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Advisor(s)
Abstract(s)
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.
Description
Keywords
Botanical origin Honey Chemometrics Electronic tongue Sensor fusion Spectroscopy
Citation
Comput Electron Agr 94 (2013) 1–11
Publisher
Elsevier