Rapid Classification of Pork NIR Speca Using PCA and FLVQ

Abstract

Pork storage time is relevant to its freshness which influences pork quality. To achieve the rapid and effective discrimination of pork storage time, near infrared spectroscopy was used to collect the near infrared reflectance (NIR) spectra of pork in different storage time. The high-dimensional NIR spectra was firstly compressed by principal component analysis (PCA) and then classified by fuzzy learning vector quantization (FLVQ). PCA plus FLVQ is a completely unsupervised learning algorithm which finds hidden patterns in unlabeled data. Experimental results showed that PCA plus FLVQ could classify pork NIR spectra effectively.