Determination of the botanical origin of honey by sensor fusion of impedance e-tongue and optical spectroscopy

• E-tongue (ET) and optical spectroscopy (OS) are used to discriminate different honeys.
• Individually, ET clearly outperforms the OS techniques (200–1000 nm).
• The use of physical models to fit the data improves the results with ET but not with OS.
• Data fusion was performed with multi-way PCA and improved individual results.
• A new method for data fusion based on one-dimensional clustering gives the best results.

The aim of this study was to discriminate four commercial brands of Portuguese honeys according to their botanical origin by sensor fusion of impedance electronic tongue (e-tongue) and optical spectroscopy (UV–Vis–NIR) assisted by Principal Component Analysis (PCA) and Cluster Analysis (CA). We have also introduced a new technique for variable selection through one-dimensional clustering which proved very useful for data fusion. The results were referenced against standard sample identification by classical melissopalynology analysis. Individual analysis of each technique showed that the e-tongue clearly outperformed the optical techniques. The electronic and optical spectra were fitted to analytical models and the model coefficients were used as new variables for PCA and CA. This approach has improved honey classification by the e-tongue but not by the optical methods. Data from the three techniques was then considered simultaneously. Simple concatenation of all matrices did not improve the classification results. Multi-way PCA (MPCA) proved to be a good option for data fusion yielding 100% classification success. Finally, a variable selection method based on one-dimensional clustering was used to define two new approaches to sensor fusion, and both yielded sample clusters even better defined than using MPCA. In this work we demonstrate for the first time the feasibility of sensor fusion of electronic and optical spectroscopy data and propose a new variable selection method that improved significantly the classification of the samples through multivariate statistical analysis.