Determination of the adulteration of butter fat by its triglyceride composition obtained by GC. A comparison of the suitability of PLS and neural networks

The suitability of partial least squares (PLS) and neural nets for the identification of mixtures of butter fat with foreign fat is compared. While neural nets are most suitable for classification, quantitative results are obtained by PLS. Butter fats of various European countries have been analyzed by GC. Fifty-six samples were used as the calibration set to build the PLS model and prepare the neural net, respectiveley. For successful modelling a 11-factor PLS model was sufficient. The neural net architecture chosen is a 17 × 1 preceptron. Both data evaluation techniques have been validated with 28 samples not included in the calibration set. For PLS the results indicate a detection limit of 1–2% foreign fat in butter fat. The neural net classified 20 samples correctly, but eight samples could not be classified at all.