Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy
In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gao, Wenli [verfasserIn] Zhou, Liang [verfasserIn] Liu, Shengquan [verfasserIn] Guan, Ying [verfasserIn] Gao, Hui [verfasserIn] Hu, Jianjun [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Carbohydrate polymers - Amsterdam [u.a.] : Elsevier Science, 1981, 292 |
|---|---|
| Übergeordnetes Werk: |
volume:292 |
| DOI / URN: |
10.1016/j.carbpol.2022.119635 |
|---|
| Katalog-ID: |
ELV05818676X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV05818676X | ||
| 003 | DE-627 | ||
| 005 | 20240104093914.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220808s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.carbpol.2022.119635 |2 doi | |
| 035 | |a (DE-627)ELV05818676X | ||
| 035 | |a (ELSEVIER)S0144-8617(22)00540-9 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 540 |a 660 |q VZ |
| 084 | |a 58.34 |2 bkl | ||
| 084 | |a 49.25 |2 bkl | ||
| 100 | 1 | |a Gao, Wenli |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy |
| 264 | 1 | |c 2022 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs. | ||
| 650 | 4 | |a Holocellulose content | |
| 650 | 4 | |a Machine learning algorithms | |
| 650 | 4 | |a Raman spectroscopy | |
| 650 | 4 | |a CatBoost | |
| 650 | 4 | |a XGBoost | |
| 700 | 1 | |a Zhou, Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Shengquan |e verfasserin |4 aut | |
| 700 | 1 | |a Guan, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Jianjun |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Carbohydrate polymers |d Amsterdam [u.a.] : Elsevier Science, 1981 |g 292 |h Online-Ressource |w (DE-627)306717611 |w (DE-600)1501516-6 |w (DE-576)109967178 |x 1879-1344 |7 nnns |
| 773 | 1 | 8 | |g volume:292 |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_101 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_150 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_702 | ||
| 912 | |a GBV_ILN_2003 | ||
| 912 | |a GBV_ILN_2004 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_2021 | ||
| 912 | |a GBV_ILN_2025 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2034 | ||
| 912 | |a GBV_ILN_2038 | ||
| 912 | |a GBV_ILN_2044 | ||
| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2049 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2056 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2064 | ||
| 912 | |a GBV_ILN_2065 | ||
| 912 | |a GBV_ILN_2068 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2112 | ||
| 912 | |a GBV_ILN_2113 | ||
| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2122 | ||
| 912 | |a GBV_ILN_2129 | ||
| 912 | |a GBV_ILN_2143 | ||
| 912 | |a GBV_ILN_2147 | ||
| 912 | |a GBV_ILN_2148 | ||
| 912 | |a GBV_ILN_2152 | ||
| 912 | |a GBV_ILN_2153 | ||
| 912 | |a GBV_ILN_2190 | ||
| 912 | |a GBV_ILN_2336 | ||
| 912 | |a GBV_ILN_2507 | ||
| 912 | |a GBV_ILN_2522 | ||
| 912 | |a GBV_ILN_4035 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4242 | ||
| 912 | |a GBV_ILN_4251 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4393 | ||
| 936 | b | k | |a 58.34 |j Lebensmitteltechnologie |q VZ |
| 936 | b | k | |a 49.25 |j Lebensmittelkunde |j Ernährungslehre |q VZ |
| 951 | |a AR | ||
| 952 | |d 292 | ||
| author_variant |
w g wg l z lz s l sl y g yg h g hg j h jh |
|---|---|
| matchkey_str |
article:18791344:2022----::ahnlannagrtmfraietmtoohlcluoeotnoppac |
| hierarchy_sort_str |
2022 |
| bklnumber |
58.34 49.25 |
| publishDate |
2022 |
| allfields |
10.1016/j.carbpol.2022.119635 doi (DE-627)ELV05818676X (ELSEVIER)S0144-8617(22)00540-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl 49.25 bkl Gao, Wenli verfasserin aut Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs. Holocellulose content Machine learning algorithms Raman spectroscopy CatBoost XGBoost Zhou, Liang verfasserin aut Liu, Shengquan verfasserin aut Guan, Ying verfasserin aut Gao, Hui verfasserin aut Hu, Jianjun verfasserin aut Enthalten in Carbohydrate polymers Amsterdam [u.a.] : Elsevier Science, 1981 292 Online-Ressource (DE-627)306717611 (DE-600)1501516-6 (DE-576)109967178 1879-1344 nnns volume:292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ 49.25 Lebensmittelkunde Ernährungslehre VZ AR 292 |
| spelling |
10.1016/j.carbpol.2022.119635 doi (DE-627)ELV05818676X (ELSEVIER)S0144-8617(22)00540-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl 49.25 bkl Gao, Wenli verfasserin aut Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs. Holocellulose content Machine learning algorithms Raman spectroscopy CatBoost XGBoost Zhou, Liang verfasserin aut Liu, Shengquan verfasserin aut Guan, Ying verfasserin aut Gao, Hui verfasserin aut Hu, Jianjun verfasserin aut Enthalten in Carbohydrate polymers Amsterdam [u.a.] : Elsevier Science, 1981 292 Online-Ressource (DE-627)306717611 (DE-600)1501516-6 (DE-576)109967178 1879-1344 nnns volume:292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ 49.25 Lebensmittelkunde Ernährungslehre VZ AR 292 |
| allfields_unstemmed |
