Kinetic models of peroxidase activity in potato leaves infected with late blight based on hyperspectral data
Keywords:
POD (peroxidase) activity, kinetic model, potato leaves, late blight, hyperspectral data, latency predictionAbstract
Potato late blight, which is caused by Phytophthorainfestans (Mont.) de Bary, is a worldwide devastating disease for potato. It decreased yields of potato and caused unpredictable losses all over the world. Various simple statistical methods and forecasting models have been developed to predict and manage potato late blight. Meanwhile, there is a rising need to develop prediction models reflecting peroxidase (POD) activity, which is an important health index that varies with infection and correlated with stress resistance in plants. Thus, the aim of this research was to develop kinetic models to predict POD activity. Infection-induced changes in potato leaves stored in an artificial climate chest at 25°C were analyzed using hyperspectroscopy. Four prediction models were developed by using linear partial least squares (PLS) and nonlinear support vector machine (SVM) methods based on the full spectrum and effective wavelengths. The effective wavelengths were selected by the successive projection algorithm (SPA). In this study, the prediction model developed by means of SPA-SVM method obtained the best performance, with a Rp (correlation coefficient of prediction) value of 0.923 and a RMSEp (root mean square error of prediction) value of 24.326. Five-order kinetics models according to the prediction model were developed, and late blight disease can be predicted using this model. This study provided a theoretical basis for the prediction of latencies of late blight. Keywords: POD (peroxidase) activity, kinetic model, potato leaves, late blight, hyperspectral data, latency prediction DOI: 10.25165/j.ijabe.20191202.4574 Citation: Li Q Y, Hu Y H. Kinetic models of peroxidase activity in potato leaves infected with late blight based on hyperspectral data. Int J Agric & Biol Eng, 2019; 12(2): 160–165.References
[1] Gu Y H, Yoo S J, Park C J, Kim Y H, Park S K, Kim J S, et al. BLITE-SVR: New forecasting model for late blight on potato using support-vector regression. Computers & Electronics in Agriculture, 2016; 130: 169–176.
[2] Information Office of the Ministry of Agriculture, Leading the development of potato staple food with scientific and technological innovation. Available at: http://jiuban.moa.gov.cn/zwllm/zwdt/201501/ t20150106_4323476.htm. (in Chinese)
[3] Journal of the Chinese People's Political Consultative Conference, Strategic start of staple food in China. Available at: http://cppcc.people. com.cn/n/2015/0108/c34948-26348803.html. (in Chinese)
[4] Pallavi S, Bhushan J A, Shanker D R, Mohammad P. Reactive oxygen species, oxidative damage, and antioxidative defense mechanism in plants under stressful conditions. Journal of Botany, 2012; Article ID 217037, 26p. http://dx.doi.org/10.1155/2012/217037
[5] Pal R S, Agrawal P K, Bhatt J C. Molecular approach towards the understanding of defensive systems against oxidative stress in plant: A critical review. International Journal of Pharmaceutical Sciences Review & Research, 2013; 22(2): 131–138.
[6] Künstler A, Bacsó R, Hafez Y M, Király L. Reactive oxygen species and plant disease resistance. Springer International Publishing, 2015; pp.31–50.
[7] Ik-Hwa H, Woobong C. Phytophthora species, new threats to the plant health in Korea. Plant Pathology Journal, 2014; 30(4): 331–342.
[8] Kamoun S, Furzer O, Jones J D, Judelson H S, Ali G S, Dalio R J, et al. The top 10 oomycete pathogens in molecular plant pathology. Molecular Plant Pathology, 2015; 16(4): 413–434.
[9] Dehury B, Sarma K, Sarmah R, Sahu J, Sahoo S, Sahu M, et al. In silico analyses of superoxide dismutases (SODs) of rice (Oryza sativa L). Journal of Plant Biochemistry & Biotechnology, 2013; 22(1): 150–156.
[10] Leonowicz G, Trzebuniak K F, Zimakpiekarczyk P, Ślesak I, Mysliwakurdziel B. The activity of superoxide dismutases (SODs) at the early stages of wheat deetiolation. Plos One, 2018; 13(3): e0194678.
[11] Champagne C M, Staenz K, Bannari A, Mcnairn H, Deguise J C. Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies. Remote Sensing of Environment, 2003; 87(2–3): 148–160.
[12] Sonobe R, Wang Q. Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests. Ecological Informatics, 2016: 37: 1–9.
[13] Stroppiana D, Boschetti M, Brivio P A, Bocchi S. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 2009; 111(1): 119–129.
