Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study

• Published in: Meat science Vol. 65; no. 3; pp. 1107 - 1115
• Main Authors: Liu, Yongliang, Lyon, Brenda G, Windham, William R, Realini, Carolina E, Pringle, T.Dean D, Duckett, Susan
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2003; Elsevier
• Subjects: Beef meat; Biological and medical sciences; Classification; Color and sensory properties; Food industries; Fundamental and applied biological sciences. Psychology; General aspects; Meat and meat product industries; Methods of analysis, processing and quality control, regulation, standards; Tenderness; Visible/NIR spectroscopy; Color and sensory properties; Beef meat; Visible/NIR spectroscopy; Tenderness; Classification; Color; Meat product; Visible spectrometry; Forecasting; Texture; Near infrared spectrometry; Quality control; Control method; Beef
• ISSN: 0309-1740; 1873-4138
• DOI: 10.1016/S0309-1740(02)00328-5

Color, instrumental texture, and sensory attributes of steaks from 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* yielded the coefficient of determination ( R 2) in calibration to be 0.78–0.90, and 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 partial least square (PLS) model predicted tenderness, beef samples were classified into tender and tough classes with a correct classification of 83%. Soft independent modeling of class analogy of principal component analysis (SIMCA/PCA) model of measured tenderness showed great promise in the classification of tender and tough meats with over 96% success.