10.1016/j.carbpol.2022.119635 doi (DE-627)ELV05818676X (ELSEVIER)S0144-8617(22)00540-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl 49.25 bkl Gao, Wenli verfasserin aut Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs. Holocellulose content Machine learning algorithms Raman spectroscopy CatBoost XGBoost Zhou, Liang verfasserin aut Liu, Shengquan verfasserin aut Guan, Ying verfasserin aut Gao, Hui verfasserin aut Hu, Jianjun verfasserin aut Enthalten in Carbohydrate polymers Amsterdam [u.a.] : Elsevier Science, 1981 292 Online-Ressource (DE-627)306717611 (DE-600)1501516-6 (DE-576)109967178 1879-1344 nnns volume:292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ 49.25 Lebensmittelkunde Ernährungslehre VZ AR 292 |
| allfieldsGer |
10.1016/j.carbpol.2022.119635 doi (DE-627)ELV05818676X (ELSEVIER)S0144-8617(22)00540-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl 49.25 bkl Gao, Wenli verfasserin aut Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs. Holocellulose content Machine learning algorithms Raman spectroscopy CatBoost XGBoost Zhou, Liang verfasserin aut Liu, Shengquan verfasserin aut Guan, Ying verfasserin aut Gao, Hui verfasserin aut Hu, Jianjun verfasserin aut Enthalten in Carbohydrate polymers Amsterdam [u.a.] : Elsevier Science, 1981 292 Online-Ressource (DE-627)306717611 (DE-600)1501516-6 (DE-576)109967178 1879-1344 nnns volume:292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ 49.25 Lebensmittelkunde Ernährungslehre VZ AR 292 |
| allfieldsSound |
10.1016/j.carbpol.2022.119635 doi (DE-627)ELV05818676X (ELSEVIER)S0144-8617(22)00540-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl 49.25 bkl Gao, Wenli verfasserin aut Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs. Holocellulose content Machine learning algorithms Raman spectroscopy CatBoost XGBoost Zhou, Liang verfasserin aut Liu, Shengquan verfasserin aut Guan, Ying verfasserin aut Gao, Hui verfasserin aut Hu, Jianjun verfasserin aut Enthalten in Carbohydrate polymers Amsterdam [u.a.] : Elsevier Science, 1981 292 Online-Ressource (DE-627)306717611 (DE-600)1501516-6 (DE-576)109967178 1879-1344 nnns volume:292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ 49.25 Lebensmittelkunde Ernährungslehre VZ AR 292 |
| language |
English |
| source |
Enthalten in Carbohydrate polymers 292 volume:292 |
| sourceStr |
Enthalten in Carbohydrate polymers 292 volume:292 |
| format_phy_str_mv |
Article |
| bklname |
Lebensmitteltechnologie Lebensmittelkunde Ernährungslehre |
| institution |
findex.gbv.de |
| topic_facet |
Holocellulose content Machine learning algorithms Raman spectroscopy CatBoost XGBoost |
| dewey-raw |
540 |
| isfreeaccess_bool |
false |
| container_title |
Carbohydrate polymers |
| authorswithroles_txt_mv |
Gao, Wenli @@aut@@ Zhou, Liang @@aut@@ Liu, Shengquan @@aut@@ Guan, Ying @@aut@@ Gao, Hui @@aut@@ Hu, Jianjun @@aut@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
306717611 |
| dewey-sort |
3540 |
| id |
ELV05818676X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV05818676X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240104093914.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.carbpol.2022.119635</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV05818676X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0144-8617(22)00540-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="a">660</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.34</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">49.25</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gao, Wenli</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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">In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Holocellulose content</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Raman spectroscopy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CatBoost</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">XGBoost</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Liang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Shengquan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guan, Ying</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Hui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Jianjun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Carbohydrate polymers</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1981</subfield><subfield code="g">292</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306717611</subfield><subfield code="w">(DE-600)1501516-6</subfield><subfield code="w">(DE-576)109967178</subfield><subfield code="x">1879-1344</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:292</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">58.34</subfield><subfield code="j">Lebensmitteltechnologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">49.25</subfield><subfield code="j">Lebensmittelkunde</subfield><subfield code="j">Ernährungslehre</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">292</subfield></datafield></record></collection>
|
| author |
Gao, Wenli |
| spellingShingle |
