Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm
Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Longguo [verfasserIn] Zhang, Yao [verfasserIn] Jiang, Qiufei [verfasserIn] Zhang, Yiyang [verfasserIn] Ma, Ling [verfasserIn] Ma, Siyan [verfasserIn] Wang, Jing [verfasserIn] Ma, Yan [verfasserIn] Du, Minghua [verfasserIn] Li, Jianshe [verfasserIn] Gao, Yanming [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Spectrochimica acta / A - Amsterdam [u.a.] : Elsevier Science, 1967, 302 |
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| Übergeordnetes Werk: |
volume:302 |
| DOI / URN: |
10.1016/j.saa.2023.123047 |
|---|
| Katalog-ID: |
ELV062533274 |
|---|
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| 245 | 1 | 0 | |a Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm |
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| 520 | |a Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry. | ||
| 650 | 4 | |a Microscopic hyperspectral imaging technique | |
| 650 | 4 | |a Tomato leaf cells | |
| 650 | 4 | |a Salt stress | |
| 650 | 4 | |a Catalase activity | |
| 650 | 4 | |a Transfer learning | |
| 700 | 1 | |a Zhang, Yao |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Qiufei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yiyang |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Ling |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Siyan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Minghua |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jianshe |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Yanming |e verfasserin |4 aut | |
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10.1016/j.saa.2023.123047 doi (DE-627)ELV062533274 (ELSEVIER)S1386-1425(23)00732-1 DE-627 ger DE-627 rda eng 540 530 VZ 35.00 bkl Wu, Longguo verfasserin aut Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry. Microscopic hyperspectral imaging technique Tomato leaf cells Salt stress Catalase activity Transfer learning Zhang, Yao verfasserin aut Jiang, Qiufei verfasserin aut Zhang, Yiyang verfasserin aut Ma, Ling verfasserin aut Ma, Siyan verfasserin aut Wang, Jing verfasserin aut Ma, Yan verfasserin aut Du, Minghua verfasserin aut Li, Jianshe verfasserin aut Gao, Yanming verfasserin aut Enthalten in Spectrochimica acta / A Amsterdam [u.a.] : Elsevier Science, 1967 302 Online-Ressource (DE-627)320570983 (DE-600)2016492-0 (DE-576)090956206 1873-3557 nnns volume:302 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_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_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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 Chemie: Allgemeines VZ AR 302 |
| spelling |
10.1016/j.saa.2023.123047 doi (DE-627)ELV062533274 (ELSEVIER)S1386-1425(23)00732-1 DE-627 ger DE-627 rda eng 540 530 VZ 35.00 bkl Wu, Longguo verfasserin aut Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry. Microscopic hyperspectral imaging technique Tomato leaf cells Salt stress Catalase activity Transfer learning Zhang, Yao verfasserin aut Jiang, Qiufei verfasserin aut Zhang, Yiyang verfasserin aut Ma, Ling verfasserin aut Ma, Siyan verfasserin aut Wang, Jing verfasserin aut Ma, Yan verfasserin aut Du, Minghua verfasserin aut Li, Jianshe verfasserin aut Gao, Yanming verfasserin aut Enthalten in Spectrochimica acta / A Amsterdam [u.a.] : Elsevier Science, 1967 302 Online-Ressource (DE-627)320570983 (DE-600)2016492-0 (DE-576)090956206 1873-3557 nnns volume:302 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_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_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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 Chemie: Allgemeines VZ AR 302 |
| allfields_unstemmed |
10.1016/j.saa.2023.123047 doi (DE-627)ELV062533274 (ELSEVIER)S1386-1425(23)00732-1 DE-627 ger DE-627 rda eng 540 530 VZ 35.00 bkl Wu, Longguo verfasserin aut Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry. Microscopic hyperspectral imaging technique Tomato leaf cells Salt stress Catalase activity Transfer learning Zhang, Yao verfasserin aut Jiang, Qiufei verfasserin aut Zhang, Yiyang verfasserin aut Ma, Ling verfasserin aut Ma, Siyan verfasserin aut Wang, Jing verfasserin aut Ma, Yan verfasserin aut Du, Minghua verfasserin aut Li, Jianshe verfasserin aut Gao, Yanming verfasserin aut Enthalten in Spectrochimica acta / A Amsterdam [u.a.] : Elsevier Science, 1967 302 Online-Ressource (DE-627)320570983 (DE-600)2016492-0 (DE-576)090956206 1873-3557 nnns volume:302 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_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_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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 Chemie: Allgemeines VZ AR 302 |
