Repetition rate priority combination method based on equidistant wavelengths screening with application to NIR analysis of serum albumin
For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yao, Lijun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
Repetition rate priority combination partial least squares |
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| Umfang: |
6 |
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| Übergeordnetes Werk: |
Enthalten in: Migration and characterisation of nanosilver from food containers by AF4-ICP-MS - Artiaga, G. ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:162 ; year:2017 ; day:15 ; month:03 ; pages:191-196 ; extent:6 |
| Links: |
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| DOI / URN: |
10.1016/j.chemolab.2017.01.017 |
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ELV040343561 |
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| 245 | 1 | 0 | |a Repetition rate priority combination method based on equidistant wavelengths screening with application to NIR analysis of serum albumin |
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| 520 | |a For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. | ||
| 520 | |a For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. | ||
| 650 | 7 | |a Repetition rate priority combination partial least squares |2 Elsevier | |
| 650 | 7 | |a Human serum albumin |2 Elsevier | |
| 650 | 7 | |a Equidistant combination partial least squares |2 Elsevier | |
| 650 | 7 | |a Equivalence model set |2 Elsevier | |
| 650 | 7 | |a Near-infrared spectroscopy |2 Elsevier | |
| 700 | 1 | |a Tang, Yi |4 oth | |
| 700 | 1 | |a Yin, Zhiwei |4 oth | |
| 700 | 1 | |a Pan, Tao |4 oth | |
| 700 | 1 | |a Chen, Jiemei |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Artiaga, G. ELSEVIER |t Migration and characterisation of nanosilver from food containers by AF4-ICP-MS |d 2015 |g Amsterdam [u.a.] |w (DE-627)ELV012980455 |
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10.1016/j.chemolab.2017.01.017 doi GBV00000000000059A.pica (DE-627)ELV040343561 (ELSEVIER)S0169-7439(17)30049-7 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Yao, Lijun verfasserin aut Repetition rate priority combination method based on equidistant wavelengths screening with application to NIR analysis of serum albumin 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. Repetition rate priority combination partial least squares Elsevier Human serum albumin Elsevier Equidistant combination partial least squares Elsevier Equivalence model set Elsevier Near-infrared spectroscopy Elsevier Tang, Yi oth Yin, Zhiwei oth Pan, Tao oth Chen, Jiemei oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:162 year:2017 day:15 month:03 pages:191-196 extent:6 https://doi.org/10.1016/j.chemolab.2017.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 162 2017 15 0315 191-196 6 045F 540 |
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10.1016/j.chemolab.2017.01.017 doi GBV00000000000059A.pica (DE-627)ELV040343561 (ELSEVIER)S0169-7439(17)30049-7 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Yao, Lijun verfasserin aut Repetition rate priority combination method based on equidistant wavelengths screening with application to NIR analysis of serum albumin 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. Repetition rate priority combination partial least squares Elsevier Human serum albumin Elsevier Equidistant combination partial least squares Elsevier Equivalence model set Elsevier Near-infrared spectroscopy Elsevier Tang, Yi oth Yin, Zhiwei oth Pan, Tao oth Chen, Jiemei oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:162 year:2017 day:15 month:03 pages:191-196 extent:6 https://doi.org/10.1016/j.chemolab.2017.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 162 2017 15 0315 191-196 6 045F 540 |
| allfields_unstemmed |
10.1016/j.chemolab.2017.01.017 doi GBV00000000000059A.pica (DE-627)ELV040343561 (ELSEVIER)S0169-7439(17)30049-7 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Yao, Lijun verfasserin aut Repetition rate priority combination method based on equidistant wavelengths screening with application to NIR analysis of serum albumin 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. Repetition rate priority combination partial least squares Elsevier Human serum albumin Elsevier Equidistant combination partial least squares Elsevier Equivalence model set Elsevier Near-infrared spectroscopy Elsevier Tang, Yi oth Yin, Zhiwei oth Pan, Tao oth Chen, Jiemei oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:162 year:2017 day:15 month:03 pages:191-196 extent:6 https://doi.org/10.1016/j.chemolab.2017.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 162 2017 15 0315 191-196 6 045F 540 |
| allfieldsGer |
10.1016/j.chemolab.2017.01.017 doi GBV00000000000059A.pica (DE-627)ELV040343561 (ELSEVIER)S0169-7439(17)30049-7 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Yao, Lijun verfasserin aut Repetition rate priority combination method based on equidistant wavelengths screening with application to NIR analysis of serum albumin 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. Repetition rate priority combination partial least squares Elsevier Human serum albumin Elsevier Equidistant combination partial least squares Elsevier Equivalence model set Elsevier Near-infrared spectroscopy Elsevier Tang, Yi oth Yin, Zhiwei oth Pan, Tao oth Chen, Jiemei oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:162 year:2017 day:15 month:03 pages:191-196 extent:6 https://doi.org/10.1016/j.chemolab.2017.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 162 2017 15 0315 191-196 6 045F 540 |
| allfieldsSound |
10.1016/j.chemolab.2017.01.017 doi GBV00000000000059A.pica (DE-627)ELV040343561 (ELSEVIER)S0169-7439(17)30049-7 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Yao, Lijun verfasserin aut Repetition rate priority combination method based on equidistant wavelengths screening with application to NIR analysis of serum albumin 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. Repetition rate priority combination partial least squares Elsevier Human serum albumin Elsevier Equidistant combination partial least squares Elsevier Equivalence model set Elsevier Near-infrared spectroscopy Elsevier Tang, Yi oth Yin, Zhiwei oth Pan, Tao oth Chen, Jiemei oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:162 year:2017 day:15 month:03 pages:191-196 extent:6 https://doi.org/10.1016/j.chemolab.2017.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 162 2017 15 0315 191-196 6 045F 540 |
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repetition rate priority combination method based on equidistant wavelengths screening with application to nir analysis of serum albumin |
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Repetition rate priority combination method based on equidistant wavelengths screening with application to NIR analysis of serum albumin |
| abstract |
For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. |
| abstractGer |
For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. |
| abstract_unstemmed |
For the rapid measurement of an analyte in complex samples using near-infrared (NIR) spectroscopy, appropriate wavelength selection is an important and albeit difficult aspect, which is essential for improving prediction performance. Based on equidistant combination partial least squares (EC-PLS), an equivalence model set was proposed. A wavelength selection method, called repetition rate priority combination PLS (RRPC-PLS), was further proposed and applied for NIR analysis of human serum albumin. The competitive adaptive reweighted sampling combined PLS (CARS-PLS) and EC-PLS, which are well-performed wavelength selection methods, were also conducted for comparison. Based on the various divisions of calibration and prediction sets, the modeling was performed to achieve parameter stability. The posterior sample group excluded in modeling was used to validate and achieve an objective evaluation. Using CARS-PLS, EC-PLS and RRPC-PLS methods, the selected optimal models included 32, 36 and 24 wavelengths, respectively. A simpler and high performance model with 15 wavelengths was also selected with RRPC-PLS method. The root-mean-square errors and correlation coefficients for validation were 0.505gL−1 and 0.997 for the optimal RRPC-PLS model and 0.530gL−1 and 0.997 for the RRPC-PLS model (N=15), respectively. The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. The results showed that RRPC-PLS is the good improvement on EC-PLS, which can be more effective to enhance the prediction performance and remove the redundant wavelengths. |
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The validation effects were superior to the previous two methods in two aspects of prediction performance and model complexity. The prediction values were close to the measured values with high precision. 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