Application of hyperspectral imaging for characterization of intramuscular fat distribution in beef

In this study, a hyperspectral imaging system in the spectral region of 400–1000 nm was used for visualization and determination of intramuscular fat concentration in beef samples. Hyperspectral images were acquired for beef samples, and spectral information was then extracted from each single sample from the fat and non-fat regions. The intramuscular fat content was chemically extracted and quantified for the same samples. Chemometrics including analysis of variance (ANOVA) and spectral similarity measures involving spectral angle measure (SAM), and Euclidian distance measure (EDM) were then used to analyze the data. An ANOVA analysis indicates that the two selected spectral variables (e.g., 650.4–736.4 nm) are effective to generate ratio image for visualization of the intramuscular fat distribution in beef. The spectral similarity analysis methods, which is based on the quantifying the spectral similarities by using predetermined endmember spectrum vector, provided comparable results for characterization and detection of intramuscular fat in beef. In term of overall classification accuracy, spectral similarity measure methods outperformed the ratio image of selected bands based on the result of ANOVA analysis. The results demonstrate that proposed technique has a potential for fast and nondestructive determination of intramuscular fat in beef.