基于Adaboost+OLDA和近红外光谱的猪肉贮藏时间辨别

猪肉的贮藏时间和猪肉的新鲜度紧密相关。通过近红外漫反射光谱技术获取猪肉样本数据，利用正交线性判别分析(OLDA)算法进行特征提取，同时将自适应提升法(Adaboost)引入OLDA，提出了一种基于Adaboost和OLDA的集成学习算法——Adaboost+OLDA。实验针对分类正确率和运算时间将传统特征提取算法(PCA+LDA和OLDA)和Adaboost+OLDA算法进行了对比研究，结果表明Adaboost+OLDA算法不仅具有很好的运算效率，而且提高了OLDA算法的泛化能力，在猪肉样本测试中达到了95%以上的分类正确率。

Discrimination of Pork Storage Time Using Near Infrared Spectroscopy and Adaboost+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%.