Abstract
In this work, an electronic nose and a human panel were used for the quantification of wines formed by binary mixtures of four white grape varieties and two varieties of red wines at different percentages (from 0 to 100% in 10% steps for the electronic nose and from 0 to 100% in 25% steps for the human panel). The wines were prepared using the traditional method with commercial yeasts. Both techniques were able to quantify the mixtures tested, but it is important to note that the technology of the electronic nose is faster, simpler, and more objective than the human panel. In addition, better results of quantification were also obtained using the electronic nose.
Highlights
Nowadays, more information is required on the food products that the citizen consumes and even more on those with high added value, such as wine
There are specific provisions governing the labeling of the various wine products and containing mandatory and optional indications for the labeling of each category of products
The root mean square error (RMSE) of the prediction and the regression coefficients (R), for all the mixtures tested by the electronic nose, using the Partial least Artificial squares neural network (PLS) and ANN algorithms, are shown in Tables 5 and 6
Summary
More information is required on the food products that the citizen consumes and even more on those with high added value, such as wine. The labeling of wine products should mention certain characteristics of the product, such as the alcoholic strength and the presence of sulfites. There is a European Regulation, Regulation (EC) No 1493/1999, on the common organization of the market in wine. The rules of this regulation help consumers to better understand the specificities of wine products and guarantee to producers, the value of the quality of their products. The purpose of this regulation is to protect the interests of consumers and producers through the establishment of certain implementing provisions
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