动物营养学报 >

2021 , Vol. 33 >Issue 5: 2939 - 2948

DOI: https://doi.org/10.3969/j.issn.1006-267x.2021.05.051

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

苜蓿干草不同处理方式对近红外预测模型预测准确性的影响

  • 郭涛 ,
  • 黄右琴 ,
  • 代露茗 ,
  • 郭龙 ,
  • 李发弟 ,
  • 潘发明 ,
  • 张兆杰 ,
  • 李飞
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  • 1. 兰州大学草地农业科技学院, 兰州大学草地农业生态系统国家重点实验室, 农业农村部草牧业创新重点实验室, 兰州 730020;
    2. 甘肃省肉羊繁育生物技术工程实验室, 民勤 733300;
    3. 甘肃省农业科学院畜草与绿色农业研究所, 兰州 730070;
    4. 景泰县草窝滩镇畜牧兽医站, 白银 730400
郭涛(1994-),男,甘肃庆阳人,硕士研究生,从事饲料资源开发与利用研究。E-mail:guot2018@lzu.edu.cn

收稿日期: 2020-10-28

  网络出版日期: 2021-05-14

基金资助

农业农村部农牧交错带牛羊牧繁农育关键技术集成示范项目（16200158）；公益性行业（农业）科研专项——北方农作物秸秆饲用化利用技术研究与示范（201503134）

收起

Effects of Different Processing Methods of Alfalfa Hay on Accuracy of Near-Infrared Prediction Models

  • GUO Tao ,
  • HUANG Youqin ,
  • DAI Luming ,
  • GUO Long ,
  • LI Fadi ,
  • PAN Faming ,
  • ZHANG Zhaojie ,
  • LI Fei
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  • 1. Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, State Key Laboratory of Grassland Agri-Ecosystems of Lanzhou University, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China;
    2. Engineering Laboratory of Mutton Sheep Breeding and Reproduction Biotechnology, Minqin 733300, China;
    3. Institute of Animal & Pasture Science and Green Agriculture of Gansu Academy of Agricultural Science, Lanzhou 730070, China;
    4. Animal Husbandry and Veterinary Station of Caowotan Town, Baiyin 730400, China

Received date: 2020-10-28

  Online published: 2021-05-14

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摘要

本试验旨在利用近红外光谱（NIRS）技术分别建立切短苜蓿干草（3~5 cm）和粉碎苜蓿干草（过1.0 mm筛）的干物质（DM）、粗蛋白质（CP）、中性洗涤纤维（NDF）、酸性洗涤纤维（ADF）、粗脂肪（EE）和粗灰分（Ash）预测模型，并且分析这2种处理方式对近红外预测模型预测准确性的影响。试验共采集186份苜蓿干草样品，选取149份作为定标集，37份作为验证集，利用近红外光谱技术结合改良偏最小二乘法（MPLS）分别建立切短苜蓿干草和粉碎苜蓿干草的营养成分含量预测模型。结果表明：1）利用近红外光谱技术建立的切短苜蓿干草NDF预测模型可用于精确预测，DM和ADF预测模型可用于实际生产中的预测，Ash和CP预测模型只能用于粗略的筛选分析，EE预测模型不可用。2）利用近红外光谱技术建立的粉碎苜蓿干草DM、NDF和ADF预测模型可用于精确预测，Ash预测模型可用于实际生产中的预测，CP和EE预测模型只能用于粗略的筛选分析。3）利用近红外光谱技术建立的切短苜蓿干草各营养成分预测模型的预测偏差与粉碎苜蓿干草无显著差异（P>0.05），但预测决定系数（RSQ）和外部验证相对分析误差（RPD）低于粉碎苜蓿干草，说明切短处理各营养成分定标模型预测分析准确性低于粉碎苜蓿干草。由此可见，利用近红外光谱技术建立的预测模型可用于预测切短苜蓿干草和粉碎苜蓿干草的DM、NDF和ADF含量，切短苜蓿干草营养成分定标模型预测分析准确性低于粉碎苜蓿干草。

关键词： 苜蓿干草; 处理方式; 近红外光谱; 准确性

本文引用格式

郭涛 , 黄右琴 , 代露茗 , 郭龙 , 李发弟 , 潘发明 , 张兆杰 , 李飞 . 苜蓿干草不同处理方式对近红外预测模型预测准确性的影响[J]. 动物营养学报, 2021 , 33(5) : 2939 -2948 . DOI: 10.3969/j.issn.1006-267x.2021.05.051

Abstract

The experiment is aimed to establish the dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), ether extract (EE) and crude ash (Ash) prediction models of chopped alfalfa hay (3 to 5 cm) and crushed alfalfa hay (passing 1.0 mm sieve) by near infrared spectroscopy (NIRS) technology, and to analyze the effects of these two processing methods on the prediction accuracy of near-infrared prediction models. A total of 186 alfalfa hay samples were collected in the experiment, 149 samples were used as the calibration sets and 37 samples were used as the validation sets. The nutrient contents prediction models of chopped alfalfa hay and crushed alfalfa hay were established by using near-infrared spectroscopy and modified partial least squares (MPLS). The results showed as follows:1) the NDF prediction model of chopped alfalfa hay established by near-infrared spectroscopy could be used for accurate prediction, the DM and ADF prediction models could be used for prediction in actual production, the Ash and CP prediction models could only be used for rough screening analysis, and the EE prediction model was not available. 2) The DM, NDF and ADF prediction models of crushed alfalfa hay established by near-infrared spectroscopy technology could be used for accurate prediction, the Ash prediction model could be used for prediction in actual production, and the CP and EE prediction models could only be used for rough screening analysis. 3) The prediction bias of the nutrient composition prediction model of chopped alfalfa hay established by near-infrared spectroscopy technology had no significant difference with that of crushed alfalfa hay (P>0.05), but the coefficient of determination for validation (RSQ) and the ratio of performance to deviation for validation (RPD) was lower than that of crushed alfalfa hay, indicating that the accuracy of the prediction and analysis of the calibration model for each nutrient component of the chopped treatment was lower than that of crushed alfalfa hay. It can be seen that the prediction model established by near-infrared spectroscopy technology can be used to predict the content of DM, NDF and ADF of chopped alfalfa hay and crushed alfalfa hay, and the prediction and analysis accuracy of the nutrient composition calibration model of chopped alfalfa hay is lower than that of crushed alfalfa hay.

Key words： alfalfa hay; processing methods; near infrared reflectance spectroscopy; accuracy

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