Predicting shrinkage of ellipsoid beef joints as affected by water immersion cooking using image analysis and neural network

Optical Methods and Techniques for Meat Quality Inspection

“…Examples include prediction of shrinkage of ellipsoid beef joints (Zheng et al, 2007); prediction of color, marbling, and surface texture ; measurement of texture features to classify beef as tough or tender (Li et al, 2001); prediction of skeletal maturity based on cartilage ossification in the thoracic vertebrae (Hatem et al, 2003); measurement of CIE L*, a*, b*, hue angle, and chroma (Larrain et al, 2008); automatic segmentation of the longissimus dorsi muscle and marbling (Jackman et al, 2009); prediction of percentage surface metmyoglobin on fresh beef, which causes wide variation in surface color (Demos et al, 1996); and classification of fresh and stained meat samples based on marbling in the longissimus thoracis muscle (Pena et al, 2013).…”

Comprehensive Water Quality Evaluation Based on Variable Fuzzy Model

“…The seawater environment is influenced by various factors, and the pollutants are characterized by uncertainty and fuzziness since the complex and unclear relationship exists among them. Presently the main methods for seawater quality evaluation include single factor evaluation method [2] , fuzzy comprehensive evaluation method [3] , grey correlation analysis method [4] , neural network method [5] etc., all of which have certain flaws: single factor evaluation method overemphasizes the pollution influence of the worst water on comprehensive evaluation grade; grey correlation analysis method usually simplifies the grade intervals into point value form and accordingly influences the reliability of classification; fuzzy comprehensive evaluation method adopts complicated calculation for relative membership degree and cannot bring about the practical result when grade synchronicity of indexes is poor [6] ; the commonly used neural network evaluation method is BP neural network method, but BP neural network sometimes encounters such problems as local minimum, slow convergence speed and oscillation effect [7] . Therefore, in order to evaluate seawater quality scientifically and make the evaluation method practicable, this paper applied the variable fuzzy method [8] proposed by Professor Chen Shouyu to seawater quality evaluation, reasonably determined the relative membership degree and relative membership function of sample's indexes at grade intervals, and reasonably determined the evaluation grade of seawater quality sample by varying the model and its parameters.…”

Correlation of the Attributes Measured by Computer Vision with Moisture and Fat Content of Meat Batters

“…It enables researchers to determine the size and shape of meat joints (Du and Sun, 2006;Zheng et al, 2006a;Zheng et al, 2007) as well as the colour coordinates in RGB and CIELAB colour systems of meat and meat products (O'Sullivan et al, 2003;Larraín et al, 2008). It can be used for beef carcass grading (Díez et al, 2006), classification of beef based on either marbling features (Chen and Qin, 2008) or colour of fat (Chen et al, 2010), detection of PSE (pale soft exudative) pork (Chmiel et al, 2011a) and estimation of fat content in chicken and turkey muscles (Chmiel et al, 2011b).…”