[14] Likar B, Pernuš F, Katrašnik J, Bürmen M, Špiclin Ž. Geometric calibration of a hyperspectral imaging system. Applied Optics, 2010; 49(15): 2813–2818.
[15] Behmann J, Mahlein A K, Paulus S, Kuhlmann H, Oerke E C, Plümer L. Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping. Isprs Journal of Photogrammetry & Remote Sensing, 2015; 106: 172–182.
[16] Chen Y N, Sun DW, Cheng J H, Gao W H. Recent advances for rapid identification of chemical information of muscle foods by hyperspectral imaging analysis. Food Engineering Reviews, 2016; 8(3): 336–350.
[17] Zhang Z L. The experimental guide for plant physiology. Beijing: High Education Press, 2008. (in Chinese)
[18] Menn M L, Tocnaye J L B, Grosso P, Delauney L, Podeur C, Brault P, et al. Advances in measuring ocean salinity with an optical sensor. Measurement Science & Technology, 2011; 22(11): 115202.
[19] Yu C F, Ding Y L, Hui S W, Yu S B, Wang L Q, Shi L, et al. Analysis of influence on the new type aviation lens shutter to the optical transfer function. Acta Optica Sinica, 2014; 34(11): 1112005
[20] Zhang B, Li J, Fan S, Huang W, Zhao C, Liu C, et al. Hyperspectral imaging combined with multivariate analysis and band math for detection of common defects on peaches (Prunuspersica). Computers & Electronics in Agriculture, 2015; 114(C): 14–24.
[21] Sun Y, Gu X, Sun K, Hu H, Xu M, Wang Z, Pan L. Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches. Food Science and Technology, 2016; 75: 557–564. (in Chinese)
[22] Xie C, Feng L, Feng B, Li X, Liu F, He Y. Relevance of hyperspectral image feature to catalase activity in eggplant leaves with grey mold disease. Transactions of the CSAE, 2012; 28(18): 177–184. (in Chinese)
[23] Ghosh S, Chakraborty R, Chatterjee G, Raychaudhuri U. Study on fermentation conditions of palm juice vinegar by response surface methodology and development of a kinetic model. Brazilian Journal of Chemical Engineering, 2012; 29(3): 461–472.
[24] Mounira K A, Serge H, Nawel O, Radia C, Noreddine. Kinetic models and parameters estimation study of biomass and ethanol production from inulin by Pichiacaribbica (KC977491). African Journal of Biotechnology, 2017; 16(3): 124–131.
[25] Vašát R, Kodešová R, Klement A, Borůvka L. Simple but efficient signal pre-processing in soil organic carbon spectroscopic estimation. Geoderma, 2017; 298: 46–53.
[26] Huang S, Yan W, Liu M, Hu J. Detection of difenoconazole pesticides in pakchoi by surface-enhanced Raman scattering spectroscopy coupled with gold nanoparticles. Analytical Methods, 2016; 8(23): 4755–4761.
[27] Koseki J, Kita Y M. Formation of Schiff-base for photoreaction mechanism of red shift of GFP spectra. Biophysical Chemistry, 2010; 147(3): 140–145.
[2] Information Office of the Ministry of Agriculture, Leading the development of potato staple food with scientific and technological innovation. Available at: http://jiuban.moa.gov.cn/zwllm/zwdt/201501/ t20150106_4323476.htm. (in Chinese)
[3] Journal of the Chinese People's Political Consultative Conference, Strategic start of staple food in China. Available at: http://cppcc.people. com.cn/n/2015/0108/c34948-26348803.html. (in Chinese)
[4] Pallavi S, Bhushan J A, Shanker D R, Mohammad P. Reactive oxygen species, oxidative damage, and antioxidative defense mechanism in plants under stressful conditions. Journal of Botany, 2012; Article ID 217037, 26p. http://dx.doi.org/10.1155/2012/217037
[5] Pal R S, Agrawal P K, Bhatt J C. Molecular approach towards the understanding of defensive systems against oxidative stress in plant: A critical review. International Journal of Pharmaceutical Sciences Review & Research, 2013; 22(2): 131–138.
[6] Künstler A, Bacsó R, Hafez Y M, Király L. Reactive oxygen species and plant disease resistance. Springer International Publishing, 2015; pp.31–50.
[7] Ik-Hwa H, Woobong C. Phytophthora species, new threats to the plant health in Korea. Plant Pathology Journal, 2014; 30(4): 331–342.
