On-Line Automated Inspection of Poultry Carcasses by Machine Vision
Development of an automated poultry inspection system that is low-cost, operates 
with minimum human intervention, and is able to maintain its accuracy is important to 
the U.S. Food Safety and Inspection Service (FSIS) and the poultry industry. Such 
a system, placed strategically in the processing plants, would help improve the 
inspection speed, minimize problems of human error and variability, improve the 
effectiveness of the federal inspection program, and increase the slaughter plants’ 
productivity. This paper describes an automated poultry inspection system that 
utilizes machine vision technology. The system was tested on-line at a poultry plant. 
The results showed that the classification accuracy for wholesome and unwholesome 
carcasses were 94 and 87%, respectively.
Multispectral imaging for detecting contamination in poultry carcasses
A multispectral imaging system with selected optical filters of 542 and 700-nm wavelength was shown feasible for detecting contaminated poultry carcasses with high accuracy. The analysis of textural features based on co-occurrence matrix (COM) was conducted to determine the performance of multispectral image analyses in discriminating unwholesome poultry carcasses from wholesome carcasses. The variance, sum average, sum variance, and sum entropy of COM were the most significant texture features (P less than 0.0005) to identify unwholesome poultry carcasses. The feature values of angular second moment, variance, sum average, sum variance, and sum entropy did not vary with the distances and directions of COM for the spectral images. When a direction was equal to 0 degrees, the contrast was lower and the inverse difference moment and difference variance were higher (P less than 0.01) than any other direction in the visible spectral images. The characteristics of variance and sum variance texture feature of spectral images varied with the wavelength of spectral images and unwholesomeness of poultry carcasses as well. The sum variance of wholesome was higher (P less than 0.005) than unwholesome carcasses at the spectral image of 542-nm. The linear discriminant model was able to identify wholesome carcasses with classification accuracy of 83.9 percent and the unwholesome carcasses could be identified by quadratic model with 97.1 percent accuracy when textural features of spectral image at 700-nm wavelength were used as input data for models.
Linear Programming in the Allocation of Milk Resources for Cheese Making
Abstract To use linear programming, the cheese making process can be modeled as a resource conversion process. The relationship between input milk resources and output products can be analyzed using mass balance assumptions. The variables used in the linear programming model will represent the pounds of each milk resource chosen to formulate a vat of cheese milk. Additional variables will be used to represent the pounds of cream to be removed from a milk resource that has the potential to have cream removed. Three objectives that could be considered are 1) maximize net returns, 2) maximize cheese yield, or 3) minimize cost. A hypothetical example is used to illustrate how the cheese making process can be modeled. This linear programming model depends on formulas that relate ratio of casein to fat of milk to the fat on the dry basis of the cheese. Sensitivity analysis is performed to determine at what cost a milk resource not in the optimal formulation will replace a milk resource currently in the optimal formulation. The linear programming model can be an effective management tool in the allocation of milk resources for cheese making.