A CLASSIFICATION FUZZY MODEL FOR MINHOTA AND HOLSTEIN FRIESIAN COWS MILK BASED ON FUNCTIONAL FATTY ACIDS

• Type: Article in International Conference Proceedings Book
• Date: 2010

Authors

Heryka M M Ramalho (Author) Other; Susana Casal (Author) FFUP; Eulalia Mendes (Author) Other; Getulio Igrejas (Author) Other; Pedro Joao Rodrigues (Author) Other; Beatriz B P P Oliveira (Author) FFUP

Conference proceedings International

Title: 6TH INTERNATIONAL CONFERENCE ON SIMULATION AND MODELLING IN THE FOOD AND BIO-INDUSTRY (FOODSIM 2010)

Pages: 23-26

6th International Conference on Simulation and Modelling in the Food and Bio-Industry

Braganca, PORTUGAL, JUN 24-26, 2010

Indexing

ISI Web of Knowledge - 0 Citations

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Scientific classification

FOS: Agrarian Sciences > Other Agrarian Sciences

Other information

Authenticus ID: P-003-DGA

Abstract (EN): Recent studies have focused on the healthy components of milk fats, including polyunsaturated fatty acids (PUFA) of the omega-3 group, and conjugated linoleic acid (CLA). Several cows from Holstein Friesian and Minhota (autochthonous from the north of Portugal) were evaluated for their milk fatty acid composition, with the latter presenting significantly higher CLA amounts (P<0.05), lower saturated fatty acids and a smaller n6:n3 ratio. Considering these potentially functional differences, a classification system for an effective and fast discrimination of milks from different cow breeds would be an interesting tool for the dairy industry. In this work a Fuzzy model system was developed to classify milk samples as belonging to one of each evaluated breeds. For the total of 173 samples analyzed, the model identified correctly all the Friesian's milk but failed to classify 7 samples of the Minhota's milk, corresponding to a classification error of 7.5% within the class and a 4% error considering the total number of samples. These results are supported by the chemical data, with a higher variability observed in Minhota's milk composition.

Language: English

Type (Professor's evaluation): Scientific