Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy
Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopam...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, J. H. [verfasserIn] Yuan, H. C. [verfasserIn] Peng, Y. J. [verfasserIn] Hong, Q. [verfasserIn] Liu, M. H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of applied spectroscopy - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965, 84(2017), 1 vom: März, Seite 76-81 |
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| Übergeordnetes Werk: |
volume:84 ; year:2017 ; number:1 ; month:03 ; pages:76-81 |
| Links: |
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| DOI / URN: |
10.1007/s10812-017-0430-6 |
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| Katalog-ID: |
SPR013509470 |
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| 245 | 1 | 0 | |a Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy |
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| 520 | |a Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork. | ||
| 650 | 4 | |a ractopamine |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a clenbuterol hydrochloride |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a surface enhanced Raman spectroscopy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a pork |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a rapid detection and identification |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Yuan, H. C. |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Y. J. |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Q. |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, M. H. |e verfasserin |4 aut | |
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10.1007/s10812-017-0430-6 doi (DE-627)SPR013509470 (SPR)s10812-017-0430-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, J. H. verfasserin aut Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork. ractopamine (dpeaa)DE-He213 clenbuterol hydrochloride (dpeaa)DE-He213 surface enhanced Raman spectroscopy (dpeaa)DE-He213 pork (dpeaa)DE-He213 rapid detection and identification (dpeaa)DE-He213 Yuan, H. C. verfasserin aut Peng, Y. J. verfasserin aut Hong, Q. verfasserin aut Liu, M. H. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 84(2017), 1 vom: März, Seite 76-81 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:84 year:2017 number:1 month:03 pages:76-81 https://dx.doi.org/10.1007/s10812-017-0430-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 84 2017 1 03 76-81 |
| spelling |
10.1007/s10812-017-0430-6 doi (DE-627)SPR013509470 (SPR)s10812-017-0430-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, J. H. verfasserin aut Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork. ractopamine (dpeaa)DE-He213 clenbuterol hydrochloride (dpeaa)DE-He213 surface enhanced Raman spectroscopy (dpeaa)DE-He213 pork (dpeaa)DE-He213 rapid detection and identification (dpeaa)DE-He213 Yuan, H. C. verfasserin aut Peng, Y. J. verfasserin aut Hong, Q. verfasserin aut Liu, M. H. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 84(2017), 1 vom: März, Seite 76-81 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:84 year:2017 number:1 month:03 pages:76-81 https://dx.doi.org/10.1007/s10812-017-0430-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 84 2017 1 03 76-81 |
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10.1007/s10812-017-0430-6 doi (DE-627)SPR013509470 (SPR)s10812-017-0430-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, J. H. verfasserin aut Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork. ractopamine (dpeaa)DE-He213 clenbuterol hydrochloride (dpeaa)DE-He213 surface enhanced Raman spectroscopy (dpeaa)DE-He213 pork (dpeaa)DE-He213 rapid detection and identification (dpeaa)DE-He213 Yuan, H. C. verfasserin aut Peng, Y. J. verfasserin aut Hong, Q. verfasserin aut Liu, M. H. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 84(2017), 1 vom: März, Seite 76-81 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:84 year:2017 number:1 month:03 pages:76-81 https://dx.doi.org/10.1007/s10812-017-0430-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 84 2017 1 03 76-81 |
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10.1007/s10812-017-0430-6 doi (DE-627)SPR013509470 (SPR)s10812-017-0430-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, J. H. verfasserin aut Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork. ractopamine (dpeaa)DE-He213 clenbuterol hydrochloride (dpeaa)DE-He213 surface enhanced Raman spectroscopy (dpeaa)DE-He213 pork (dpeaa)DE-He213 rapid detection and identification (dpeaa)DE-He213 Yuan, H. C. verfasserin aut Peng, Y. J. verfasserin aut Hong, Q. verfasserin aut Liu, M. H. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 84(2017), 1 vom: März, Seite 76-81 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:84 year:2017 number:1 month:03 pages:76-81 https://dx.doi.org/10.1007/s10812-017-0430-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 84 2017 1 03 76-81 |
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10.1007/s10812-017-0430-6 doi (DE-627)SPR013509470 (SPR)s10812-017-0430-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, J. H. verfasserin aut Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork. ractopamine (dpeaa)DE-He213 clenbuterol hydrochloride (dpeaa)DE-He213 surface enhanced Raman spectroscopy (dpeaa)DE-He213 pork (dpeaa)DE-He213 rapid detection and identification (dpeaa)DE-He213 Yuan, H. C. verfasserin aut Peng, Y. J. verfasserin aut Hong, Q. verfasserin aut Liu, M. H. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 84(2017), 1 vom: März, Seite 76-81 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:84 year:2017 number:1 month:03 pages:76-81 https://dx.doi.org/10.1007/s10812-017-0430-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 84 2017 1 03 76-81 |
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H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ractopamine</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">clenbuterol hydrochloride</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">surface enhanced Raman spectroscopy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pork</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rapid detection and identification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yuan, H. 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|
| author |
Zhao, J. H. |
| spellingShingle |
Zhao, J. H. ddc 530 bkl 33.00 misc ractopamine misc clenbuterol hydrochloride misc surface enhanced Raman spectroscopy misc pork misc rapid detection and identification Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy |
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Zhao, J. H. |
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530 ASE 33.00 bkl Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy ractopamine (dpeaa)DE-He213 clenbuterol hydrochloride (dpeaa)DE-He213 surface enhanced Raman spectroscopy (dpeaa)DE-He213 pork (dpeaa)DE-He213 rapid detection and identification (dpeaa)DE-He213 |
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ddc 530 bkl 33.00 misc ractopamine misc clenbuterol hydrochloride misc surface enhanced Raman spectroscopy misc pork misc rapid detection and identification |
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ddc 530 bkl 33.00 misc ractopamine misc clenbuterol hydrochloride misc surface enhanced Raman spectroscopy misc pork misc rapid detection and identification |
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ddc 530 bkl 33.00 misc ractopamine misc clenbuterol hydrochloride misc surface enhanced Raman spectroscopy misc pork misc rapid detection and identification |
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Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy |
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(DE-627)SPR013509470 (SPR)s10812-017-0430-6-e |
| title_full |
Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy |
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Zhao, J. H. |
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Journal of applied spectroscopy |
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Zhao, J. H. Yuan, H. C. Peng, Y. J. Hong, Q. Liu, M. H. |
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detection of ractopamine and clenbuterol hydrochloride residues in pork using surface enhanced raman spectroscopy |
| title_auth |
Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy |
| abstract |
Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork. |
| abstractGer |
Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork. |
| abstract_unstemmed |
Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 $ cm^{–1} $ were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 $ cm^{–1} $ were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork. |
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| title_short |
Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy |
| url |
https://dx.doi.org/10.1007/s10812-017-0430-6 |
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| author2 |
Yuan, H. C. Peng, Y. J. Hong, Q. Liu, M. H. |
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Yuan, H. C. Peng, Y. J. Hong, Q. Liu, M. H. |
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| doi_str |
10.1007/s10812-017-0430-6 |
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2024-07-03T20:13:26.105Z |
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| score |
7.398549 |