Automated image analysis as a tool to quantify the colour and composition of rainbow trout ( Oncorhynchus mykiss W.) cutlets

Abstract

The goal of this paper is to propose and evaluate automated image analysis methods for describing muscle cutlets in rainbow trout. The proposed automated image analysis methods were tested on a total of 983 scanned images of trout cutlets, and included quality traits such as fat percentage, flesh colour and the size of morphologically distinguishable subparts of the cutlet. A sub-sample of 50 images was randomly selected for manual segmentation of the cutlet, the dorsal fat depot and the red muscle and regions. The identification of these regions by manual and automatic image analysis correlated strongly ( r = 0.97, r = 0.95 and r = 0.91, respectively). The estimated fat percentage obtained from image analysis, based on the area of visible fat and the colour of the cutlet flesh, correlated well with chemical fat percentage measured by mid-infrared transmission spectroscopy (MIT) ( r = 0.78). The automated image analysis methods are therefore a reliable means of predicting the fat percentage of trout cutlets. Principal component analysis (PCA) loading plots were used to identify subsets of variables from the image analysis of special significance for further studies; cutlet area, dorsal fat depot area, red muscle area, back height, cutlet width, and width of left and right abdomen wall were among the variables selected. PCA loading plots of different colour variables indicated that simple statistical coefficients such as percentiles and mean values can be used to quantify different aspects of flesh colour. In conclusion, the methods presented here provide a powerful toolbox for describing important morphological structures and quality traits of trout cutlets.