Unsupervised Pattern Recognition Chemometrics for Distinguishing Different Egyptian Olive Varieties Using a New Integrated Densitometric Reversed-Phase High-Performance Thin-Layer Chromatography—Image Analysis Technique
Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer c...
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| Autor*in: |
Ibrahim, Reham S. [verfasserIn] Zaatout, Hala H. [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of planar chromatography, modern TLC - Budapest : Akadémiai Kiadó, 2001, 32(2019), 6 vom: 01. Dez., Seite 453-460 |
|---|---|
| Übergeordnetes Werk: |
volume:32 ; year:2019 ; number:6 ; day:01 ; month:12 ; pages:453-460 |
| Links: |
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| DOI / URN: |
10.1556/1006.2019.32.6.2 |
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SPR039235130 |
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| 520 | |a Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency. | ||
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10.1556/1006.2019.32.6.2 doi (DE-627)SPR039235130 (SPR)1006.2019.32.6.2-e DE-627 ger DE-627 rakwb eng 540 530 ASE Ibrahim, Reham S. verfasserin aut Unsupervised Pattern Recognition Chemometrics for Distinguishing Different Egyptian Olive Varieties Using a New Integrated Densitometric Reversed-Phase High-Performance Thin-Layer Chromatography—Image Analysis Technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency. Zaatout, Hala H. verfasserin aut Enthalten in Journal of planar chromatography, modern TLC Budapest : Akadémiai Kiadó, 2001 32(2019), 6 vom: 01. Dez., Seite 453-460 (DE-627)529764563 (DE-600)2304198-5 1789-0993 nnns volume:32 year:2019 number:6 day:01 month:12 pages:453-460 https://dx.doi.org/10.1556/1006.2019.32.6.2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_22 GBV_ILN_31 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_4305 AR 32 2019 6 01 12 453-460 |
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10.1556/1006.2019.32.6.2 doi (DE-627)SPR039235130 (SPR)1006.2019.32.6.2-e DE-627 ger DE-627 rakwb eng 540 530 ASE Ibrahim, Reham S. verfasserin aut Unsupervised Pattern Recognition Chemometrics for Distinguishing Different Egyptian Olive Varieties Using a New Integrated Densitometric Reversed-Phase High-Performance Thin-Layer Chromatography—Image Analysis Technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency. Zaatout, Hala H. verfasserin aut Enthalten in Journal of planar chromatography, modern TLC Budapest : Akadémiai Kiadó, 2001 32(2019), 6 vom: 01. Dez., Seite 453-460 (DE-627)529764563 (DE-600)2304198-5 1789-0993 nnns volume:32 year:2019 number:6 day:01 month:12 pages:453-460 https://dx.doi.org/10.1556/1006.2019.32.6.2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_22 GBV_ILN_31 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_4305 AR 32 2019 6 01 12 453-460 |
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10.1556/1006.2019.32.6.2 doi (DE-627)SPR039235130 (SPR)1006.2019.32.6.2-e DE-627 ger DE-627 rakwb eng 540 530 ASE Ibrahim, Reham S. verfasserin aut Unsupervised Pattern Recognition Chemometrics for Distinguishing Different Egyptian Olive Varieties Using a New Integrated Densitometric Reversed-Phase High-Performance Thin-Layer Chromatography—Image Analysis Technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency. Zaatout, Hala H. verfasserin aut Enthalten in Journal of planar chromatography, modern TLC Budapest : Akadémiai Kiadó, 2001 32(2019), 6 vom: 01. Dez., Seite 453-460 (DE-627)529764563 (DE-600)2304198-5 1789-0993 nnns volume:32 year:2019 number:6 day:01 month:12 pages:453-460 https://dx.doi.org/10.1556/1006.2019.32.6.2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_22 GBV_ILN_31 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_4305 AR 32 2019 6 01 12 453-460 |
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10.1556/1006.2019.32.6.2 doi (DE-627)SPR039235130 (SPR)1006.2019.32.6.2-e DE-627 ger DE-627 rakwb eng 540 530 ASE Ibrahim, Reham S. verfasserin aut Unsupervised Pattern Recognition Chemometrics for Distinguishing Different Egyptian Olive Varieties Using a New Integrated Densitometric Reversed-Phase High-Performance Thin-Layer Chromatography—Image Analysis Technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency. Zaatout, Hala H. verfasserin aut Enthalten in Journal of planar chromatography, modern TLC Budapest : Akadémiai Kiadó, 2001 32(2019), 6 vom: 01. Dez., Seite 453-460 (DE-627)529764563 (DE-600)2304198-5 1789-0993 nnns volume:32 year:2019 number:6 day:01 month:12 pages:453-460 https://dx.doi.org/10.1556/1006.2019.32.6.2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_22 GBV_ILN_31 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_4305 AR 32 2019 6 01 12 453-460 |
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10.1556/1006.2019.32.6.2 doi (DE-627)SPR039235130 (SPR)1006.2019.32.6.2-e DE-627 ger DE-627 rakwb eng 540 530 ASE Ibrahim, Reham S. verfasserin aut Unsupervised Pattern Recognition Chemometrics for Distinguishing Different Egyptian Olive Varieties Using a New Integrated Densitometric Reversed-Phase High-Performance Thin-Layer Chromatography—Image Analysis Technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency. Zaatout, Hala H. verfasserin aut Enthalten in Journal of planar chromatography, modern TLC Budapest : Akadémiai Kiadó, 2001 32(2019), 6 vom: 01. Dez., Seite 453-460 (DE-627)529764563 (DE-600)2304198-5 1789-0993 nnns volume:32 year:2019 number:6 day:01 month:12 pages:453-460 https://dx.doi.org/10.1556/1006.2019.32.6.2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_22 GBV_ILN_31 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_4305 AR 32 2019 6 01 12 453-460 |
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Ibrahim, Reham S. Zaatout, Hala H. |
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Elektronische Aufsätze |
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Ibrahim, Reham S. |
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10.1556/1006.2019.32.6.2 |
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unsupervised pattern recognition chemometrics for distinguishing different egyptian olive varieties using a new integrated densitometric reversed-phase high-performance thin-layer chromatography—image analysis technique |
| title_auth |
Unsupervised Pattern Recognition Chemometrics for Distinguishing Different Egyptian Olive Varieties Using a New Integrated Densitometric Reversed-Phase High-Performance Thin-Layer Chromatography—Image Analysis Technique |
| abstract |
Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency. |
| abstractGer |
Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency. |
| abstract_unstemmed |
Summary The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency. |
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| container_issue |
6 |
| title_short |
Unsupervised Pattern Recognition Chemometrics for Distinguishing Different Egyptian Olive Varieties Using a New Integrated Densitometric Reversed-Phase High-Performance Thin-Layer Chromatography—Image Analysis Technique |
| url |
https://dx.doi.org/10.1556/1006.2019.32.6.2 |
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Zaatout, Hala H. |
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| up_date |
2024-07-03T22:49:14.110Z |
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