Two-dimensional correlation analysis of visible/near-infrared spectral intensity variations of chicken breasts with various chilled and frozen storages.

Generalized two-dimensional (2D) correlation analysis of visible/near-infrared (NIR) spectra was performed to characterize the spectral intensity variations of chicken muscles induced by either storage time/temperature regime or shear force values. The results showed that intensities of two visible bands at 445 and 560 nm increase with the storage temperature under identical treatment, possibly indicating a color change due to frozen storage. The 2D NIR correlation spectra indicated that all NIR bands reduce their spectral intensities, probably due to the water loss and compositional alterations during the freeze-thaw process as well as the tenderization development in muscle storage. The heterospectra correlating the spectral bands in both visible and NIR regions exhibited a strong correlation and suggested the sequential change between color and other developments in muscles. In addition, shear value-induced NIR spectral intensity variations detected significant differences in spectral features between tender and tough muscles.

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

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.