Abstract:Frozen fish are usually less desirable than the fresh counterparts on the market. As a result, it has become a common issue that the frozen-thawed fish are disguised as fresh for a higher price. In this study the near infrared (NIR) spectroscopy was employed to separate the frozen-thawed fish from the fresh. One hundred and twenty prepared Sebastes schlegeli samples including 60 fresh and 60 frozen-thawed were scanned using a near-infrared spectroscopy system between 10000–4000 cm-1 wavenumbers. The working model was based on the fact that the average NIR spectra of fresh fish were distinctive from the frozen-thawed and possessed certain characteristics and fingerprint resistance. Principal component analysis (PCA) was used for the dimension reduction of the spectra data. The first two principal components (PCs) explained over 98% of variances in all the spectral bands. Clustering was performed and analyzed based on the first two PCs of all samples. The principal component score plot demonstrated that the fresh (above the X axis) and the frozen-thawed samples (below the X axis) were well separated, and that the distribution of fresh samples was dispersal. In order to improve the accuracy of prediction, the support vector machine (SVM) classification model was developed to differentiate the fresh fish from the frozen-thawed, based on principal component analysis scores. The score values of the first ten PCAs were used as the input variables of the SVM, and the penalty parameter c and kernel function parameter g were optimized. Ninety samples were used for building the SVM model. This model was then applied to predict the rest 30 unknown samples, and the prediction rate was 100%. These results suggested that the near infrared spectroscopy combined with principal component analysis and support vector machine could be used as a rapid, simple and reliable method to identify the fresh and frozen-thawed fish.