LAUSR

In this paper, a method to rapidly determine living Chinese mitten crab freshness using an electronic nose (E-nose) and a non-linear data processing technique was studied. E-nose responses to crabs stored at 4 °C were measured. Meanwhile, total volatile basic nitrogen (TVB-N) was examined to provide freshness references for the E-nose analysis. However, traditional classification algorithms are not suitable for E-nose data with non-linear manifold structures; therefore, a modified unsupervised discriminant projection (MUDP) coupled with sample label information was proposed. MUDP can retain the local and global structure and take advantage of the importance of label information and then create a geometric structure optimal linear projection. Data classification experimental results proved that the classification accuracy of K-nearest-neighbor (KNN) combined with the data processed by MUDP was much better than that of other considered methods. Validation experiments indicated that the recognition rates of the proposed algorithm were higher than those of traditional linear algorithms such as PCA and LDA or nonlinear algorithms such as KPCA.