Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera.
PeerJ. 2019; 7:e7997.P

Abstract

The anthocyanin content in leaves can reveal valuable information about a plant's physiological status and its responses to stress. Therefore, it is of great value to accurately and efficiently determine anthocyanin content in leaves. The selection of calibration method is a major factor which can influence the accuracy of measurement with visible and near infrared (NIR) spectroscopy. Three multivariate calibrations including principal component regression (PCR), partial least squares regression (PLSR), and back-propagation neural network (BPNN) were adopted for the development of determination models of leaf anthocyanin content using reflectance spectra data (450-600 nm) in Prunus cerasifera and then the performance of these models was compared for three multivariate calibrations. Certain principal components (PCs) and latent variables (LVs) were used as input for the back-propagation neural network (BPNN) model. The results showed that the best PCR and PLSR models were obtained by standard normal variate (SNV), and BPNN models outperformed both the PCR and PLSR models. The coefficient of determination (R2), the root mean square error of prediction (RMSEp), and the residual prediction deviation (RPD) values for the validation set were 0.920, 0.274, and 3.439, respectively, for the BPNN-PCs model, and 0.922, 0.270, and 3.489, respectively, for the BPNN-LVs model. Visible spectroscopy combined with BPNN was successfully applied to determine leaf anthocyanin content in P. cerasifera and the performance of the BPNN-LVs model was the best. The use of the BPNN-LVs model and visible spectroscopy showed significant potential for the nondestructive determination of leaf anthocyanin content in plants.

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Authors+Show Affiliations

Liu X

College of Agriculture, Henan University of Science and Technology, Luoyang, Henan, China. Luoyang Key Laboratory of Symbiotic Microorganism and Green Development/Luoyang Key Laboratory of Plant Nutrition and Environmental Ecology, Luoyang, Henan Province, China. Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan Province, China.

Liu C

Shi Z

Chang Q

College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31687285

Citation

Liu, Xiuying, et al. "Comparison of Prediction Power of Three Multivariate Calibrations for Estimation of Leaf Anthocyanin Content With Visible Spectroscopy in Prunus Cerasifera." PeerJ, vol. 7, 2019, pp. e7997.

Liu X, Liu C, Shi Z, et al. Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera. PeerJ. 2019;7:e7997.

Liu, X., Liu, C., Shi, Z., & Chang, Q. (2019). Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera. PeerJ, 7, e7997. https://doi.org/10.7717/peerj.7997

Liu X, et al. Comparison of Prediction Power of Three Multivariate Calibrations for Estimation of Leaf Anthocyanin Content With Visible Spectroscopy in Prunus Cerasifera. PeerJ. 2019;7:e7997. PubMed PMID: 31687285.

* Article titles in AMA citation format should be in sentence-case

TY - JOUR T1 - Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera. AU - Liu,Xiuying, AU - Liu,Chenzhou, AU - Shi,Zhaoyong, AU - Chang,Qingrui, Y1 - 2019/10/31/ PY - 2019/07/08/received PY - 2019/10/07/accepted PY - 2019/11/6/entrez PY - 2019/11/7/pubmed PY - 2019/11/7/medline KW - Anthocyanin content KW - Back-propagation neural network KW - Partial least squares analysis KW - Principal component analysis KW - Reflectance spectra SP - e7997 EP - e7997 JF - PeerJ JO - PeerJ VL - 7 N2 - The anthocyanin content in leaves can reveal valuable information about a plant's physiological status and its responses to stress. Therefore, it is of great value to accurately and efficiently determine anthocyanin content in leaves. The selection of calibration method is a major factor which can influence the accuracy of measurement with visible and near infrared (NIR) spectroscopy. Three multivariate calibrations including principal component regression (PCR), partial least squares regression (PLSR), and back-propagation neural network (BPNN) were adopted for the development of determination models of leaf anthocyanin content using reflectance spectra data (450-600 nm) in Prunus cerasifera and then the performance of these models was compared for three multivariate calibrations. Certain principal components (PCs) and latent variables (LVs) were used as input for the back-propagation neural network (BPNN) model. The results showed that the best PCR and PLSR models were obtained by standard normal variate (SNV), and BPNN models outperformed both the PCR and PLSR models. The coefficient of determination (R2), the root mean square error of prediction (RMSEp), and the residual prediction deviation (RPD) values for the validation set were 0.920, 0.274, and 3.439, respectively, for the BPNN-PCs model, and 0.922, 0.270, and 3.489, respectively, for the BPNN-LVs model. Visible spectroscopy combined with BPNN was successfully applied to determine leaf anthocyanin content in P. cerasifera and the performance of the BPNN-LVs model was the best. The use of the BPNN-LVs model and visible spectroscopy showed significant potential for the nondestructive determination of leaf anthocyanin content in plants. SN - 2167-8359 UR - https://www.unboundmedicine.com/medline/citation/31687285/Comparison_of_prediction_power_of_three_multivariate_calibrations_for_estimation_of_leaf_anthocyanin_content_with_visible_spectroscopy_in_Prunus_cerasifera_ DB - PRIME DP - Unbound Medicine ER -

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Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera.PeerJ. 2019; 7:e7997.P