title: Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection.
abstract: A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets.
authors.affiliation: FRCFT Research Group, Biosystems Engineering, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland.
authors.forename: Patrick
authors.initials: P
authors.lastname: Jackman
authors.forename: Da-Wen
authors.initials: DW
authors.lastname: Sun
authors.forename: Paul
authors.initials: P
authors.lastname: Allen
authors.forename: Nektarios A
authors.initials: NA
authors.lastname: Valous
authors.forename: Fernando
authors.initials: F
authors.lastname: Mendoza
authors.forename: Paddy
authors.initials: P
authors.lastname: Ward
mesh_terms: D000818:Animals; D003116:Color; D005511:Food Handling; D007091:Image Processing, Computer-Assisted; D008461:Meat Products / Q000032:analysis* / Q000145:classification / Q000592:standards*; D015203:Reproducibility of Results; D013552:Swine / Q000235:genetics; D014422:Turkeys / Q000235:genetics