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10.1016/j.saa.2023.123047 doi (DE-627)ELV062533274 (ELSEVIER)S1386-1425(23)00732-1 DE-627 ger DE-627 rda eng 540 530 VZ 35.00 bkl Wu, Longguo verfasserin aut Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry. Microscopic hyperspectral imaging technique Tomato leaf cells Salt stress Catalase activity Transfer learning Zhang, Yao verfasserin aut Jiang, Qiufei verfasserin aut Zhang, Yiyang verfasserin aut Ma, Ling verfasserin aut Ma, Siyan verfasserin aut Wang, Jing verfasserin aut Ma, Yan verfasserin aut Du, Minghua verfasserin aut Li, Jianshe verfasserin aut Gao, Yanming verfasserin aut Enthalten in Spectrochimica acta / A Amsterdam [u.a.] : Elsevier Science, 1967 302 Online-Ressource (DE-627)320570983 (DE-600)2016492-0 (DE-576)090956206 1873-3557 nnns volume:302 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_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_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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 Chemie: Allgemeines VZ AR 302 |
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10.1016/j.saa.2023.123047 doi (DE-627)ELV062533274 (ELSEVIER)S1386-1425(23)00732-1 DE-627 ger DE-627 rda eng 540 530 VZ 35.00 bkl Wu, Longguo verfasserin aut Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry. Microscopic hyperspectral imaging technique Tomato leaf cells Salt stress Catalase activity Transfer learning Zhang, Yao verfasserin aut Jiang, Qiufei verfasserin aut Zhang, Yiyang verfasserin aut Ma, Ling verfasserin aut Ma, Siyan verfasserin aut Wang, Jing verfasserin aut Ma, Yan verfasserin aut Du, Minghua verfasserin aut Li, Jianshe verfasserin aut Gao, Yanming verfasserin aut Enthalten in Spectrochimica acta / A Amsterdam [u.a.] : Elsevier Science, 1967 302 Online-Ressource (DE-627)320570983 (DE-600)2016492-0 (DE-576)090956206 1873-3557 nnns volume:302 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_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_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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.00 Chemie: Allgemeines VZ AR 302 |
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Microscopic hyperspectral imaging technique Tomato leaf cells Salt stress Catalase activity Transfer learning |
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Wu, Longguo @@aut@@ Zhang, Yao @@aut@@ Jiang, Qiufei @@aut@@ Zhang, Yiyang @@aut@@ Ma, Ling @@aut@@ Ma, Siyan @@aut@@ Wang, Jing @@aut@@ Ma, Yan @@aut@@ Du, Minghua @@aut@@ Li, Jianshe @@aut@@ Gao, Yanming @@aut@@ |
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2023-01-01T00:00:00Z |
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Wu, Longguo |
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Wu, Longguo ddc 540 bkl 35.00 misc Microscopic hyperspectral imaging technique misc Tomato leaf cells misc Salt stress misc Catalase activity misc Transfer learning Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm |
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540 530 VZ 35.00 bkl Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm Microscopic hyperspectral imaging technique Tomato leaf cells Salt stress Catalase activity Transfer learning |
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ddc 540 bkl 35.00 misc Microscopic hyperspectral imaging technique misc Tomato leaf cells misc Salt stress misc Catalase activity misc Transfer learning |
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Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm |
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Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm |
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Wu, Longguo Zhang, Yao Jiang, Qiufei Zhang, Yiyang Ma, Ling Ma, Siyan Wang, Jing Ma, Yan Du, Minghua Li, Jianshe Gao, Yanming |
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study on cat activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm |
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Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm |
| abstract |
Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry. |
| abstractGer |
Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry. |
| abstract_unstemmed |
Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry. |
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