ABSTRACT
We report an application of data fusion for chemometric classification of 135 canned samples of Chinese
lager beers by manufacturer based on the combination of fluorescence, UV and visible spectroscopies.
Right-angle synchronous fluorescence spectra (SFS) at three wavelength difference Dk = 30, 60 and
80 nm and visible spectra in the range 380–700 nm of undiluted beers were recorded. UV spectra in
the range 240–400 nm of diluted beers were measured. A classification model was built using principal
component analysis (PCA) and linear discriminant analysis (LDA). LDA with cross-validation showed that the data fusion could achieve 78.5–86.7% correct classification (sensitivity), while those rates using
individual spectroscopies ranged from 42.2% to 70.4%. The results demonstrated that the fluorescence,
UV and visible spectroscopies complemented each other, yielding higher synergic effect.