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Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum

Italian ryegrass (Lolium multiflorum) is an important cool-season, annual forage crop for the grassland rotation system in Southern China. The primary aim of breeding programs is always to seek to improve forage quality in the animal productivity system; however, it is time- and labor-consuming when...

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Autores principales: Yang, Zhongfu, Nie, Gang, Pan, Ling, Zhang, Yan, Huang, Linkai, Ma, Xiao, Zhang, Xinquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629960/
https://www.ncbi.nlm.nih.gov/pubmed/29018608
http://dx.doi.org/10.7717/peerj.3867
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author Yang, Zhongfu
Nie, Gang
Pan, Ling
Zhang, Yan
Huang, Linkai
Ma, Xiao
Zhang, Xinquan
author_facet Yang, Zhongfu
Nie, Gang
Pan, Ling
Zhang, Yan
Huang, Linkai
Ma, Xiao
Zhang, Xinquan
author_sort Yang, Zhongfu
collection PubMed
description Italian ryegrass (Lolium multiflorum) is an important cool-season, annual forage crop for the grassland rotation system in Southern China. The primary aim of breeding programs is always to seek to improve forage quality in the animal productivity system; however, it is time- and labor-consuming when analyzed excessive large number of samples. The main objectives of this study were to construct near-infrared reflectance spectroscopy (NIRS) models to predict the forage chemistry quality of Italian ryegrass including the concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and water soluble carbohydrate (WSC). The results showed that a broader range of CP, NDF, ADF and WSC contents (%DM) were obtained (4.45–30.60, 21.29–60.47, 11.66–36.17 and 3.95–51.52, respectively) from the samples selected for developing NIRS models. In addition, the critical wavelengths identified in this study to construct optimal NIRS models were located in 4,247–6,102 and 4,247–5,450 cm(-1) for CP and NDF content, and both wavelengths 5,446–6,102 and 4,247–4,602 cm(-1) could for ADF and WSC. Finally, the optimal models were developed based on the laboratory data and the spectral information by partial least squares (PLS) regression, with relatively high coefficients of determination (R(2)(CV), CP = 0.99, NDF = 0.94, ADF = 0.92, WSC = 0.88), ratio of prediction to devitation (RPD, CP = 8.58, NDF = 4.25, ADF = 3.64, WSC = 3.10). The further statistics of prediction errors relative to laboratory (PRL) and the range error ratio (RER) give excellent assessments of the models with the PRL ratios lower than 2 and the RER values greater than 10. The NIRS models were validated using a completely independent set of samples and have coefficients of determination (R(2)(V), CP = 0.99, NDF = 0.91, ADF = 0.95, WSC = 0.91) and ratio of prediction to deviation (RPD, CP = 9.37, NDF = 3.44, ADF = 4.40, WSC = 3.39). The result suggested that routine screening for forage quality parameters with large numbers of samples is available with the NIRS model in Italian ryegrass breeding programs, as well as facilitating graziers to monitor the forage development stage for improving grazing efficiency.
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spelling pubmed-56299602017-10-10 Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum Yang, Zhongfu Nie, Gang Pan, Ling Zhang, Yan Huang, Linkai Ma, Xiao Zhang, Xinquan PeerJ Agricultural Science Italian ryegrass (Lolium multiflorum) is an important cool-season, annual forage crop for the grassland rotation system in Southern China. The primary aim of breeding programs is always to seek to improve forage quality in the animal productivity system; however, it is time- and labor-consuming when analyzed excessive large number of samples. The main objectives of this study were to construct near-infrared reflectance spectroscopy (NIRS) models to predict the forage chemistry quality of Italian ryegrass including the concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and water soluble carbohydrate (WSC). The results showed that a broader range of CP, NDF, ADF and WSC contents (%DM) were obtained (4.45–30.60, 21.29–60.47, 11.66–36.17 and 3.95–51.52, respectively) from the samples selected for developing NIRS models. In addition, the critical wavelengths identified in this study to construct optimal NIRS models were located in 4,247–6,102 and 4,247–5,450 cm(-1) for CP and NDF content, and both wavelengths 5,446–6,102 and 4,247–4,602 cm(-1) could for ADF and WSC. Finally, the optimal models were developed based on the laboratory data and the spectral information by partial least squares (PLS) regression, with relatively high coefficients of determination (R(2)(CV), CP = 0.99, NDF = 0.94, ADF = 0.92, WSC = 0.88), ratio of prediction to devitation (RPD, CP = 8.58, NDF = 4.25, ADF = 3.64, WSC = 3.10). The further statistics of prediction errors relative to laboratory (PRL) and the range error ratio (RER) give excellent assessments of the models with the PRL ratios lower than 2 and the RER values greater than 10. The NIRS models were validated using a completely independent set of samples and have coefficients of determination (R(2)(V), CP = 0.99, NDF = 0.91, ADF = 0.95, WSC = 0.91) and ratio of prediction to deviation (RPD, CP = 9.37, NDF = 3.44, ADF = 4.40, WSC = 3.39). The result suggested that routine screening for forage quality parameters with large numbers of samples is available with the NIRS model in Italian ryegrass breeding programs, as well as facilitating graziers to monitor the forage development stage for improving grazing efficiency. PeerJ Inc. 2017-10-03 /pmc/articles/PMC5629960/ /pubmed/29018608 http://dx.doi.org/10.7717/peerj.3867 Text en ©2017 Yang et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Agricultural Science
Yang, Zhongfu
Nie, Gang
Pan, Ling
Zhang, Yan
Huang, Linkai
Ma, Xiao
Zhang, Xinquan
Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum
title Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum
title_full Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum
title_fullStr Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum
title_full_unstemmed Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum
title_short Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum
title_sort development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in lolium multiflorum
topic Agricultural Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629960/
https://www.ncbi.nlm.nih.gov/pubmed/29018608
http://dx.doi.org/10.7717/peerj.3867
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