Commercial canned dog and cat foods (four type of each) were classified by electronic nose (EN) and tongue (ET) methods. The classification was performed by canonical discriminant analysis (DA) followed by cross-validation, using the ET and EN sensory values separately (7 and 18 sensors) and also jointly. The number of entered variables corresponding to the total number of sensors (n=25) were decreased by using a stepwise procedure during DA. First the dog and cat samples were classified than the discrimination were performed on the canned foods (eight type). Thereafter two groups were formed depending on the compositional characteristics of the foods (pure animal vs animal and plant origin), and finally these groups were divided into four subgroups according to the concerning species (dog vs cat). In general, the lowest discriminating results were achieved by the single application of ET method (58.3- 81.7 %). The highest classification power (85–98.3%, CV% 83.3–95.8) derived from the joint application of the two sensory methods. According to the results achieved, the common application of EN and ET technology seems to be a promising tool for the aroma classification of pet foods.

electronic nose; electronic tongue; classification; pet food; cat; dog

Published by University of Zagreb, Faculty of Agriculture, Zagreb, CROATIA
Sponsored by Ministry Science and Education of the Republic of Croatia