EXPERIMENTAL METHOD AND ANIMAL

Development of Near-Infrared Spectroscopy Prediction Model for Nutritional Composition of Natural Mixed Forage in Eastern Region of Three-Rivers Source

  • YANG Jinfen ,
  • MA Cunxia ,
  • LI Yumin ,
  • DU Xueyan ,
  • HAO Lizhuang ,
  • XIANG Yang ,
  • BAI Binqiang
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  • 1. College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China;
    2. Qinghai Province Forage Technology Extension Station, Xining 810016, China;
    3. State Key Laboratory of Three-Rivers Source Ecology and Plateau Agriculture and Animal Husbandry, Qinghai Province Key Laboratory of Animal Nutrition and Feed Science for Plateau Grazing Animals, Xining 810016, China

Received date: 2021-05-09

  Online published: 2021-12-16

Abstract

This experiment was conducted to build the near-infrared spectroscopy prediction model for nutritional composition of natural mixed forage in eastern region of three-rivers source. A total of 301 natural mixed forage samples were collected from 4 sample points in the eastern region of the three-rivers source, and the samples were divided into calibration set and verification set according to the ratio of 7:3. The measured values and near-infrared spectroscopy values of the calibration set samples were used to construct and cross-validate the crude protein (CP), neutral detergent fiber (NDF) and acid detergent fiber (ADF) prediction models, and the values of verification set samples were used for external verification to further evaluate the prediction effect of the prediction models. The results showed that the best method of data multiprocessing in the construction of CP prediction model was standard normalization processing+de-scattering processing+second-order derivation, and the best method of data multiprocessing in the construction of NDF and ADF prediction models was multivariate dispersion correction+second order derivation. The determination coefficient of calibration (Rcal2) and cross-validation correlation coefficient (1-VR) of CP, NDF and ADF prediction models were all higher than 0.900, and the model prediction value predicted by the models for the validation set samples had no significant difference with the chemical measured value (P>0.05), and the determination coefficient of verification (RCV2) was higher than 0.900, and the cross-verify relative standard deviation (RPDcal) and external validation relative to standard deviation (RPDCV) were both greater than 3.00. The results indicate that near-infrared spectroscopy technology can be used to evaluate the nutritional value of natural mixed forage, and the prediction models in this study have good prediction effects, it can be applied to the actual production.

Cite this article

YANG Jinfen , MA Cunxia , LI Yumin , DU Xueyan , HAO Lizhuang , XIANG Yang , BAI Binqiang . Development of Near-Infrared Spectroscopy Prediction Model for Nutritional Composition of Natural Mixed Forage in Eastern Region of Three-Rivers Source[J]. Chinese Journal of Animal Nutrition, 2021 , 33(12) : 7042 -7049 . DOI: 10.3969/j.issn.1006-267x.2021.12.044

