Control of ham salting by using image segmentation

Head: A. Sánchez Company: Instituto ai2 Term: 2006/2007

Project Abstract

Curing is one of the most traditional processes in the meat industry, being used in a great variety of products such as cured ham. During the salting process the raw material acquires the curing agents, allowing the safe development of subsequent stages in processing.

Digital image analysis has been used in different food research areas. Most of the studies that use image analysis in the evaluation of different aspects of meat products have been carried out mainly on ham, detecting quality problems of the product. However, none of these studies deals with the influence of different components present on the ham surface (fat, connective tissue and lean) and its relationship with mass transfer during ham processing.

This study uses image segmentation to quantify the lean, fatty and connective tissue areas on the ham surface, and the relationship of those areas to salt gain during the salting process.

A robust technique was developed for segmenting different regions of the ham surface using colour images. The proposed method has two steps: (i) computation of a high contrast grey value image from a linear combination of the RGB colour components; (ii) segmentation based on the k-nearest-neighbors pattern recognition approach.