Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose

The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to selec...
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Autor*in:	Wang, Honghong [verfasserIn] Hu, Yunchao [verfasserIn] Liu, Zhijian [verfasserIn] Wang, Ying [verfasserIn] Huang, Haoran [verfasserIn] Xiong, Zhixin [verfasserIn] Liang, Long [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Spectrum ratio analysis Direct Standardization optimization Holocellulose content Near infrared spectroscopy Model transfer

Übergeordnetes Werk:	Enthalten in: Infrared physics & technology - Amsterdam [u.a.] : Elsevier Science, 1994, 135
Übergeordnetes Werk:	volume:135

DOI / URN:	10.1016/j.infrared.2023.104981

Katalog-ID:	ELV065884574

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100	1		\|a Wang, Honghong \|e verfasserin \|0 (orcid)0000-0003-3319-2881 \|4 aut
245	1	0	\|a Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose
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520			\|a The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments.
650		4	\|a Spectrum ratio analysis
650		4	\|a Direct Standardization optimization
650		4	\|a Holocellulose content
650		4	\|a Near infrared spectroscopy
650		4	\|a Model transfer
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700	1		\|a Liu, Zhijian \|e verfasserin \|4 aut
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700	1		\|a Huang, Haoran \|e verfasserin \|4 aut
700	1		\|a Xiong, Zhixin \|e verfasserin \|4 aut
700	1		\|a Liang, Long \|e verfasserin \|4 aut
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allfields	10.1016/j.infrared.2023.104981 doi (DE-627)ELV065884574 (ELSEVIER)S1350-4495(23)00439-5 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Honghong verfasserin (orcid)0000-0003-3319-2881 aut Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments. Spectrum ratio analysis Direct Standardization optimization Holocellulose content Near infrared spectroscopy Model transfer Hu, Yunchao verfasserin aut Liu, Zhijian verfasserin aut Wang, Ying verfasserin aut Huang, Haoran verfasserin aut Xiong, Zhixin verfasserin aut Liang, Long verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 135 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:135 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 135
spelling	10.1016/j.infrared.2023.104981 doi (DE-627)ELV065884574 (ELSEVIER)S1350-4495(23)00439-5 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Honghong verfasserin (orcid)0000-0003-3319-2881 aut Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments. Spectrum ratio analysis Direct Standardization optimization Holocellulose content Near infrared spectroscopy Model transfer Hu, Yunchao verfasserin aut Liu, Zhijian verfasserin aut Wang, Ying verfasserin aut Huang, Haoran verfasserin aut Xiong, Zhixin verfasserin aut Liang, Long verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 135 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:135 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 135
allfields_unstemmed	10.1016/j.infrared.2023.104981 doi (DE-627)ELV065884574 (ELSEVIER)S1350-4495(23)00439-5 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Honghong verfasserin (orcid)0000-0003-3319-2881 aut Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments. Spectrum ratio analysis Direct Standardization optimization Holocellulose content Near infrared spectroscopy Model transfer Hu, Yunchao verfasserin aut Liu, Zhijian verfasserin aut Wang, Ying verfasserin aut Huang, Haoran verfasserin aut Xiong, Zhixin verfasserin aut Liang, Long verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 135 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:135 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 135
allfieldsGer	10.1016/j.infrared.2023.104981 doi (DE-627)ELV065884574 (ELSEVIER)S1350-4495(23)00439-5 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Honghong verfasserin (orcid)0000-0003-3319-2881 aut Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments. Spectrum ratio analysis Direct Standardization optimization Holocellulose content Near infrared spectroscopy Model transfer Hu, Yunchao verfasserin aut Liu, Zhijian verfasserin aut Wang, Ying verfasserin aut Huang, Haoran verfasserin aut Xiong, Zhixin verfasserin aut Liang, Long verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 135 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:135 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 135
allfieldsSound	10.1016/j.infrared.2023.104981 doi (DE-627)ELV065884574 (ELSEVIER)S1350-4495(23)00439-5 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Honghong verfasserin (orcid)0000-0003-3319-2881 aut Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments. Spectrum ratio analysis Direct Standardization optimization Holocellulose content Near infrared spectroscopy Model transfer Hu, Yunchao verfasserin aut Liu, Zhijian verfasserin aut Wang, Ying verfasserin aut Huang, Haoran verfasserin aut Xiong, Zhixin verfasserin aut Liang, Long verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 135 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:135 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 135
language	English
source	Enthalten in Infrared physics & technology 135 volume:135
sourceStr	Enthalten in Infrared physics & technology 135 volume:135
format_phy_str_mv	Article
bklname	Technische Optik Quantenoptik nichtlineare Optik Spektroskopie
institution	findex.gbv.de
topic_facet	Spectrum ratio analysis Direct Standardization optimization Holocellulose content Near infrared spectroscopy Model transfer
dewey-raw	530
isfreeaccess_bool	false
container_title	Infrared physics & technology
authorswithroles_txt_mv	Wang, Honghong @@aut@@ Hu, Yunchao @@aut@@ Liu, Zhijian @@aut@@ Wang, Ying @@aut@@ Huang, Haoran @@aut@@ Xiong, Zhixin @@aut@@ Liang, Long @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320592146
dewey-sort	3530
id	ELV065884574
language_de	englisch
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author	Wang, Honghong
spellingShingle	Wang, Honghong ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Spectrum ratio analysis misc Direct Standardization optimization misc Holocellulose content misc Near infrared spectroscopy misc Model transfer Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose
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topic_title	530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose Spectrum ratio analysis Direct Standardization optimization Holocellulose content Near infrared spectroscopy Model transfer
topic	ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Spectrum ratio analysis misc Direct Standardization optimization misc Holocellulose content misc Near infrared spectroscopy misc Model transfer
topic_unstemmed	ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Spectrum ratio analysis misc Direct Standardization optimization misc Holocellulose content misc Near infrared spectroscopy misc Model transfer
topic_browse	ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Spectrum ratio analysis misc Direct Standardization optimization misc Holocellulose content misc Near infrared spectroscopy misc Model transfer
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title	Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose
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title_full	Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose
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journal	Infrared physics & technology
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author_browse	Wang, Honghong Hu, Yunchao Liu, Zhijian Wang, Ying Huang, Haoran Xiong, Zhixin Liang, Long
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author-letter	Wang, Honghong
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title_sort	application of swsra-ds algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose
title_auth	Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose
abstract	The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments.
abstractGer	The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments.
abstract_unstemmed	The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments.
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title_short	Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose
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author2	Hu, Yunchao Liu, Zhijian Wang, Ying Huang, Haoran Xiong, Zhixin Liang, Long
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doi_str	10.1016/j.infrared.2023.104981
up_date	2024-07-07T00:35:07.670Z
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Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose

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