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Gas chromatography (GC), high performance liquid chromatography (HPLC) and pyrolysis mass spectrometry (Py-MS) were applied for the discrimination of cocoa butters and other vegetable fast and the quantitation of mixtures made thereof. The quantitation of vegetable fats added to cocoa butter was investigated on the basis of triglyceride profiles in model mixtures of cocoa butter and vegetable fats. HPLC with evaporative light scatter detector was compared to profiles obtained by high temperature using a temperature resistant low-medium polarity column. Combined with multivariate data analysis GC provides a method, that allows the detection of the presence of foreign fat in cocoa butter with an uncertainty of about 3-5%. HPLC analysis and the subsequent application of neural net allows a classification of the samples with an error of approximately 5%. Py-MS has been shown to be a fast and versatile method for some problems related to the proof of food authenticity, such as olive oil, orange juice and whisky. The hereby obtained mass spectra show unique patterns which can be correlated to the individual food composition. Py-MS was applied for the discrimination of cocoa butter and a variety of other pure vegetable fats. Only kokum fat interfered with cocoa butter, all other fats were clearly separated.

Additional information

Authors: ANKLAM E, JRC Ispra (IT);HANNAERT P, JRC Ispra (IT);LIPP M, JRC Ispra (IT);SIMONEAU C, JRC Ispra (IT)
Bibliographic Reference: Paper presented: Food Authenticity 96, Norwich (GB), September 1-3, 1996
Availability: Available from (1) as Paper EN 40026 ORA
Record Number: 199611386 / Last updated on: 1996-12-04
Original language: en
Available languages: en
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