References

[1] TORRES I,PÉREZ-MARÍN D,DE LA HABA M J,et al.Fast and accurate quality assessment of Raf tomatoes using NIRS technology[J].Postharvest Biology and Technology,2015,107:9-15.
[2] DING X X,GUO Y,NI Y N,et al.A novel NIR spectroscopic method for rapid analyses of lycopene,total acid,sugar,phenols and antioxidant activity in dehydrated tomato samples[J].Vibrational Spectroscopy,2016,82:1-9.
[3] RUNGPICHAYAPICHET P,MAHAYOTHEE B,NAGLE M,et al.Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango[J].Postharvest Biology and Technology,2016,111:31-40.
[4] SIMON C J,RODEMANN T,CARTER C G.Near-infrared spectroscopy as a novel non-invasive tool to assess spiny lobster nutritional condition[J].PLoS One,2016,11(7):e0159671.
[5] NORRIS K,BARNES R F,MOORE J E,et al.Predicting forage quality by infrared reflectance spectroscopy[J].Journal of Animal Science,1976,43(4):889-897.  
[6] CAMPO L,MONTEAGUDO A B,SALLERES B,et al.NIRS determination of non-structural carbohydrates,water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability[J].Spanish Journal of Agricultural Research,2013,11(2):463-471.  
[7] CHEN J S,ZHU R F,XU R X,et al.Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses[J].PeerJ,2015,3(7):e1416.
[8] RUSHING J B,SAHA U K,LEMUS R,et al.Analysis of some important forage quality attributes of southeastern wildrye (Elymus glabriflorus) using near-infrared reflectance spectroscopy[J].American Journal of Analytical Chemistry,2016,7(9):642-662.  
[9] YANG Z F,NIE G,PAN L,et al.Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum[J].PeerJ,2017,5:e3867.
[10] MONRROY M,GUTIÉRREZ D,MIRANDA M,et al.Determination of Brachiaria spp. forage quality by near-infrared spectroscopy and partial least squares regression[J].Journal of the Chilean Chemical Society,2017,62(2):3472-3477.  
[11] PATTON L,MCCONNELL D,ARCHER J,et al.Portable NIRS:a novel technology for the prediction of forage nutritive quality[J].Grassland Science in Europe,2018,23:892-894.
[12] 纳嵘,胡波,史蓓.近红外光谱技术测定苜蓿蛋白质含量的研究[J].畜牧与饲料科学,2018,39(8):31-34. NA R,HU B,SHI B.Measurement of protein content of alfalfa using near-infrared reflectance spectroscopy[J].Animal Husbandry and Feed Science,2018,39(8):31-34.(in Chinese)
[13] 石丹,张英俊.近红外光谱法快速测定羊草干草品质的研究[J].光谱学与光谱分析,2011,31(10):2730-2733. SHI D,ZHANG Y J.Determination of Leymus chinensis quality by near infrared reflectance spectroscopy[J].Spectroscopy and Spectral Analysis,2011,31(10):2730-2733.(in Chinese)
[14] 李洁,王建福,吴建平,等.可见/近红外漫反射光谱分析法测定牧草营养成分研究[J].家畜生态学报,2014,35(4):54-58. LI J,WANG J F,WU J P,et al.Study on the determination of nutritional ingredient in native grass with visible/near infrared reflectance spectroscopy[J].Acta Ecologae Animalis Domastici,2014,35(4):54-58.(in Chinese)
[15] 杨天辉,常生华,莫本田,等.黄土高原13种栽培牧草营养成分NIRS模型分析[J].草业科学,2017,34(3):575-581. YANG T H,CHANG S H,MO B T,et al.Analysis of nutritional content in 13 forage crop varieties in the Loess Plateau based on visible/near infrared reflectance spectroscopy[J].Pratacultural Science,2017,34(3):575-581.(in Chinese)
[16] CUNNIFF P.Official methods of analysis of AOAC International[M].16th ed.Arlington:AOAC International,1995.
[17] VAN SOEST P J,ROBERTSON J B,LEWIS B A.Methods for dietary fiber,neutral detergent fiber,and nonstarch polysaccharides in relation to animal nutrition[J].Journal of Dairy Science,1991,74(10):3583-3597.  
[18] 刘哲,王玉琴,薛树媛,等.近红外漫反射光谱定量分析天然牧草营养成分[J].草地学报,2018,26(1):249-255. LIU Z,WANG Y Q,XUE S Y,et al.Quantitative analysis of herbage nutritional components on natural pasture by near infrared diffuse reflectance spectroscopy[J].Acta Agrestia Sinica,2018,26(1):249-255.(in Chinese)
[19] 薛祝林,刘楠,张英俊.近红外光谱法预测紫花苜蓿草捆的营养品质和消化率[J].草地学报,2017,25(1):165-171. XUE Z L,LIU N,ZHANG Y J.Nutritional quality and digestibility evaluation of alfalfa hay bale by near infrared reflectance[J].Acta Agrestia Sinica,2017,25(1):165-171.(in Chinese)
[20] 杜雪燕,王迅,柴沙驼,等.天然牧草营养成分的近红外光谱定量分析[J].中国农学通报,2015,31(17):6-11. DU X Y,WANG X,CHAI S T,et al.Quantitative analysis of nutrition composition of native grasses by near infrared reflectance spectroscopy[J].Chinese Agricultural Science Bulletin,2015,31(17):6-11.(in Chinese)
[21] 姚喜喜,孙海群,李长慧,等.高寒草原天然牧草营养品质近红外光谱预测模型的建立[J/OL].动物营养学报,2021:1-10.(2021-04-20)[2021-05-07].http://kns.cnki.net/kcms/detail/11.5461.S.20210419.1118.068.html. YAO X X,SUN H Q,LI C H,et al.The establishment of near-infrared spectral prediction model of natural pasture nutrition quality in high-cold grasslands[J/OL].Chinese Journal of Animal Nutrition,2021:1-10.(2021-04-20)[2021-05-07].http://kns.cnki.net/kcms/detail/11.5461.S.20210419.1118.068.html.(in Chinese)
[22] 王勇生,李洁,王博,等.基于近红外光谱技术评估高粱中粗蛋白质、水分含量的研究[J].动物营养学报,2020,32(3):1353-1361. WANG Y S,LI J,WANG B,et al.Research on evaluation of crude protein and moisture contents in sorghum grain based on near-infrared spectroscopy technique[J].Chinese Journal of Animal Nutrition,2020,32(3):1353-1361.(in Chinese)
[23] 高燕丽,孙彦.利用近红外光谱分析预测紫花苜蓿干草品质[J].草地学报,2015,23(5):1080-1085. GAO Y L,SUN Y.Quality evaluation of alfalfa hay by the spectroscopic analysis of near infrared reflectance[J].Acta Agrestia Sinica,2015,23(5):1080-1085.(in Chinese)
[24] SHENK J S,LANDA I,HOOVER M R,et al.Description and evaluation of a near infrared reflectance spectro-computer for forage and grain analysis[J].Crop Science,1981,21(3):355-358.  
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