Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry

For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristi...
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Gespeichert in:

Autor*in:	Zhou, Man [verfasserIn] Wang, Li [verfasserIn] Wu, Hejun [verfasserIn] Li, Qingye [verfasserIn] Li, Meiliang [verfasserIn] Zhang, Zhiqing [verfasserIn] Zhao, Yongpeng [verfasserIn] Lu, Zhiwei [verfasserIn] Zou, Zhiyong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Peanut protein Hyperspectral imaging technology Spectrophotometry MF—XGBoost—Ridge Optimal model

Übergeordnetes Werk:	Enthalten in: LWT - food science and technology - Amsterdam [u.a.] : Elsevier, 1993, 169
Übergeordnetes Werk:	volume:169

DOI / URN:	10.1016/j.lwt.2022.114015

Katalog-ID:	ELV059153288

Internformat


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245	1	0	\|a Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry
264		1	\|c 2022
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337			\|a Computermedien \|b c \|2 rdamedia
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520			\|a For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short.
650		4	\|a Peanut protein
650		4	\|a Hyperspectral imaging technology
650		4	\|a Spectrophotometry
650		4	\|a MF—XGBoost—Ridge
650		4	\|a Optimal model
700	1		\|a Wang, Li \|e verfasserin \|4 aut
700	1		\|a Wu, Hejun \|e verfasserin \|4 aut
700	1		\|a Li, Qingye \|e verfasserin \|4 aut
700	1		\|a Li, Meiliang \|e verfasserin \|4 aut
700	1		\|a Zhang, Zhiqing \|e verfasserin \|4 aut
700	1		\|a Zhao, Yongpeng \|e verfasserin \|4 aut
700	1		\|a Lu, Zhiwei \|e verfasserin \|4 aut
700	1		\|a Zou, Zhiyong \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t LWT - food science and technology \|d Amsterdam [u.a.] : Elsevier, 1993 \|g 169 \|h Online-Ressource \|w (DE-627)266892248 \|w (DE-600)1469139-5 \|w (DE-576)103373179 \|7 nnns
773	1	8	\|g volume:169
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936	b	k	\|a 48.00 \|j Land- und Forstwirtschaft: Allgemeines \|q VZ
936	b	k	\|a 58.34 \|j Lebensmitteltechnologie \|q VZ
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Indexfelder

