动物营养学报 >

2022 , Vol. 34 >Issue 2: 1334 - 1342

DOI: https://doi.org/10.3969/j.issn.1006-267x.2022.02.065

研究论文 RESEARCH PAPER

燕麦草常规营养成分含量近红外预测模型的建立

  • 张书阅 ,
  • 熊安然 ,
  • 潘予琮 ,
  • 余诗强 ,
  • 蒋林树 ,
  • 熊本海
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  • 1. 北京农学院动物科学技术学院, 奶牛营养学北京市重点实验室, 北京 102206;
    2. 中国农业科学院 北京畜牧兽医研究所, 北京 100193
张书阅(1998-),女,山东临沂人,硕士研究生,从事奶牛营养与免疫调控研究。E-mail:zhangshuyue1223@163.com

收稿日期: 2021-07-07

  网络出版日期: 2022-02-15

基金资助

国家"十三五"重点研发计划（2017YFD0701604-2）；北京市现代农业产业技术体系奶牛创新团队

收起

Near Infrared Prediction Model Establishment for Conventional Nutrient Contents of Oat Grass

  • ZHANG Shuyue ,
  • XIONG Anran ,
  • PAN Yucong ,
  • YU Shiqiang ,
  • JIANG Linshu ,
  • XIONG Benhai
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  • 1. Beijing Key Laboratory of Cow Nutrition, Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China;
    2. Beijing Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China

Received date: 2021-07-07

  Online published: 2022-02-15

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摘要

本试验旨在构建燕麦草常规营养成分含量的近红外预测模型。试验于2017—2019年，从我国京津冀等地区的牧场及种植基地收集了80份不同品种、不同产地和不同成熟度的燕麦草，参照燕麦草常规营养成分国标检测方法测定采集80份样品中水分（MSTR）、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）含量并进行燕麦草近红外光谱采集。使用OPUS7.5中的偏最小二乘（PLS）化学计量学方法将燕麦草的光谱图和理化指标进行关联，交叉检验法评价预测模型效果。结果显示：不同来源的燕麦草中MSTR、CP、NDF、ADF、EE和Ash含量变异较大；MSTR、CP、NDF、ADF和Ash含量预测模型校正决定系数（RSQcal）为0.886~0.977，交叉验证决定系数（1-VR）为0.84~0.95，交叉验证相对分析误差（RPDCV）为2.50~4.23，定标效果较为理想，外部验证预测决定系数（RSQv）为0.846~0.945，预测相对分析误差（RPDV）为2.57~4.20，表明模型均可应用于实际检测且适用性良好；EE含量预测模型RSQcal为0.870，1-VR为0.772，RPDCV为1.80，外部验证结果RSQv为0.735，RPDV为1.95，模型效果不理想，不能应用于实际检测。综上所述，本试验初步建立燕麦草中MSTR、CP、NDF、ADF和Ash含量的近红外预测模型效果较好，为生产中快速高效测定燕麦草常规营养成分提供技术支撑。

关键词： 燕麦草; 常规营养成分; 近红外光谱技术; 模型构建

本文引用格式

张书阅 , 熊安然 , 潘予琮 , 余诗强 , 蒋林树 , 熊本海 . 燕麦草常规营养成分含量近红外预测模型的建立[J]. 动物营养学报, 2022 , 34(2) : 1334 -1342 . DOI: 10.3969/j.issn.1006-267x.2022.02.065

Abstract

In order to establish the near infrared prediction model of oat grass conventional nutrient contents, eighty samples of oatgrass from different pastures and planting bases in Beijing, Tianjin, Hebei and other regions were collected during 2017 to 2019. The contents of moisture (MSTR), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), ether extract (EE) and ash (Ash) in 80 samples were determined reference to the national standard testing method for oatgrass nutrients and the oat grass near-infrared spectroscopy was collected. The partial least squares regression (PLS) chemometric method in OPUS 7.5 was used to correlate the spectra of oat grass with physical and chemical indicators, and the cross-check method was used to evaluate the effect of the prediction model. The results showed as follows:the contents of MSTR, CP, NDF, ADF, EE and Ash in oat grass from different sources varied greatly; the calibration decision coefficient (RSQcal) of MSTR, CP, NDF, ADF and Ash content prediction model was 0.886 to 0.977, cross-validation determination coefficient (1-VR) was 0.84 to 0.95, cross-validation relative analysis error (RPDCV) was 2.50 to 4.23, the calibration effect was relatively satisfactory. The externally verified prediction coefficient (RSQv) was 0.846 to 0.945, and the ratio of performance to deviation for validation (RPDV) was 2.57 to 4.20, indicating that the model would be applied to actual testing and has good applicability; the RSQcal of EE content prediction model was 0.870, 1-VR was 0.772, and RPDCV was 1.80, the external verification result RSQv was 0.735, RPDV was 1.95, the model effect was not ideal, and it cannot be applied to actual detection. It is concluded that the preliminary establishment of the near-infrared prediction model of MSTR, CP, NDF, ADF and Ash contents in oat grass in this study is effective, and it provides technical support for the rapid and efficient determination of conventional nutrients of oat grass in production.

Key words： oat grass; conventional nutrients; near-infrared spectroscopy technique; model construction

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