Application of image analysis and artificial neural networks to the prediction in-line of OTR in oak wood planks for cooperage
open access
Volume 181, 5 November 2019, 107979
Oxygen Transmission Rate (OTR) is an important property of the wood
employed in cooperage because of its relationship with the
characteristics attained by the wine during the aging process.
Nevertheless, this property has not been considered in the barrel making
process because the time and systems required for measuring it do not
allow its integration into a production line. This article proposes a
method to classify the staves that compose each barrel in order to be
able to build low-OTR barrels and high-OTR ones depending on the OTRs of
the oak staves with significantly different levels among them. This
method uses eight anatomical and physical parameters of the wood, which
could be measured with in-line non-destructive methods, and a multilayer
perceptron artificial neural network (MLP-ANN) to estimate the
toasted-staves OTR value. Finally, the staves are classified according
to their estimated OTR in three groups: low-OTR, high-OTR and those in
between as the third group. The proposed stave classification system
makes it possible to build oak barrels with different OTRs. Thus barrels
with high OTRs with an average wood OTR almost three times higher than
those with low OTRs could be built.
