Prediction of Trained Panel Sensory Scores for Beef with Non-Invasive Raman Spectroscopy

Jamie Cafferky,Torres Sweeney,Ruth M Hamill,Raquel Cama-Moncunill,Paul Allen,Andrew Cromie

doi:10.3390/chemosensors10010006

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.

Highlights

Sensory characteristics have a major influence on consumer eating satisfaction with regard to repurchasing fresh meat [1,2]
The 5th and 6th steak removed from the loin were used for trained sensory panel analysis, while the 12th steak was allocated for Raman spectroscopy
The most accurate coefficients of determination of cross validation calibrated within the current study were for the trained sensory panel textural scores; in particular tenderness, chewiness and stringiness, with practical predictions achieved for metallic flavour, fatty after-effect and juiciness

Summary

Introduction

Sensory characteristics have a major influence on consumer eating satisfaction with regard to repurchasing fresh meat [1,2]. The most direct and accurate method of evaluating the sensory attributes of meat is through the use of trained panels [3,4]. Sensory panels are expensive and time consuming to conduct [5]. These methods are difficult to implement for either routine quality monitoring within meat management systems by commercial meat processors [6], or large scale, industry-wide recording of beef sensory data for the purposes of genetic evaluations [7]. Raman spectroscopy is a non-invasive vibrational spectroscopic technique that has applications in prediction of food quality. Raman spectroscopy measures the light scattered inelastically resulting from the interaction of a laser light with the molecules of a sample

Objectives

Methods

Results

Conclusion