期刊
MEAT SCIENCE
卷 84, 期 3, 页码 455-465出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.meatsci.2009.09.016
关键词
Pre-sliced; Pork; Turkey; Ham; Colour; Image texture; Consumer agreement; Classification; Image analysis; Computer vision
资金
- Irish Department of Agriculture, Fisheries Food
- Teagasc Walsh
Images of three qualities of pre-sliced pork and Turkey hams were evaluated for colour and textural features to characterize and classify them, and to model the ham appearance grading and preference responses of a group of consumers. A total of 26 colour features and 40 textural features were extracted for analysis. Using Mahalanobis distance and feature inter-correlation analyses, two best colour [mean of S (saturation in HSV colour space), std. deviation of W, which indicates blue to yellow in L*a*b* colour space] and three textural features [entropy of b*, contrast of H (hue of HSV colour space), entropy of R (red of RGB colour space)] for pork, and three colour (mean of R. mean of H, std. deviation of a*, which indicates green to red in L*a*b* colour space) and two textural features [contrast of B, contrast of L* (luminance or lightness in L*a*b* colour space)l for Turkey hams were selected as features with the highest discriminant power. High classification performances were reached for both types of hams (>99.5% for pork and >90.5% for Turkey) using the best selected features or combinations of them. In spite of the poor/fair agreement among ham consumers as determined by Kappa analysis (Kappa-value < 0.4) for sensory grading (surface colour, colour uniformity, bitonality, texture appearance and acceptability). a dichotomous logistic regression model using the best image features was able to explain the variability of consumers' responses for all sensorial attributes with accuracies higher than 74.1% for pork hams and 83.3% for Turkey hams. (C) 2009 Elsevier Ltd. All rights reserved.
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