Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine
Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pargari, Maryam [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of analytical chemistry - Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000, 77(2022), 4 vom: Apr., Seite 484-494 |
|---|---|
| Übergeordnetes Werk: |
volume:77 ; year:2022 ; number:4 ; month:04 ; pages:484-494 |
| Links: |
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| DOI / URN: |
10.1134/S1061934822040074 |
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| Katalog-ID: |
SPR050635557 |
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| 100 | 1 | |a Pargari, Maryam |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine |
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| 500 | |a © Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. | ||
| 520 | |a Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. To the best of our knowledge, this is the first reported study applying a neural network model for the quantification of epinephrine by starch-capped AgNPs sensor in blood and urine. | ||
| 700 | 1 | |a Marahel, Farzaneh |4 aut | |
| 700 | 1 | |a Goodajdar, Bijan Mombeni |4 aut | |
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10.1134/S1061934822040074 doi (DE-627)SPR050635557 (SPR)S1061934822040074-e DE-627 ger DE-627 rakwb eng Pargari, Maryam verfasserin aut Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. To the best of our knowledge, this is the first reported study applying a neural network model for the quantification of epinephrine by starch-capped AgNPs sensor in blood and urine. Marahel, Farzaneh aut Goodajdar, Bijan Mombeni aut Enthalten in Journal of analytical chemistry Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 77(2022), 4 vom: Apr., Seite 484-494 (DE-627)334292689 (DE-600)2057336-4 1608-3199 nnns volume:77 year:2022 number:4 month:04 pages:484-494 https://dx.doi.org/10.1134/S1061934822040074 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 AR 77 2022 4 04 484-494 |
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10.1134/S1061934822040074 doi (DE-627)SPR050635557 (SPR)S1061934822040074-e DE-627 ger DE-627 rakwb eng Pargari, Maryam verfasserin aut Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. To the best of our knowledge, this is the first reported study applying a neural network model for the quantification of epinephrine by starch-capped AgNPs sensor in blood and urine. Marahel, Farzaneh aut Goodajdar, Bijan Mombeni aut Enthalten in Journal of analytical chemistry Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 77(2022), 4 vom: Apr., Seite 484-494 (DE-627)334292689 (DE-600)2057336-4 1608-3199 nnns volume:77 year:2022 number:4 month:04 pages:484-494 https://dx.doi.org/10.1134/S1061934822040074 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 AR 77 2022 4 04 484-494 |
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10.1134/S1061934822040074 doi (DE-627)SPR050635557 (SPR)S1061934822040074-e DE-627 ger DE-627 rakwb eng Pargari, Maryam verfasserin aut Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. To the best of our knowledge, this is the first reported study applying a neural network model for the quantification of epinephrine by starch-capped AgNPs sensor in blood and urine. Marahel, Farzaneh aut Goodajdar, Bijan Mombeni aut Enthalten in Journal of analytical chemistry Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 77(2022), 4 vom: Apr., Seite 484-494 (DE-627)334292689 (DE-600)2057336-4 1608-3199 nnns volume:77 year:2022 number:4 month:04 pages:484-494 https://dx.doi.org/10.1134/S1061934822040074 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 AR 77 2022 4 04 484-494 |
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10.1134/S1061934822040074 doi (DE-627)SPR050635557 (SPR)S1061934822040074-e DE-627 ger DE-627 rakwb eng Pargari, Maryam verfasserin aut Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. To the best of our knowledge, this is the first reported study applying a neural network model for the quantification of epinephrine by starch-capped AgNPs sensor in blood and urine. Marahel, Farzaneh aut Goodajdar, Bijan Mombeni aut Enthalten in Journal of analytical chemistry Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 77(2022), 4 vom: Apr., Seite 484-494 (DE-627)334292689 (DE-600)2057336-4 1608-3199 nnns volume:77 year:2022 number:4 month:04 pages:484-494 https://dx.doi.org/10.1134/S1061934822040074 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 AR 77 2022 4 04 484-494 |
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10.1134/S1061934822040074 doi (DE-627)SPR050635557 (SPR)S1061934822040074-e DE-627 ger DE-627 rakwb eng Pargari, Maryam verfasserin aut Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. To the best of our knowledge, this is the first reported study applying a neural network model for the quantification of epinephrine by starch-capped AgNPs sensor in blood and urine. Marahel, Farzaneh aut Goodajdar, Bijan Mombeni aut Enthalten in Journal of analytical chemistry Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 77(2022), 4 vom: Apr., Seite 484-494 (DE-627)334292689 (DE-600)2057336-4 1608-3199 nnns volume:77 year:2022 number:4 month:04 pages:484-494 https://dx.doi.org/10.1134/S1061934822040074 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 AR 77 2022 4 04 484-494 |
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ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. 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Pargari, Maryam |
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Pargari, Maryam Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine |
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kinetic spectrophotometric method and neural network model application for the quantitation of epinephrine by starch-capped agnps sensor in blood and urine |
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Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine |
| abstract |
Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. To the best of our knowledge, this is the first reported study applying a neural network model for the quantification of epinephrine by starch-capped AgNPs sensor in blood and urine. © Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. |
| abstractGer |
Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. To the best of our knowledge, this is the first reported study applying a neural network model for the quantification of epinephrine by starch-capped AgNPs sensor in blood and urine. © Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. |
| abstract_unstemmed |
Abstract In this study, a simple, accurate, precise, rapid, economical, and highly sensitive ultraviolet spectrophotometric method has been developed for the determination of epinephrine (EP) in real samples. Although the liquid chromatographic method for measuring epinephrine has such advantages as excellent accuracy and reproducibility, it has such limitations as long-time measurement, high equipment cost. For the determination of epinephrine in the solution, we used a starch-capped silver nanoparticles (AgNPs) sensor and a kinetic spectrophotometric method. The calibration curve was linear in the range of 0.01–10 µg/L. The standard deviation of 1.8% and the method detection limit of 0.023 µg/L (in 8 min at 425 nm) were obtained for the sensor at a 95% confidence level. The artificial neural network (ANN) model was used for the mean squared error (MSE) determination. The ANN model in predicting data with a linear transfer function algorithm at the output layer and tansig was helpful. Mean squared error values of 0.607, 0.515, and 0.408 corresponding to $ MSE_{ANN} $, $ MSE_{FL} $, and $ MSE_{ANFIS} $, respectively, in epinephrine determination by starch-capped AgNPs sensor were obtained. The observed outcomes confirmed the suitable recovery and very low detection limit for epinephrine. The method was used to measure epinephrine in real samples such as urine and blood and can be applied for hospital samples. To the best of our knowledge, this is the first reported study applying a neural network model for the quantification of epinephrine by starch-capped AgNPs sensor in blood and urine. © Pleiades Publishing, Ltd. 2022. ISSN 1061-9348, Journal of Analytical Chemistry, 2022, Vol. 77, No. 4, pp. 484–494. © Pleiades Publishing, Ltd., 2022. |
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Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine |
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Marahel, Farzaneh Goodajdar, Bijan Mombeni |
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| score |
7.400298 |