Discrimination of Pork Storage Time Using Near Infrared Spectroscopy and Adaboost plus OLDA

Pork storage time is closely related to its freshness. With the help of near infrared diffuse reflectance spectroscopy, pork sample data were collected. The orthogonal linear discriminant analysis (OLDA) algorithm was used to extract features. Furthermore, by introducing Adaboost algorithm to OLDA, a new algorithm, named Adaboost+OLDA, was proposed based on OLDA and Adaboost. To investigate the classification rate and the computational time of Adaboost+OLDA algorithm, the classical feature extraction methods (PCA+LDA and OLDA) were compared with Adaboost+OLDA in the experiments. Experimental results showed that Adaboost+OLDA could be computed efficiently and in improved the generalization ability of OLDA. The average classification rate of Adaboost+OLDA is more than 95%.