Bio-electronic tongue was linked to artificial intelligence processing unit and used for classification of wines based on carboxylic acids levels, which were indirectly related to malolactic fermentation. The system employed amperometric biosensors with lactate oxidase, sarcosine oxidase, and fumarase/sarcosine oxidase in the three sensing channels. The results were processed using two statistical methods - principal component analysis (PCA) and self-organized maps (SOM) in order to classify 31 wine samples from the South Moravia region in the Czech Republic. Reference assays were carried out using the capillary electrophoresis (CE). The PCA patterns for both CE and biosensor data provided good correspondence in the clusters of samples. The SOM treatment provided a better resolution of the generated patterns of samples compared to PCA, the SOM derived clusters corresponded with the PCA classification only partially. The biosensor/SOM combination offers a novel procedure of wine classif...ication.
TY - JOUR
AU - Milovanović, Miodrag
AU - Zeravik, Jiri
AU - Oboril, Michal
AU - Pelcova, Marta
AU - Lacina, Karel
AU - Čakar, Uroš D.
AU - Petrović, Aleksandar
AU - Glatz, Zdenek
AU - Skladal, Petr
PY - 2019
UR - http://aspace.agrif.bg.ac.rs/handle/123456789/5035
AB - Bio-electronic tongue was linked to artificial intelligence processing unit and used for classification of wines based on carboxylic acids levels, which were indirectly related to malolactic fermentation. The system employed amperometric biosensors with lactate oxidase, sarcosine oxidase, and fumarase/sarcosine oxidase in the three sensing channels. The results were processed using two statistical methods - principal component analysis (PCA) and self-organized maps (SOM) in order to classify 31 wine samples from the South Moravia region in the Czech Republic. Reference assays were carried out using the capillary electrophoresis (CE). The PCA patterns for both CE and biosensor data provided good correspondence in the clusters of samples. The SOM treatment provided a better resolution of the generated patterns of samples compared to PCA, the SOM derived clusters corresponded with the PCA classification only partially. The biosensor/SOM combination offers a novel procedure of wine classification.
PB - Elsevier Sci Ltd, Oxford
T2 - Food Chemistry
T1 - A novel method for classification of wine based on organic acids
EP - 302
SP - 296
VL - 284
DO - 10.1016/j.foodchem.2019.01.113
ER -
@article{
author = "Milovanović, Miodrag and Zeravik, Jiri and Oboril, Michal and Pelcova, Marta and Lacina, Karel and Čakar, Uroš D. and Petrović, Aleksandar and Glatz, Zdenek and Skladal, Petr",
year = "2019",
abstract = "Bio-electronic tongue was linked to artificial intelligence processing unit and used for classification of wines based on carboxylic acids levels, which were indirectly related to malolactic fermentation. The system employed amperometric biosensors with lactate oxidase, sarcosine oxidase, and fumarase/sarcosine oxidase in the three sensing channels. The results were processed using two statistical methods - principal component analysis (PCA) and self-organized maps (SOM) in order to classify 31 wine samples from the South Moravia region in the Czech Republic. Reference assays were carried out using the capillary electrophoresis (CE). The PCA patterns for both CE and biosensor data provided good correspondence in the clusters of samples. The SOM treatment provided a better resolution of the generated patterns of samples compared to PCA, the SOM derived clusters corresponded with the PCA classification only partially. The biosensor/SOM combination offers a novel procedure of wine classification.",
publisher = "Elsevier Sci Ltd, Oxford",
journal = "Food Chemistry",
title = "A novel method for classification of wine based on organic acids",
pages = "302-296",
volume = "284",
doi = "10.1016/j.foodchem.2019.01.113"
}
Milovanović, M., Zeravik, J., Oboril, M., Pelcova, M., Lacina, K., Čakar, U. D., Petrović, A., Glatz, Z.,& Skladal, P.. (2019). A novel method for classification of wine based on organic acids. in Food Chemistry
Elsevier Sci Ltd, Oxford., 284, 296-302.
https://doi.org/10.1016/j.foodchem.2019.01.113
Milovanović M, Zeravik J, Oboril M, Pelcova M, Lacina K, Čakar UD, Petrović A, Glatz Z, Skladal P. A novel method for classification of wine based on organic acids. in Food Chemistry. 2019;284:296-302.
doi:10.1016/j.foodchem.2019.01.113 .
Milovanović, Miodrag, Zeravik, Jiri, Oboril, Michal, Pelcova, Marta, Lacina, Karel, Čakar, Uroš D., Petrović, Aleksandar, Glatz, Zdenek, Skladal, Petr, "A novel method for classification of wine based on organic acids" in Food Chemistry, 284 (2019):296-302,
https://doi.org/10.1016/j.foodchem.2019.01.113 . .
