Evaluation of nutrient content, physicochemical and functional properties of desalted duck egg white by ultrafiltration as desalination.

In this study, the effects of ultrafiltration technique on the desalination efficiency, nutrient content, physicochemical properties, functional properties, texture profile, and microstructure of salted duck egg white were evaluated. The results showed that ultrafiltration can remove 92.93% salt from salted duck egg white (SDEW) and final salt% of desalted duck egg white (DDEW) was 0.65%. The analysis of nutrient content and amino acid of SDEW and desalted duck egg white powder (DDEWP) sample was significantly lower than those of fresh duck egg white (FDEW). Although emulsifying capacity of SDEW, DDEW, and DDEWP exhibited significantly lower than that of FDEW, an excellent foaming ability was found in those samples. Moreover, the texture profiles (gel strength, hardness and elasticity) of SDEW, DDEW, and DDEWP samples presented lower value than FDEW. The observation of microstructure, DDEWP possessed smooth surface of protein globules with deep hole liked donuts and distribution of a few of salt crystals. While salted duck egg white powder (SDEWP) had a raisin-like surface formation with salt formed cubic crystals. Overall, both liquid and dried material of desalted duck egg could be used as a good ingredient in baking food due to their excellent foaming capacity.

Prediction of pork loin quality using online computer vision system and artificial intelligence model.

The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds.

Prediction of pork color attributes using computer vision system.

Color image processing and regression methods were utilized to evaluate color score of pork center cut loin samples. One hundred loin samples of subjective color scores 1 to 5 (NPB, 2011; n=20 for each color score) were selected to determine correlation values between Minolta colorimeter measurements and image processing features. Eighteen image color features were extracted from three different RGB (red, green, blue) model, HSI (hue, saturation, intensity) and L*a*b* color spaces. When comparing Minolta colorimeter values with those obtained from image processing, correlations were significant (P<0.0001) for L* (0.91), a* (0.80), and b* (0.66). Two comparable regression models (linear and stepwise) were used to evaluate prediction results of pork color attributes. The proposed linear regression model had a coefficient of determination (R(2)) of 0.83 compared to the stepwise regression results (R(2)=0.70). These results indicate that computer vision methods have potential to be used as a tool in predicting pork color attributes.