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publishDate	2022
allfields	10.1016/j.lwt.2022.114015 doi (DE-627)ELV059153288 (ELSEVIER)S0023-6438(22)00950-1 DE-627 ger DE-627 rda eng 660 VZ 48.00 bkl 58.34 bkl Zhou, Man verfasserin aut Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short. Peanut protein Hyperspectral imaging technology Spectrophotometry MF—XGBoost—Ridge Optimal model Wang, Li verfasserin aut Wu, Hejun verfasserin aut Li, Qingye verfasserin aut Li, Meiliang verfasserin aut Zhang, Zhiqing verfasserin aut Zhao, Yongpeng verfasserin aut Lu, Zhiwei verfasserin aut Zou, Zhiyong verfasserin aut Enthalten in LWT - food science and technology Amsterdam [u.a.] : Elsevier, 1993 169 Online-Ressource (DE-627)266892248 (DE-600)1469139-5 (DE-576)103373179 nnns volume:169 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2031 GBV_ILN_2034 GBV_ILN_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ 58.34 Lebensmitteltechnologie VZ AR 169
spelling	10.1016/j.lwt.2022.114015 doi (DE-627)ELV059153288 (ELSEVIER)S0023-6438(22)00950-1 DE-627 ger DE-627 rda eng 660 VZ 48.00 bkl 58.34 bkl Zhou, Man verfasserin aut Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short. Peanut protein Hyperspectral imaging technology Spectrophotometry MF—XGBoost—Ridge Optimal model Wang, Li verfasserin aut Wu, Hejun verfasserin aut Li, Qingye verfasserin aut Li, Meiliang verfasserin aut Zhang, Zhiqing verfasserin aut Zhao, Yongpeng verfasserin aut Lu, Zhiwei verfasserin aut Zou, Zhiyong verfasserin aut Enthalten in LWT - food science and technology Amsterdam [u.a.] : Elsevier, 1993 169 Online-Ressource (DE-627)266892248 (DE-600)1469139-5 (DE-576)103373179 nnns volume:169 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2031 GBV_ILN_2034 GBV_ILN_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ 58.34 Lebensmitteltechnologie VZ AR 169
allfields_unstemmed	10.1016/j.lwt.2022.114015 doi (DE-627)ELV059153288 (ELSEVIER)S0023-6438(22)00950-1 DE-627 ger DE-627 rda eng 660 VZ 48.00 bkl 58.34 bkl Zhou, Man verfasserin aut Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short. Peanut protein Hyperspectral imaging technology Spectrophotometry MF—XGBoost—Ridge Optimal model Wang, Li verfasserin aut Wu, Hejun verfasserin aut Li, Qingye verfasserin aut Li, Meiliang verfasserin aut Zhang, Zhiqing verfasserin aut Zhao, Yongpeng verfasserin aut Lu, Zhiwei verfasserin aut Zou, Zhiyong verfasserin aut Enthalten in LWT - food science and technology Amsterdam [u.a.] : Elsevier, 1993 169 Online-Ressource (DE-627)266892248 (DE-600)1469139-5 (DE-576)103373179 nnns volume:169 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2031 GBV_ILN_2034 GBV_ILN_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ 58.34 Lebensmitteltechnologie VZ AR 169
allfieldsGer	10.1016/j.lwt.2022.114015 doi (DE-627)ELV059153288 (ELSEVIER)S0023-6438(22)00950-1 DE-627 ger DE-627 rda eng 660 VZ 48.00 bkl 58.34 bkl Zhou, Man verfasserin aut Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short. Peanut protein Hyperspectral imaging technology Spectrophotometry MF—XGBoost—Ridge Optimal model Wang, Li verfasserin aut Wu, Hejun verfasserin aut Li, Qingye verfasserin aut Li, Meiliang verfasserin aut Zhang, Zhiqing verfasserin aut Zhao, Yongpeng verfasserin aut Lu, Zhiwei verfasserin aut Zou, Zhiyong verfasserin aut Enthalten in LWT - food science and technology Amsterdam [u.a.] : Elsevier, 1993 169 Online-Ressource (DE-627)266892248 (DE-600)1469139-5 (DE-576)103373179 nnns volume:169 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2031 GBV_ILN_2034 GBV_ILN_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ 58.34 Lebensmitteltechnologie VZ AR 169
allfieldsSound	10.1016/j.lwt.2022.114015 doi (DE-627)ELV059153288 (ELSEVIER)S0023-6438(22)00950-1 DE-627 ger DE-627 rda eng 660 VZ 48.00 bkl 58.34 bkl Zhou, Man verfasserin aut Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short. Peanut protein Hyperspectral imaging technology Spectrophotometry MF—XGBoost—Ridge Optimal model Wang, Li verfasserin aut Wu, Hejun verfasserin aut Li, Qingye verfasserin aut Li, Meiliang verfasserin aut Zhang, Zhiqing verfasserin aut Zhao, Yongpeng verfasserin aut Lu, Zhiwei verfasserin aut Zou, Zhiyong verfasserin aut Enthalten in LWT - food science and technology Amsterdam [u.a.] : Elsevier, 1993 169 Online-Ressource (DE-627)266892248 (DE-600)1469139-5 (DE-576)103373179 nnns volume:169 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2031 GBV_ILN_2034 GBV_ILN_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ 58.34 Lebensmitteltechnologie VZ AR 169
language	English
source	Enthalten in LWT - food science and technology 169 volume:169
sourceStr	Enthalten in LWT - food science and technology 169 volume:169
format_phy_str_mv	Article
bklname	Land- und Forstwirtschaft: Allgemeines Lebensmitteltechnologie
institution	findex.gbv.de
topic_facet	Peanut protein Hyperspectral imaging technology Spectrophotometry MF—XGBoost—Ridge Optimal model
dewey-raw	660
isfreeaccess_bool	false
container_title	LWT - food science and technology
authorswithroles_txt_mv	Zhou, Man @@aut@@ Wang, Li @@aut@@ Wu, Hejun @@aut@@ Li, Qingye @@aut@@ Li, Meiliang @@aut@@ Zhang, Zhiqing @@aut@@ Zhao, Yongpeng @@aut@@ Lu, Zhiwei @@aut@@ Zou, Zhiyong @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	266892248
dewey-sort	3660
id	ELV059153288
language_de	englisch
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author	Zhou, Man
spellingShingle	Zhou, Man ddc 660 bkl 48.00 bkl 58.34 misc Peanut protein misc Hyperspectral imaging technology misc Spectrophotometry misc MF—XGBoost—Ridge misc Optimal model Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry
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topic_title	660 VZ 48.00 bkl 58.34 bkl Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry Peanut protein Hyperspectral imaging technology Spectrophotometry MF—XGBoost—Ridge Optimal model
topic	ddc 660 bkl 48.00 bkl 58.34 misc Peanut protein misc Hyperspectral imaging technology misc Spectrophotometry misc MF—XGBoost—Ridge misc Optimal model
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title	Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry
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title_full	Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry
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title_sort	machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry
title_auth	Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry
abstract	For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short.
abstractGer	For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short.
abstract_unstemmed	For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short.
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title_short	Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry
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author2	Wang, Li Wu, Hejun Li, Qingye Li, Meiliang Zhang, Zhiqing Zhao, Yongpeng Lu, Zhiwei Zou, Zhiyong
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doi_str	10.1016/j.lwt.2022.114015
up_date	2024-07-06T21:08:11.574Z
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For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. 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Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry

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