Pattern recognition methods and multivariate image analysis in HPTLC fingerprinting of propolis extracts
Само за регистроване кориснике
2014
Аутори
Ristivojević, PetarAndrić, Filip
Trifković, Jelena
Vovk, Irena
Stanisavljević, Ljubiša
Tešić, Živoslav Lj.
Milojković-Opsenica, Dušanka
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
High-performance thin-layer chromatography (HPTLC) combined with image analysis and pattern recognition methods were used for fingerprinting and classification of 52 propolis samples collected from Serbia and one sample from Croatia. Modern thin-layer chromatography equipment in combination with software for image processing and warping was applied for fingerprinting and data acquisition. The three mostly used chemometric techniques for classification, principal component analysis, cluster analysis and partial least square-discriminant analysis, in combination with simple and fast HPTLC method for fingerprint analysis of propolis, were performed in order to favor and encourage their use in planar chromatography. HPTLC fingerprint analysis of propolis was for the first time performed on amino silica plates. All studied propolis samples have been classified in two major types, orange and blue, supporting the idea of existence of two types of European propolis. Signals at specific R-F val...ues responsible for classification of studied extracts have also been isolated and underlying compounds targeted for further investigation. Copyright (c) 2014 John Wiley & Sons, Ltd. High-performance thin-layer chromatography combined with image analysis and pattern recognition methods were used for fingerprinting and classification of 53 propolis samples. High-performance thin-layer chromatography analysis of propolis was for the first time performed on amino silica plates. All studied propolis samples have been classified in two major types, orange and blue, supporting the idea of existence of two varieties of European propolis. Signals at specific R-F values responsible for classification of studied extracts have also been isolated and underlying compounds targeted for further investigation.
Кључне речи:
pattern recognition methods / image processing / dynamic time warping / HPTLC / propolisИзвор:
Journal of Chemometrics, 2014, 28, 4, 301-310Издавач:
- Wiley-Blackwell, Hoboken
Финансирање / пројекти:
- Корелација структуре и особина природних и синтетичких молекула и њихових комплекса са металима (RS-MESTD-Basic Research (BR or ON)-172017)
- EN-FIST Centre of Excellence
- Slovenian Research Agency [P1-0005]
DOI: 10.1002/cem.2592
ISSN: 0886-9383
WoS: 000333753400010
Scopus: 2-s2.0-84897566440
Институција/група
Hemijski fakultet / Faculty of ChemistryTY - JOUR AU - Ristivojević, Petar AU - Andrić, Filip AU - Trifković, Jelena AU - Vovk, Irena AU - Stanisavljević, Ljubiša AU - Tešić, Živoslav Lj. AU - Milojković-Opsenica, Dušanka PY - 2014 UR - https://cherry.chem.bg.ac.rs/handle/123456789/1521 AB - High-performance thin-layer chromatography (HPTLC) combined with image analysis and pattern recognition methods were used for fingerprinting and classification of 52 propolis samples collected from Serbia and one sample from Croatia. Modern thin-layer chromatography equipment in combination with software for image processing and warping was applied for fingerprinting and data acquisition. The three mostly used chemometric techniques for classification, principal component analysis, cluster analysis and partial least square-discriminant analysis, in combination with simple and fast HPTLC method for fingerprint analysis of propolis, were performed in order to favor and encourage their use in planar chromatography. HPTLC fingerprint analysis of propolis was for the first time performed on amino silica plates. All studied propolis samples have been classified in two major types, orange and blue, supporting the idea of existence of two types of European propolis. Signals at specific R-F values responsible for classification of studied extracts have also been isolated and underlying compounds targeted for further investigation. Copyright (c) 2014 John Wiley & Sons, Ltd. High-performance thin-layer chromatography combined with image analysis and pattern recognition methods were used for fingerprinting and classification of 53 propolis samples. High-performance thin-layer chromatography analysis of propolis was for the first time performed on amino silica plates. All studied propolis samples have been classified in two major types, orange and blue, supporting the idea of existence of two varieties of European propolis. Signals at specific R-F values responsible for classification of studied extracts have also been isolated and underlying compounds targeted for further investigation. PB - Wiley-Blackwell, Hoboken T2 - Journal of Chemometrics T1 - Pattern recognition methods and multivariate image analysis in HPTLC fingerprinting of propolis extracts VL - 28 IS - 4 SP - 301 EP - 310 DO - 10.1002/cem.2592 ER -
@article{ author = "Ristivojević, Petar and Andrić, Filip and Trifković, Jelena and Vovk, Irena and Stanisavljević, Ljubiša and Tešić, Živoslav Lj. and Milojković-Opsenica, Dušanka", year = "2014", abstract = "High-performance thin-layer chromatography (HPTLC) combined with image analysis and pattern recognition methods were used for fingerprinting and classification of 52 propolis samples collected from Serbia and one sample from Croatia. Modern thin-layer chromatography equipment in combination with software for image processing and warping was applied for fingerprinting and data acquisition. The three mostly used chemometric techniques for classification, principal component analysis, cluster analysis and partial least square-discriminant analysis, in combination with simple and fast HPTLC method for fingerprint analysis of propolis, were performed in order to favor and encourage their use in planar chromatography. HPTLC fingerprint analysis of propolis was for the first time performed on amino silica plates. All studied propolis samples have been classified in two major types, orange and blue, supporting the idea of existence of two types of European propolis. Signals at specific R-F values responsible for classification of studied extracts have also been isolated and underlying compounds targeted for further investigation. Copyright (c) 2014 John Wiley & Sons, Ltd. High-performance thin-layer chromatography combined with image analysis and pattern recognition methods were used for fingerprinting and classification of 53 propolis samples. High-performance thin-layer chromatography analysis of propolis was for the first time performed on amino silica plates. All studied propolis samples have been classified in two major types, orange and blue, supporting the idea of existence of two varieties of European propolis. Signals at specific R-F values responsible for classification of studied extracts have also been isolated and underlying compounds targeted for further investigation.", publisher = "Wiley-Blackwell, Hoboken", journal = "Journal of Chemometrics", title = "Pattern recognition methods and multivariate image analysis in HPTLC fingerprinting of propolis extracts", volume = "28", number = "4", pages = "301-310", doi = "10.1002/cem.2592" }
Ristivojević, P., Andrić, F., Trifković, J., Vovk, I., Stanisavljević, L., Tešić, Ž. Lj.,& Milojković-Opsenica, D.. (2014). Pattern recognition methods and multivariate image analysis in HPTLC fingerprinting of propolis extracts. in Journal of Chemometrics Wiley-Blackwell, Hoboken., 28(4), 301-310. https://doi.org/10.1002/cem.2592
Ristivojević P, Andrić F, Trifković J, Vovk I, Stanisavljević L, Tešić ŽL, Milojković-Opsenica D. Pattern recognition methods and multivariate image analysis in HPTLC fingerprinting of propolis extracts. in Journal of Chemometrics. 2014;28(4):301-310. doi:10.1002/cem.2592 .
Ristivojević, Petar, Andrić, Filip, Trifković, Jelena, Vovk, Irena, Stanisavljević, Ljubiša, Tešić, Živoslav Lj., Milojković-Opsenica, Dušanka, "Pattern recognition methods and multivariate image analysis in HPTLC fingerprinting of propolis extracts" in Journal of Chemometrics, 28, no. 4 (2014):301-310, https://doi.org/10.1002/cem.2592 . .