Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses.
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 b*, 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)] 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.
Iqbal
Abdullah
A
FRCFT Group, Biosystems Engineering, Agriculture and Food Science Centre, School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
Valous
Nektarios A
NA
Mendoza
Fernando
F
Sun
Da-Wen
DW
Allen
Paul
P
D000818:Animals; D003116:Color; D003119:Color Perception Tests; D003258:Consumer Behavior; D006801:Humans; D016015:Logistic Models; D008460:Meat / Q000032:analysis / Q000145:classification* / Q000592:standards; D018482:Muscle, Skeletal; D013552:Swine