Ruth M.
- Abstract
The objective of this study was to investigate Raman spectroscopy as a tool for the prediction of sensory quality in beef. Raman spectra were collected from M. longissimus thoracis et lumborum (LTL) muscle on a thawed steak frozen 48 h post-mortem. Another steak was removed from the muscle and aged for 14 days before being assessed for 12 sensory traits by a trained panel. The most accurate coefficients of determination of cross validation (R2CV) calibrated within the current study were for the trained sensory panel textural scores; particularly tenderness (0.46), chewiness (0.43), stringiness (0.35) and difficulty to swallow (0.33), with practical predictions also achieved for metallic flavour (0.52), fatty after-effect (0.44) and juiciness (0.36). In general, the application of mathematical spectral pre-treatments to Raman spectra improved the predictive accuracy of chemometric models developed. This study provides calibrations for valuable quality traits derived from a trained sensory panel in a non-destructive manner, using Raman spectra collected at a time-point compatible with meat management systems.
- Subjects
RAMAN spectroscopy; BEEF quality; STEAK (Beef); MUSCLE aging; CHEMOMETRICS; FORECASTING; ERECTOR spinae muscles
- Publication
Chemosensors, 2022, Vol 10, Issue 1, p6
- ISSN
2227-9040
- Publication type
Article
- DOI
10.3390/chemosensors10010006