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 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 analysed for GC. 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 21 samples correctly, but 8 samples could not be classified at all. In an extension the neural net could be trained with all samples available, thus it seems that the cause was a lack of sufficient calibration samples rather than an intrinsic restriction of the 17x1 preceptron.
Bibliographic Reference: Article: Food chemistry (1995)
Record Number: 199511216 / Last updated on: 1995-10-10
Original language: en
Available languages: en