Gao, Wenli ddc 540 bkl 58.34 bkl 49.25 misc Holocellulose content misc Machine learning algorithms misc Raman spectroscopy misc CatBoost misc XGBoost Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy |
| authorStr |
Gao, Wenli |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)306717611 |
| format |
electronic Article |
| dewey-ones |
540 - Chemistry & allied sciences 660 - Chemical engineering |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1879-1344 |
| topic_title |
540 660 VZ 58.34 bkl 49.25 bkl Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy Holocellulose content Machine learning algorithms Raman spectroscopy CatBoost XGBoost |
| topic |
ddc 540 bkl 58.34 bkl 49.25 misc Holocellulose content misc Machine learning algorithms misc Raman spectroscopy misc CatBoost misc XGBoost |
| topic_unstemmed |
ddc 540 bkl 58.34 bkl 49.25 misc Holocellulose content misc Machine learning algorithms misc Raman spectroscopy misc CatBoost misc XGBoost |
| topic_browse |
ddc 540 bkl 58.34 bkl 49.25 misc Holocellulose content misc Machine learning algorithms misc Raman spectroscopy misc CatBoost misc XGBoost |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Carbohydrate polymers |
| hierarchy_parent_id |
306717611 |
| dewey-tens |
540 - Chemistry 660 - Chemical engineering |
| hierarchy_top_title |
Carbohydrate polymers |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)306717611 (DE-600)1501516-6 (DE-576)109967178 |
| title |
Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy |
| ctrlnum |
(DE-627)ELV05818676X (ELSEVIER)S0144-8617(22)00540-9 |
| title_full |
Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy |
| author_sort |
Gao, Wenli |
| journal |
Carbohydrate polymers |
| journalStr |
Carbohydrate polymers |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| author_browse |
Gao, Wenli Zhou, Liang Liu, Shengquan Guan, Ying Gao, Hui Hu, Jianjun |
| container_volume |
292 |
| class |
540 660 VZ 58.34 bkl 49.25 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Gao, Wenli |
| doi_str_mv |
10.1016/j.carbpol.2022.119635 |
| dewey-full |
540 660 |
| author2-role |
verfasserin |
| title_sort |
machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on raman spectroscopy |
| title_auth |
Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy |
| abstract |
In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs. |
| abstractGer |
In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs. |
| abstract_unstemmed |
In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 |
| title_short |
Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy |
| remote_bool |
true |
| author2 |
Zhou, Liang Liu, Shengquan Guan, Ying Gao, Hui Hu, Jianjun |
| author2Str |
Zhou, Liang Liu, Shengquan Guan, Ying Gao, Hui Hu, Jianjun |
| ppnlink |
306717611 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1016/j.carbpol.2022.119635 |
| up_date |
2024-07-06T18:17:34.742Z |
| _version_ |
1803854654726995968 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV05818676X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240104093914.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.carbpol.2022.119635</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV05818676X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0144-8617(22)00540-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="a">660</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.34</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">49.25</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gao, Wenli</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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">In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Holocellulose content</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Raman spectroscopy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CatBoost</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">XGBoost</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Liang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Shengquan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guan, Ying</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Hui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Jianjun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Carbohydrate polymers</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1981</subfield><subfield code="g">292</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306717611</subfield><subfield code="w">(DE-600)1501516-6</subfield><subfield code="w">(DE-576)109967178</subfield><subfield code="x">1879-1344</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:292</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">58.34</subfield><subfield code="j">Lebensmitteltechnologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">49.25</subfield><subfield code="j">Lebensmittelkunde</subfield><subfield code="j">Ernährungslehre</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">292</subfield></datafield></record></collection>
|
| score |
7.3994617 |