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Discrimination of origin of farmed trout by means of biometrical parameters, fillet composition and flavor volatile compounds
oleh: Fabio Caprino, Vittorio Maria Moretti, Ivan Giani, Giovanni Mario Turchini, Franco Valfrè
Format: | Article |
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Diterbitkan: | Taylor & Francis Group 2010-01-01 |
Deskripsi
To date it is well known that the quality of farmed trout is affected by diet composition, by feeding regime, by husbandry<br />practices and by rearing conditions and environment. The trout processing industry and the large-scale retail trade, in consideration<br />of the wide variability of trout quality and characteristics, have imposed, or will soon impose, quality criteria for<br />the end product. Moreover, recent food scares and the malpractices of some food producers have increased public requests<br />for traceability. The aim of the present study was to evaluate the main chemical quality and the biometrical characteristics<br />of rainbow trout produced in three different farms in Italy (two intensive farms, located one on mountain and one on<br />plain, and an extensive farm in which fish fed only on naturally available nutrients) and to establish whether farmed trout<br />origins could be differentiated by these parameters. Trout farmed in the intensive mountain farm (IMF) showed the highest<br />crude lipid content in the fillets and the fatty acids of their fillets were characterized by the highest percentage of MUFA.<br />Trout farmed in the intensive plain farm (IPF) were characterized by low dressing percentage, and the lipid of their fillets<br />was rich in n-6 fatty acids. Trout stocked for the last year of their life in the extensive farm (EF) were leaner both in the<br />carcass and in the fillets. The analysis of flavor volatile compounds showed some differences in the bouquet design, particularly<br />differences in the amounts of n-3 and n-6 derivates volatile aldehydes and alcohols. All data significantly different<br />(P<0.05) were subjected to Linear Discriminant Analysis (LDA) and 8 variables were chosen to create two discriminant<br />equations generating a strong prediction model for classification of farmed trout respective to their origins.