Abstract

The objective of this study was to investigate the usefulness of image texture features obtained from fresh (never frozen) pork backfat for the prediction of fatty acid content and Iodine Value (IV). Five image texture features (directionality, contrast, roughness, heterogeneity and line-likeness) were extracted from cross-sectional images of 9pork loin chops with overlying subcutaneous fat. Texture features were extracted from images obtained on the subcutaneous fat using a digital camera. A full fatty acid profile was determined for each subcutaneous fat sample using AOAC and AOCS official methods. Linear and stepwise regression methods were utilized to establish the prediction models for oleic, linoleic and linolenic fatty acids and IV. Linear regression analyses produced higher coefficients of determination (R²) for all 3fatty acids. Linear regression models for linoleic and linolenic acid generated an R² of 0.95. These preliminary findings suggest potential for use of image texture features for prediction of pork fattyacid values and subsequent pork fat quality.

Keywords:

Fatty acid, image processing, iodine value, prediction, texture feature,

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The authors have no competing interests.

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