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

ZHANG Shuyue; XIONG Anran; PAN Yucong; YU Shiqiang; JIANG Linshu; XIONG Benhai

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

Chinese Journal of Animal Nutrition >

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

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

RESEARCH PAPER

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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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 (RSQ_cal) 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 (RPD_CV) was 2.50 to 4.23, the calibration effect was relatively satisfactory. The externally verified prediction coefficient (RSQ_v) was 0.846 to 0.945, and the ratio of performance to deviation for validation (RPD_V) 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 RPD_CV was 1.80, the external verification result RSQv was 0.735, RPD_V 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

Cite this article

ZHANG Shuyue , XIONG Anran , PAN Yucong , YU Shiqiang , JIANG Linshu , XIONG Benhai . Near Infrared Prediction Model Establishment for Conventional Nutrient Contents of Oat Grass[J]. Chinese Journal of Animal Nutrition, 2022 , 34(2) : 1334 -1342 . DOI: 10.3969/j.issn.1006-267x.2022.02.065

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