Chemometric characterization of wines according to their HPTLC fingerprints
Само за регистроване кориснике
2017
Аутори
Agatonović-Kuštrin, SnežanaMilojković-Opsenica, Dušanka
Morton, David W.
Ristivojević, Petar
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The objective of this study was to determine which major grape varieties are present in a given wine using both high-performance thin-layer chromatography (HPTLC) fingerprinting and multivariate analysis. For this purpose, 40 mono-and multi-varietal commercial wine samples from four vintages between 2003 and 2012 were collected and analyzed for their polyphenolic composition using HPTLC peak profiles. Polyphenolic compounds such as gallic acid, caffeic acid, resveratrol and rutin (each belonging to one of the four common classes of wine polyphenolic antioxidants) were identified. Unsupervised chemometric method, principal component analysis was used to analyze variance in HPTLC patterns as a function of wine grape variety. An artificial neutral network, as the efficient supervised chemometric tool, was used to develop a predictive model for classification of wine samples and discrimination between them.
Кључне речи:
Artificial neural network / Grape variety / Polyphenolic composition / Principle component analysis / Red wineИзвор:
European Food Research and Technology, 2017, 243, 4, 659-667Издавач:
- Springer, New York
DOI: 10.1007/s00217-016-2779-9
ISSN: 1438-2377
WoS: 000397040300013
Scopus: 2-s2.0-84984783387
Институција/група
Hemijski fakultet / Faculty of ChemistryTY - JOUR AU - Agatonović-Kuštrin, Snežana AU - Milojković-Opsenica, Dušanka AU - Morton, David W. AU - Ristivojević, Petar PY - 2017 UR - https://cherry.chem.bg.ac.rs/handle/123456789/2437 AB - The objective of this study was to determine which major grape varieties are present in a given wine using both high-performance thin-layer chromatography (HPTLC) fingerprinting and multivariate analysis. For this purpose, 40 mono-and multi-varietal commercial wine samples from four vintages between 2003 and 2012 were collected and analyzed for their polyphenolic composition using HPTLC peak profiles. Polyphenolic compounds such as gallic acid, caffeic acid, resveratrol and rutin (each belonging to one of the four common classes of wine polyphenolic antioxidants) were identified. Unsupervised chemometric method, principal component analysis was used to analyze variance in HPTLC patterns as a function of wine grape variety. An artificial neutral network, as the efficient supervised chemometric tool, was used to develop a predictive model for classification of wine samples and discrimination between them. PB - Springer, New York T2 - European Food Research and Technology T1 - Chemometric characterization of wines according to their HPTLC fingerprints VL - 243 IS - 4 SP - 659 EP - 667 DO - 10.1007/s00217-016-2779-9 ER -
@article{ author = "Agatonović-Kuštrin, Snežana and Milojković-Opsenica, Dušanka and Morton, David W. and Ristivojević, Petar", year = "2017", abstract = "The objective of this study was to determine which major grape varieties are present in a given wine using both high-performance thin-layer chromatography (HPTLC) fingerprinting and multivariate analysis. For this purpose, 40 mono-and multi-varietal commercial wine samples from four vintages between 2003 and 2012 were collected and analyzed for their polyphenolic composition using HPTLC peak profiles. Polyphenolic compounds such as gallic acid, caffeic acid, resveratrol and rutin (each belonging to one of the four common classes of wine polyphenolic antioxidants) were identified. Unsupervised chemometric method, principal component analysis was used to analyze variance in HPTLC patterns as a function of wine grape variety. An artificial neutral network, as the efficient supervised chemometric tool, was used to develop a predictive model for classification of wine samples and discrimination between them.", publisher = "Springer, New York", journal = "European Food Research and Technology", title = "Chemometric characterization of wines according to their HPTLC fingerprints", volume = "243", number = "4", pages = "659-667", doi = "10.1007/s00217-016-2779-9" }
Agatonović-Kuštrin, S., Milojković-Opsenica, D., Morton, D. W.,& Ristivojević, P.. (2017). Chemometric characterization of wines according to their HPTLC fingerprints. in European Food Research and Technology Springer, New York., 243(4), 659-667. https://doi.org/10.1007/s00217-016-2779-9
Agatonović-Kuštrin S, Milojković-Opsenica D, Morton DW, Ristivojević P. Chemometric characterization of wines according to their HPTLC fingerprints. in European Food Research and Technology. 2017;243(4):659-667. doi:10.1007/s00217-016-2779-9 .
Agatonović-Kuštrin, Snežana, Milojković-Opsenica, Dušanka, Morton, David W., Ristivojević, Petar, "Chemometric characterization of wines according to their HPTLC fingerprints" in European Food Research and Technology, 243, no. 4 (2017):659-667, https://doi.org/10.1007/s00217-016-2779-9 . .