Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network

The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are o...
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Gespeichert in:

Autor*in:	Wang, Guantian [verfasserIn] Jiang, Xiaogang [verfasserIn] Li, Xiong [verfasserIn] Liu, Yande [verfasserIn] Rao, Yu [verfasserIn] Zhang, Yu [verfasserIn] Xin, Manyu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Near-infrared spectroscopy Watermelon SSC Prediction 1D-CNN Feature visualization

Übergeordnetes Werk:	Enthalten in: Infrared physics & technology - Amsterdam [u.a.] : Elsevier Science, 1994, 133
Übergeordnetes Werk:	volume:133

DOI / URN:	10.1016/j.infrared.2023.104825

Katalog-ID:	ELV062487264

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245	1	0	\|a Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network
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520			\|a The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction ( R p 2 ) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the R p 2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious.
650		4	\|a Near-infrared spectroscopy
650		4	\|a Watermelon SSC
650		4	\|a Prediction
650		4	\|a 1D-CNN
650		4	\|a Feature visualization
700	1		\|a Jiang, Xiaogang \|e verfasserin \|4 aut
700	1		\|a Li, Xiong \|e verfasserin \|4 aut
700	1		\|a Liu, Yande \|e verfasserin \|0 (orcid)0000-0003-2391-8765 \|4 aut
700	1		\|a Rao, Yu \|e verfasserin \|4 aut
700	1		\|a Zhang, Yu \|e verfasserin \|4 aut
700	1		\|a Xin, Manyu \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Infrared physics & technology \|d Amsterdam [u.a.] : Elsevier Science, 1994 \|g 133 \|h Online-Ressource \|w (DE-627)320592146 \|w (DE-600)2019084-0 \|w (DE-576)259271705 \|7 nnns
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allfields	10.1016/j.infrared.2023.104825 doi (DE-627)ELV062487264 (ELSEVIER)S1350-4495(23)00283-9 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Guantian verfasserin (orcid)0000-0001-6833-5278 aut Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction ( R p 2 ) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the R p 2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious. Near-infrared spectroscopy Watermelon SSC Prediction 1D-CNN Feature visualization Jiang, Xiaogang verfasserin aut Li, Xiong verfasserin aut Liu, Yande verfasserin (orcid)0000-0003-2391-8765 aut Rao, Yu verfasserin aut Zhang, Yu verfasserin aut Xin, Manyu verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 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 133
spelling	10.1016/j.infrared.2023.104825 doi (DE-627)ELV062487264 (ELSEVIER)S1350-4495(23)00283-9 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Guantian verfasserin (orcid)0000-0001-6833-5278 aut Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction ( R p 2 ) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the R p 2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious. Near-infrared spectroscopy Watermelon SSC Prediction 1D-CNN Feature visualization Jiang, Xiaogang verfasserin aut Li, Xiong verfasserin aut Liu, Yande verfasserin (orcid)0000-0003-2391-8765 aut Rao, Yu verfasserin aut Zhang, Yu verfasserin aut Xin, Manyu verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 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 133
allfields_unstemmed	10.1016/j.infrared.2023.104825 doi (DE-627)ELV062487264 (ELSEVIER)S1350-4495(23)00283-9 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Guantian verfasserin (orcid)0000-0001-6833-5278 aut Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction ( R p 2 ) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the R p 2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious. Near-infrared spectroscopy Watermelon SSC Prediction 1D-CNN Feature visualization Jiang, Xiaogang verfasserin aut Li, Xiong verfasserin aut Liu, Yande verfasserin (orcid)0000-0003-2391-8765 aut Rao, Yu verfasserin aut Zhang, Yu verfasserin aut Xin, Manyu verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 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 133
allfieldsGer	10.1016/j.infrared.2023.104825 doi (DE-627)ELV062487264 (ELSEVIER)S1350-4495(23)00283-9 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Guantian verfasserin (orcid)0000-0001-6833-5278 aut Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction ( R p 2 ) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the R p 2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious. Near-infrared spectroscopy Watermelon SSC Prediction 1D-CNN Feature visualization Jiang, Xiaogang verfasserin aut Li, Xiong verfasserin aut Liu, Yande verfasserin (orcid)0000-0003-2391-8765 aut Rao, Yu verfasserin aut Zhang, Yu verfasserin aut Xin, Manyu verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 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 133
allfieldsSound	10.1016/j.infrared.2023.104825 doi (DE-627)ELV062487264 (ELSEVIER)S1350-4495(23)00283-9 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Wang, Guantian verfasserin (orcid)0000-0001-6833-5278 aut Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction ( R p 2 ) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the R p 2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious. Near-infrared spectroscopy Watermelon SSC Prediction 1D-CNN Feature visualization Jiang, Xiaogang verfasserin aut Li, Xiong verfasserin aut Liu, Yande verfasserin (orcid)0000-0003-2391-8765 aut Rao, Yu verfasserin aut Zhang, Yu verfasserin aut Xin, Manyu verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 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 133
language	English
source	Enthalten in Infrared physics & technology 133 volume:133
sourceStr	Enthalten in Infrared physics & technology 133 volume:133
format_phy_str_mv	Article
bklname	Technische Optik Quantenoptik nichtlineare Optik Spektroskopie
institution	findex.gbv.de
topic_facet	Near-infrared spectroscopy Watermelon SSC Prediction 1D-CNN Feature visualization
dewey-raw	530
isfreeaccess_bool	false
container_title	Infrared physics & technology
authorswithroles_txt_mv	Wang, Guantian @@aut@@ Jiang, Xiaogang @@aut@@ Li, Xiong @@aut@@ Liu, Yande @@aut@@ Rao, Yu @@aut@@ Zhang, Yu @@aut@@ Xin, Manyu @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320592146
dewey-sort	3530
id	ELV062487264
language_de	englisch
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author	Wang, Guantian
spellingShingle	Wang, Guantian ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Near-infrared spectroscopy misc Watermelon SSC misc Prediction misc 1D-CNN misc Feature visualization Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network
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topic_title	530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network Near-infrared spectroscopy Watermelon SSC Prediction 1D-CNN Feature visualization
topic	ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Near-infrared spectroscopy misc Watermelon SSC misc Prediction misc 1D-CNN misc Feature visualization
topic_unstemmed	ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Near-infrared spectroscopy misc Watermelon SSC misc Prediction misc 1D-CNN misc Feature visualization
topic_browse	ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Near-infrared spectroscopy misc Watermelon SSC misc Prediction misc 1D-CNN misc Feature visualization
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title	Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network
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title_full	Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network
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title_sort	determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network
title_auth	Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network
abstract	The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction ( R p 2 ) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the R p 2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious.
abstractGer	The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction ( R p 2 ) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the R p 2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious.
abstract_unstemmed	The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction ( R p 2 ) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the R p 2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious.
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title_short	Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network
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author2	Jiang, Xiaogang Li, Xiong Liu, Yande Rao, Yu Zhang, Yu Xin, Manyu
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up_date	2024-07-06T18:51:26.239Z
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Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network

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