Characterization of cocoa butters and other vegetable fats by pyrolysis-mass spectrometry

Pyrolysis Mass Spectrometry (Py-MS) was used for the discrimination of cocoa butters from other vegetable fats. Mass spectra ranging from 50 amu to 250 amu were analyzed by principal component analysis (PCA) and with neural nets. The application of neural nets leads to a good discrimination between the two classes. Detailed analysis of the nets revealed that only the first 60 masses were used within the net. The use of PCA requires a careful selection of the number of masses included in the calculation. Canonical variance analysis was applied to determine the significant masses. Optimal performance of PCA was observed only using the first 22 significant masses. Most of these masses were different from the ones used by the neural net. It seems that the mass spectra obtained by Py-MS contain sufficient information for the discrimination of pure cocoa butter from other vegetable fats, but none of the methods seems to be able to extract all information available. Neural net provides a very robust method for this task and no prior data selection was necessary. Received: 13 May 1996 / Revised: 7 August 1996 / Accepted: 7 August 1996