Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine

Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making...
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Autor*in:	De Luca, M. [verfasserIn] Ioele, G. [verfasserIn] Ragno, G. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	drug analysis derivative spectrophotometry fluorometry principal component regression partial least squares regression

Übergeordnetes Werk:	Enthalten in: Journal of applied spectroscopy - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965, 87(2021), 6 vom: Jan., Seite 1079-1086
Übergeordnetes Werk:	volume:87 ; year:2021 ; number:6 ; month:01 ; pages:1079-1086

Links:	Volltext

DOI / URN:	10.1007/s10812-021-01112-8

Katalog-ID:	SPR042974895

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520			\|a Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%.
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allfields	10.1007/s10812-021-01112-8 doi (DE-627)SPR042974895 (DE-599)SPRs10812-021-01112-8-e (SPR)s10812-021-01112-8-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl De Luca, M. verfasserin aut Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%. drug analysis (dpeaa)DE-He213 derivative spectrophotometry (dpeaa)DE-He213 fluorometry (dpeaa)DE-He213 principal component regression (dpeaa)DE-He213 partial least squares regression (dpeaa)DE-He213 Ioele, G. verfasserin aut Ragno, G. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 87(2021), 6 vom: Jan., Seite 1079-1086 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:87 year:2021 number:6 month:01 pages:1079-1086 https://dx.doi.org/10.1007/s10812-021-01112-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 87 2021 6 01 1079-1086
spelling	10.1007/s10812-021-01112-8 doi (DE-627)SPR042974895 (DE-599)SPRs10812-021-01112-8-e (SPR)s10812-021-01112-8-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl De Luca, M. verfasserin aut Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%. drug analysis (dpeaa)DE-He213 derivative spectrophotometry (dpeaa)DE-He213 fluorometry (dpeaa)DE-He213 principal component regression (dpeaa)DE-He213 partial least squares regression (dpeaa)DE-He213 Ioele, G. verfasserin aut Ragno, G. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 87(2021), 6 vom: Jan., Seite 1079-1086 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:87 year:2021 number:6 month:01 pages:1079-1086 https://dx.doi.org/10.1007/s10812-021-01112-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 87 2021 6 01 1079-1086
allfields_unstemmed	10.1007/s10812-021-01112-8 doi (DE-627)SPR042974895 (DE-599)SPRs10812-021-01112-8-e (SPR)s10812-021-01112-8-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl De Luca, M. verfasserin aut Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%. drug analysis (dpeaa)DE-He213 derivative spectrophotometry (dpeaa)DE-He213 fluorometry (dpeaa)DE-He213 principal component regression (dpeaa)DE-He213 partial least squares regression (dpeaa)DE-He213 Ioele, G. verfasserin aut Ragno, G. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 87(2021), 6 vom: Jan., Seite 1079-1086 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:87 year:2021 number:6 month:01 pages:1079-1086 https://dx.doi.org/10.1007/s10812-021-01112-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 87 2021 6 01 1079-1086
allfieldsGer	10.1007/s10812-021-01112-8 doi (DE-627)SPR042974895 (DE-599)SPRs10812-021-01112-8-e (SPR)s10812-021-01112-8-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl De Luca, M. verfasserin aut Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%. drug analysis (dpeaa)DE-He213 derivative spectrophotometry (dpeaa)DE-He213 fluorometry (dpeaa)DE-He213 principal component regression (dpeaa)DE-He213 partial least squares regression (dpeaa)DE-He213 Ioele, G. verfasserin aut Ragno, G. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 87(2021), 6 vom: Jan., Seite 1079-1086 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:87 year:2021 number:6 month:01 pages:1079-1086 https://dx.doi.org/10.1007/s10812-021-01112-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 87 2021 6 01 1079-1086
allfieldsSound	10.1007/s10812-021-01112-8 doi (DE-627)SPR042974895 (DE-599)SPRs10812-021-01112-8-e (SPR)s10812-021-01112-8-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl De Luca, M. verfasserin aut Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%. drug analysis (dpeaa)DE-He213 derivative spectrophotometry (dpeaa)DE-He213 fluorometry (dpeaa)DE-He213 principal component regression (dpeaa)DE-He213 partial least squares regression (dpeaa)DE-He213 Ioele, G. verfasserin aut Ragno, G. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 87(2021), 6 vom: Jan., Seite 1079-1086 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:87 year:2021 number:6 month:01 pages:1079-1086 https://dx.doi.org/10.1007/s10812-021-01112-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 87 2021 6 01 1079-1086
language	English
source	Enthalten in Journal of applied spectroscopy 87(2021), 6 vom: Jan., Seite 1079-1086 volume:87 year:2021 number:6 month:01 pages:1079-1086
sourceStr	Enthalten in Journal of applied spectroscopy 87(2021), 6 vom: Jan., Seite 1079-1086 volume:87 year:2021 number:6 month:01 pages:1079-1086
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	drug analysis derivative spectrophotometry fluorometry principal component regression partial least squares regression
dewey-raw	530
isfreeaccess_bool	false
container_title	Journal of applied spectroscopy
authorswithroles_txt_mv	De Luca, M. @@aut@@ Ioele, G. @@aut@@ Ragno, G. @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	325609918
dewey-sort	3530
id	SPR042974895
language_de	englisch
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author	De Luca, M.
spellingShingle	De Luca, M. ddc 530 bkl 33.00 misc drug analysis misc derivative spectrophotometry misc fluorometry misc principal component regression misc partial least squares regression Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine
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topic_title	530 ASE 33.00 bkl Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine drug analysis (dpeaa)DE-He213 derivative spectrophotometry (dpeaa)DE-He213 fluorometry (dpeaa)DE-He213 principal component regression (dpeaa)DE-He213 partial least squares regression (dpeaa)DE-He213
topic	ddc 530 bkl 33.00 misc drug analysis misc derivative spectrophotometry misc fluorometry misc principal component regression misc partial least squares regression
topic_unstemmed	ddc 530 bkl 33.00 misc drug analysis misc derivative spectrophotometry misc fluorometry misc principal component regression misc partial least squares regression
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title	Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine
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title_full	Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine
author_sort	De Luca, M.
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author_browse	De Luca, M. Ioele, G. Ragno, G.
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title_sort	spectral data analysis for a complex drug mixture containing altizide, potassium canrenoate, and rescinnamine
title_auth	Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine
abstract	Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%.
abstractGer	Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%.
abstract_unstemmed	Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%.
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title_short	Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine
url	https://dx.doi.org/10.1007/s10812-021-01112-8
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author2	Ioele, G. Ragno, G.
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doi_str	10.1007/s10812-021-01112-8
up_date	2024-07-03T15:55:37.586Z
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Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine

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