E-nose combined with chemometrics to trace tomato-juice quality

• Published in: Journal of food engineering Vol. 149; pp. 38 - 43
• Main Authors: Hong, Xuezhen, Wang, Jun, Qi, Guande
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2015
• Subjects: Chemometrics; Cherry tomato juice; Consumption; Discriminant analysis; Electronic nose; Electronic noses; Juices; Lycopersicon esculentum; Neural networks; Partial least squares regression; Regression; Semi-supervised classification; Storage; Support vector machines; Tomatoes; Trace quality; Semi-supervised classification; Trace quality; Storage; Partial least squares regression; Electronic nose; Cherry tomato juice
• ISSN: 0260-8774; 1873-5770
• DOI: 10.1016/j.jfoodeng.2014.10.003

•Quality of raw fruit was traced by detecting the squeezed juices using an e-nose.•A novel semi-supervised classifier based on spectral clustering was applied.•The new classifier outperforms four supervised linear and nonlinear classifiers.•Regression models based on 20% and 70% of the whole dataset were compared.•Quality indices of cherry tomatoes were successfully predicted. 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 R2>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.