Progress in modeling quality in aquaculture: an application of the Self‐Organizing Map to the study of skeletal anomalies and meristic counts in gilthead seabream (Sparus aurata, L. 1758)
2010
One of the most common drawbacks of artificial life conditions
imposed by aquaculture is the quite high presence of skeletal
anomalies (SAs) in reared fish, which reduce both functional
performances and marketing image ⁄ commercial value of the
reared lots. Thus, skeletal malformations and their incidence
are one of the most important factors affecting fish farmer s
production costs, and several efforts have been due to develop
appropriate tools in detecting patterns of co-variation among
rearing parameters and fish quality. In this paper we explore
the advantages of using Self-Organized Maps (SOMs) when
dealing with the analysis of correlations between the pattern of
SA presence and rearing parameters in gilthead seabream
(Sparus aurata L.), that is a largely reared fish of high
commercial value. SOM, which is one of the best known neural
networks with unsupervised learning rules, were applied to
develop a model of the occurrence of SAs, both in terms of
type and quantity, in seabream lots from different rearing
approaches (extensive, semi-intensive and intensive). The
trained SOMs classified lots according to the variation
observed in the different weights of SAs, but also allows the
detection of a series of correspondence, namely between: (i) the
patter of SAs occurrence and the different rearing approach
currently used in seabream aquaculture; and (ii) the total SAs
incidence and the variability of meristic counts, represent a
completely independent dataset. Mesocosms resulted the best
rearing approach to produce wild-like fish, whereas intensive
rearing is characterized by the large presence of SA. Globally,
results suggested that this approach is reliable to be used for
estimate the distance between aquaculture products and the
wild-like phenotype used as quality reference.
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