An e-nose was presented to trace freshness of cherry tomatoes that were squeezed for juice consumption. Four supervised approaches (linear discriminant analysis, quadratic discriminant analysis, support vector machines and back propagation neural network) and one semi-supervised approach (Cluster-then-Label) were applied to classify the juices, and the semi-supervised classifier outperformed the supervised approaches. Meanwhile, quality indices of the tomatoes (storage time, pH, soluble solids content (SSC), Vitamin C (VC) and firmness) were predicted by partial least squares regression (PLSR). Two sizes of training sets (20% and 70% of the whole dataset, respectively) were considered, and R-2 > 0.737 for all quality indices in both cases, suggesting it is possible to trace fruit quality through detecting the squeezed juices. However, PLSR models trained by the small dataset were not very good. Thus, our next plan is to explore semi-supervised regression methods for regression cases where only a few experimental data are available. (C) 2014 Elsevier Ltd. All rights reserved.