Differentiation of beef, buffalo, pork, and wild boar meats using colorimetric and digital image analysis coupled with multivariate data analysis

Fayca Rudhatin Swartidyana, Nancy Dewi Yuliana, I Ketut Mudite Adnyane, Joko Hermanianto, Irwandi Jaswir


Beef price is relatively expensive, which makes this commodity vulnerable to be counterfeited. The development of rapid, cheap and robust analytical methods for meats authentication has therefore become increasingly important. In this study, colorimetric and digital image analysis methods were used to characterize and classify four types of meat (beef, buffalo, pork, wild boar) and two muscle types from each sample (Semitendinosus and Vastus lateralis). Multivariate data analysis (PCA and OPLS-DA) was used to observe classification pattern among species using different color parameters data obtained from meat chromameter and digital image measurement. The results showed that PCA and OPLS-DA successfully classified meat from different species and different muscle type based on color, both in chromameter and in image analysis. It was shown that pork had the highest lightness level, and was the most different among the four types of meat tested. Beef was predominated by yellowish color, while buffalo meat had the highest reddish color level.  Semitendinosus and Vastus lateralis muscles had different color intensity where Vastus lateralis exhibited darker color intensity. This study showed that meat color analysis using chromameter and imaging techniques can be used as cheap and quick tools to discriminate meats form different species and different muscles type.


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Fayca Rudhatin Swartidyana
Nancy Dewi Yuliana
nancy_dewi@apps.ipb.ac.id (Primary Contact)
I Ketut Mudite Adnyane
Joko Hermanianto
Irwandi Jaswir
SwartidyanaF. R., YulianaN. D., AdnyaneI. K. M., Hermanianto J., & JaswirI. (2022). Differentiation of beef, buffalo, pork, and wild boar meats using colorimetric and digital image analysis coupled with multivariate data analysis. Jurnal Teknologi Dan Industri Pangan, 33(1), 87-99. https://doi.org/10.6066/jtip.2022.33.1.87
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