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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #324781
Title: Application of hyperspectral imaging for characterization of intramuscular fat distribution in beef

Author
item LOHOMI, SANTOSH - Chungnam National University
item LEE, SANGDAE - Korea Institute Of Industrial Technology (KITECH)
item LEE, HOONSOO - Chungnam National University
item Kim, Moon
item LEE, WANG-HEE - Chungnam National University
item CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Infrared Physics and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/31/2016
Publication Date: 2/1/2016
Citation: Lohomi, S., Lee, S., Lee, H., Kim, M.S., Lee, W., Cho, B. 2016. Application of hyperspectral imaging for characterization of intramuscular fat distribution in beef. Infrared Physics and Technology. 74:1-10.

Interpretive Summary: In many countries, fat is an unpopular component of meat for consumers, as it is considered to be unhealthy because it increases obesity risks and blood cholesterol levels, which leads to cardiovascular disease. In this study, hyperspectral imaging was used for visualization and determination of intramuscular fat concentration in beef samples. The intramuscular fat content was chemically extracted and quantified for the samples after imaging. Simple image processing and spectral similarity analysis methods allowed characterization and detection of the intramuscular fat in beef. The results demonstrate that spectral imaging technique has a potential for fast and nondestructive determination of intramuscular fat in beef. This investigation provides beneficial information to food technologists who are developing a rapid and nondestructive means to assess fat contents in beef.

Technical Abstract: 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 to736.4 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.