This paper describes a transportable spectrophotometer system developed for real-time classification of poultry carcasses on-site at slaughter plants. The system measures the spectral reflectance of poultry carcasses in the visible/near-infrared regions (471 to 963.7 nm). An optimal neural network classifier for real-time classification of poultry carcasses into normal, septicemic, and cadaver classes with an average accuracy of 93% was obtained. When the classifier was used to classify the carcasses into two classes, normal and abnormal (septicemic and cadaver), the average accuracy was 97.4%. The percentages of the false positive and the false negative error rates were 2.4 and 2.9%, respectively. This paper also proposes implementing the system at the slaughter plants as a poultry carcass screening system (PCSS). Using two visible/NIR spectrophotometer systems, the PCSS tests both sides of the breast of each bird. With the PCSS, the inspection-passed-bird and inspection-rejected-bird error rates by the spectrophotometer systems would be minimal, and less than 5% of the incoming birds would require further inspection by human inspectors.

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription