Data fusion of fluorescence and UV spectroscopies improves the detection of cocoa butter adulteration

The fraudulent addition of cheaper plant oils to cocoa butter is a real issue for the chocolate industry. The potential of fluorescence and UV and data fusion of these spectroscopies for the detection of cocoa butter adulteration with cocoa butter equivalents (CBEs) is investigated here. Principal component regression (PCR) models have been used to calculate the level of adulteration. A classification model has been built with the help of the principal component analysis (PCA) and the linear discriminant analysis (LDA). The LDA analysis has been applied to fluorescence intensities and UV spectra of cocoa butter, cocoa butter equivalents, and their mixtures in order to examine classification ability. The best prediction ability of adulteration was obtained for PCR data fusion models with the lowest errors of calibration (RMSEC) and validation (RMSEV) of 3.7 and 4.7%. The best performance of the PCA-LDA analysis has been observed for data fusion of fluorescence and UV intensities measurements at wavelength intervals of 10 and 30nm. The LDA shows that data fusion can achieve 95.2% correct classifications (sensitivity) in the validation data set, while the corresponding results for individual spectroscopies range from 66.7-90.5%. The results demonstrate that fluorescence and UV spectroscopies complement each other, giving a synergistic effect for the detection of cocoa butter adulteration with cocoa butter equivalents. Practical applications: The aim of this research is to develop a quick and cheap method of discriminating between cocoa butter and cocoa butter equivalents. The obtained results are satisfactory for classification of genuine cocoa butter and cocoa butter equivalents and for detection of cocoa butter adulterations with cheaper cocoa butter equivalents. The method is an important technical support of public health against profit-driven practices.