The use of cie L*a*b* colour space for smoked fish meat content assessment
Pages 130-136
Adrian Timar, Alin C. Teusdea, Camelia Bara, Cornelia Purcarea
Abstract
The major aim of this paper is to find a non-invasive and fast research method to assess the quality of fish meat from quantitative and hygienic point of view [1]. This method must be proper for industrial application and for aggressive environments. Thus, the main goal is to identify (i.e. to classify) the fish meat content in percentages for the five classes: red meat, fat, muscle, bone, skin.
For assessing the content of the fish meat many colour image analysis researches were done worldwide [2-4]. There were made classifications for only three classes (muscle, bone, fat).
In this paper the classification process is based on converting the digital scanned smoked fish meat images in CIE L*a*b* colour space [5, 6] which has the advantage of being a linear chromatic space. Furthermore it has a training stage to asses the chromatic discrimination boundaries for each class by using the reference class images.
For an accurate analysis there is done a comparison (“calibration”) between the smoked fish meat content results of the NIR-CIA method with the classical gravimetric invasive method one. This is accomplished with Fourier correlation analysis [7] of the meat content “profiles”.