[8] Kamoun S, Furzer O, Jones J D, Judelson H S, Ali G S, Dalio R J, et al. The top 10 oomycete pathogens in molecular plant pathology. Molecular Plant Pathology, 2015; 16(4): 413–434.
[9] Dehury B, Sarma K, Sarmah R, Sahu J, Sahoo S, Sahu M, et al. In silico analyses of superoxide dismutases (SODs) of rice (Oryza sativa L). Journal of Plant Biochemistry & Biotechnology, 2013; 22(1): 150–156.
[10] Leonowicz G, Trzebuniak K F, Zimakpiekarczyk P, Ślesak I, Mysliwakurdziel B. The activity of superoxide dismutases (SODs) at the early stages of wheat deetiolation. Plos One, 2018; 13(3): e0194678.
[11] Champagne C M, Staenz K, Bannari A, Mcnairn H, Deguise J C. Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies. Remote Sensing of Environment, 2003; 87(2–3): 148–160.
[12] Sonobe R, Wang Q. Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests. Ecological Informatics, 2016: 37: 1–9.
[13] Stroppiana D, Boschetti M, Brivio P A, Bocchi S. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 2009; 111(1): 119–129.
[14] Likar B, Pernuš F, Katrašnik J, Bürmen M, Špiclin Ž. Geometric calibration of a hyperspectral imaging system. Applied Optics, 2010; 49(15): 2813–2818.
[15] Behmann J, Mahlein A K, Paulus S, Kuhlmann H, Oerke E C, Plümer L. Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping. Isprs Journal of Photogrammetry & Remote Sensing, 2015; 106: 172–182.
[16] Chen Y N, Sun DW, Cheng J H, Gao W H. Recent advances for rapid identification of chemical information of muscle foods by hyperspectral imaging analysis. Food Engineering Reviews, 2016; 8(3): 336–350.
[17] Zhang Z L. The experimental guide for plant physiology. Beijing: High Education Press, 2008. (in Chinese)
[18] Menn M L, Tocnaye J L B, Grosso P, Delauney L, Podeur C, Brault P, et al. Advances in measuring ocean salinity with an optical sensor. Measurement Science & Technology, 2011; 22(11): 115202.
[19] Yu C F, Ding Y L, Hui S W, Yu S B, Wang L Q, Shi L, et al. Analysis of influence on the new type aviation lens shutter to the optical transfer function. Acta Optica Sinica, 2014; 34(11): 1112005
[20] Zhang B, Li J, Fan S, Huang W, Zhao C, Liu C, et al. Hyperspectral imaging combined with multivariate analysis and band math for detection of common defects on peaches (Prunuspersica). Computers & Electronics in Agriculture, 2015; 114(C): 14–24.
[21] Sun Y, Gu X, Sun K, Hu H, Xu M, Wang Z, Pan L. Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches. Food Science and Technology, 2016; 75: 557–564. (in Chinese)
[22] Xie C, Feng L, Feng B, Li X, Liu F, He Y. Relevance of hyperspectral image feature to catalase activity in eggplant leaves with grey mold disease. Transactions of the CSAE, 2012; 28(18): 177–184. (in Chinese)
[23] Ghosh S, Chakraborty R, Chatterjee G, Raychaudhuri U. Study on fermentation conditions of palm juice vinegar by response surface methodology and development of a kinetic model. Brazilian Journal of Chemical Engineering, 2012; 29(3): 461–472.
[24] Mounira K A, Serge H, Nawel O, Radia C, Noreddine. Kinetic models and parameters estimation study of biomass and ethanol production from inulin by Pichiacaribbica (KC977491). African Journal of Biotechnology, 2017; 16(3): 124–131.
[25] Vašát R, Kodešová R, Klement A, Borůvka L. Simple but efficient signal pre-processing in soil organic carbon spectroscopic estimation. Geoderma, 2017; 298: 46–53.
[26] Huang S, Yan W, Liu M, Hu J. Detection of difenoconazole pesticides in pakchoi by surface-enhanced Raman scattering spectroscopy coupled with gold nanoparticles. Analytical Methods, 2016; 8(23): 4755–4761.
[27] Koseki J, Kita Y M. Formation of Schiff-base for photoreaction mechanism of red shift of GFP spectra. Biophysical Chemistry, 2010; 147(3): 140–145.
Downloads
Published
2019-04-06
How to Cite
Li, Q., & Hu, Y. (2019). Kinetic models of peroxidase activity in potato leaves infected with late blight based on hyperspectral data. International Journal of Agricultural and Biological Engineering, 12(2), 160–165. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4574
Issue
Section
Biosystems, Biological and Ecological Engineering
License
IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
