Mj. Benito et al., Typification of vinegars from Jerez and Rioja using classical chemometric techniques and neural network methods, ANALYST, 124(4), 1999, pp. 547-552
This study demonstrates that it is possible to characterise the vinegars ob
tained from wines with Certified Denomination of Origin Rioja (66 vinegars)
and Jerez (18 vinegars) according to their chemical composition. SIMCA was
used, along with cross-validation, as a modelling multivariate technique.
In order to demonstrate that no better sensitivity and specificity of SIMCA
can be achieved, a comparison was made with the results obtained by using
GINN, which is a neural network with stochastic learning, which directly op
timises both parameters. It was found that 92.9% of the classifications obt
ained by cross-validation with SIMCA were accurate, whereas with GINN 88.7%
were correct (median of 10 training steps). The sensitivity and specificit
y obtained with SIMCA were up to 85% for Rioja vinegars and 95% for Jerez v
inegars. The neural network gives higher values than those mentioned above
for these parameters which confirms their optimal character. The modelling
and discriminant capacities of the 20 chemical variables were also studied.