动物营养学报 >

2021 , Vol. 33 >Issue 12: 7042 - 7049

DOI: https://doi.org/10.3969/j.issn.1006-267x.2021.12.044

实验方法与实验动物 EXPERIMENTAL METHOD AND ANIMAL

三江源东部地区天然混合牧草营养成分近红外光谱预测模型构建

  • 杨金芬 ,
  • 马存霞 ,
  • 李毓敏 ,
  • 杜雪燕 ,
  • 郝力壮 ,
  • 项洋 ,
  • 拜彬强
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  • 1. 青海大学农牧学院, 西宁 810016;
    2. 青海省饲草料技术推广站, 西宁 810016;
    3. 青海省高原放牧家畜动物营养与饲料科学重点实验室, 省部共建三江源生态与高原农牧国家重点实验室, 西宁 810016
杨金芬(1996-),女,青海海东人,硕士研究生,研究方向为牧草营养价值评价。E-mail:2710531354@qq.com

收稿日期: 2021-05-09

  网络出版日期: 2021-12-16

基金资助

省部共建三江源生态与高原农牧业国家重点实验室自主课题（2019-ZZ-19）；青海大学青年科研基金项目（2020-QNY-4）；中国科学院"西部之光"人才培养引进计划（1_7）；青海省"昆仑英才·高端创新创业人才"拔尖人才项目

收起

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

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摘要

本试验旨在构建三江源东部地区天然混合牧草营养成分近红外光谱预测模型。从三江源东部地区4个样点采集天然混合牧草样品301份，按照7：3的比例将样品分为定标集和验证集。采用定标集样品的实测值及近红外光谱值进行粗蛋白质（CP）、中性洗涤纤维（NDF）和酸性洗涤纤维（ADF）预测模型的构建及交叉验证，采用验证集样品进行外部验证，进一步评价预测模型的预测效果。结果表明：CP预测模型构建中数据预处理的最佳方法为标准正态化处理+去散射处理+二阶求导，NDF和ADF预测模型构建中数据预处理的最佳方法为多元离散校正+二阶求导。CP、NDF和ADF预测模型的定标决定系数（Rcal2）、交叉验证相关系数（1-VR）均高于0.900，预测模型对验证集样品的模型预测值与化学实测值之间差异不显著（P>0.05），且验证决定系数（RCV2）均高于0.900，交叉验证相对标准差（RPDcal）和外部验证相对标准差（RPDCV）均大于3.00。这表明近红外光谱技术可用于天然混合牧草营养价值的评定，本试验构建的预测模型预测效果较好，可应用于实际生产。

关键词： 近红外光谱技术; 天然混合牧草; 营养成分; 预测模型

本文引用格式

杨金芬 , 马存霞 , 李毓敏 , 杜雪燕 , 郝力壮 , 项洋 , 拜彬强 . 三江源东部地区天然混合牧草营养成分近红外光谱预测模型构建[J]. 动物营养学报, 2021 , 33(12) : 7042 -7049 . DOI: 10.3969/j.issn.1006-267x.2021.12.044

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.

Key words： near-infrared spectroscopy technology; natural mixed forage; nutritional composition; prediction model

参考文献

[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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