HPLC with light scatter detector and chemometric data evaluation for the analysis of cocoa butter and vegetable fats
In the study reported here high performance liquid chromatography equipped with an evaporative light scatter detector, was carried out in order to prove the authenticity of cocoa butter. Signals of 17 characteristic triglycerides have been used to develop two chemometric models. Partial least square (PLS) was applied for quantitation while neural nets were used for classification. The sample pool was divided in a training set of 18 and a prediction set of 14 samples. The samples included mixtures of several vegetable fats with cocoa butter. A 15x4x1 feed forward net could be trained and within the prediction set only 2 samples were not correctly assigned. A PLS model with 9 factors was applied and the mean prediction error was found to be 2.5%. The small number of samples was found to be sufficient to show the potential of this data evaluation. Results are expected to improve with a greater data pool.
Bibliographic Reference: Article: Fat Science Technology (1996)
Availability: Available from Dr E Anklam, Environment Institute, Joint Research Centre, 20120 Ispra (IT)
Record Number: 199610312 / Last updated on: 1996-03-29
Original language: en
Available languages: en