Skip to main content
ARS Home » Research » Publications at this Location » Publication #139139
Title: PREDICTION OF COLOR, TENDERNESS, AND SENSORY CHARACTERISTICS OF BEEF STEAKS BY VISIBLE AND NEAR INFRARED REFLECTANCE SPECTROSCOPY. A FEASIBILITY STUDY

Author
item LIU, YONGLIANG - UNIVERSITY OF GEORGIA
item Lyon, Brenda
item Windham, William
item REALINI, CAROLINA - UNIVERSITY OF GEORGIA
item PRINGLE, TIMOTHY - UNIVERSITY OF GEORGIA

Submitted to: Journal of Meat Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/27/2002
Publication Date: 6/29/2003
Citation: Liu, Y., Lyon, B.G., Windham, W.R., Realini, C.E., Pringle, T.D. 2003. Prediction of color, tenderness, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study. Journal of Meat Science. 65(3):1107-1115.

Interpretive Summary: During aging, raw beef steaks undergo several changes that can affect quality. These changes are reflected in many characteristics such as color, tenderness, flavor, and juiciness. A challenge in the meat industry is to obtain reliable information about meat quality at many stages along the production process, ultimately providing guaranteed quality of beef products for consumers. Existing techniques in meat quality assessment, either instrumental (for example, Warner-Bratzler shear force and Hunter color measurements) or sensory evaluation, can provide reliable information about meat quality. However, on-line techniques that are fast, non-destructive, and accurate are needed. Near infrared (NIR) spectroscopy could form the basis for such techniques due to its speed, ease of use, and minimal interferences from moisture or color of meat samples. In this study, we demonstrated the potential of visible/NIR spectroscopy to predict color, tenderness and sensory attributes of beef carcasses at different post mortem days. Moreover, we attempted to develop statistical models that could separate tender steaks from tough steaks using partial least squares (PLS) and principal component analysis (PCA). The result suggested that PCA model of measured tenderness showed greater promise than PLS in the classification of tender and tough meats, with over 96% success. This information is useful to beef packers, retailers, and researchers who are interested in applying visible/NIR spectroscopy-based quality grading or classifying.

Technical Abstract: Color, texture and sensory attributes of 24 beef carcasses at 2, 4, 8, 14, and 21 days post mortem were predicted by visible/near infrared (visible/NIR) reflectance spectroscopy in 400-1080 nm region. Predicting the Hunter a*, b*, and E* values yielded the coefficient of determination (R**2) in calibration to be 0.78-0.90, compared with R**2 was between 0.49 and 0.55 for tenderness, Hunter L*, sensory chewiness and juiciness. The prediction R**2 for tenderness was in the range of 0.22-0.72, when the samples were segregated according to the aging days. Based on PLS model predicted tenderness, beef samples were classified into tender and tough classes with a correct classification of 83%. SIMCA/PCA model of measured tenderness showed great promise in the classification of tender and tough meats with over 96% success.