Detection of adulteration in cherry tomato juices based on electronic nose and tongue: Comparison of different data fusion approaches

• Seven techniques were applied for evaluation adulteration of tomato juices.
• Sensor drift and humidity problems were considered.
• Different feature selection and data fusion methods were discussed.

Seven approaches were employed for authentication of fresh cherry tomato juices adulterated with different levels of overripe tomato juices: 0–30%. Two e-nose measurements were considered, and the result indicates that a pretreatment of using desiccant prior to e-nose measurement is unnecessary. Principle Component Analysis (PCA), factor F and stepwise selection were applied for feature construction of fusion datasets. Qualitative recognition of adulteration levels was mainly performed by Canonical Discriminant Analysis (CDA) and Library Support Vector Machines (Lib-SVM). Quantitative calibration with respect to pH and soluble solids content (SSC) was performed using Principle Components Regression (PCR). All the approaches presented well classification performances, and prediction performances based on fusion approaches are better than based on sole usage of e-nose or e-tongue; yet classification and prediction performances based on different fusion approaches vary. This study indicates that simultaneous utilization of both instruments would guarantee a better performance than individually utilization of e-nose or e-tongue when proper data fusion approaches are used.