草地学报2026，Vol.34Issue(8)：2889-2898,10.DOI:10.11733/j.issn.1007-0435.2026.08.013
多环境燕麦干草主要品质成分近红外光谱模型构建及应用
Near-Infrared Spectroscopy Models for Assessing Oat Hay Quality Across Diverse Environments
薛敏 1李嘉若 1郭玉帆 1孟雨 1王渝 1胡晓莹 1杜会龙 1何强1
作者信息
- 1. 河北大学生命科学学院,河北 保定 071000
- 折叠
摘要
Abstract
To develop a rapid quality detection model for oat(Avena sativa)hay applicable across environ-ments,849 samples were collected at three sites(Baoding and Zhangjiakou in Hebei,Guyuan in Ningxia)for two years.Crude protein(CP),neutral detergent fiber(NDF),acid detergent fiber(ADF),soluble sugars(SS),and total potassium(K)were measured.Prediction models were established using near-infrared spec-troscopy(NIRS)combined with partial least squares regression(PLSR).All five traits showed broad,near-normal distributions,indicating good sample representativeness.The CP,SS,and K models performed well,with cross-validation coefficients(R²)≥0.89,ratio of performance to deviation(RPD)≥3.0,and valida-tion set correlations(r)of 0.84,0.86,and 0.83,respectively,demonstrating strong external predictive abil-ity.The NDF and ADF models achieved R²≥0.85 and RPD≥2.5,with r values of 0.78 and 0.79,meet-ing requirements for routine screening.Based on model predictions and forage quality standards,56 high-qual-ity forage oat accessions were identified.This study provides an efficient technical platform for high-throughput phenotyping and quality-based breeding in oat germplasm.关键词
燕麦干草/近红外光谱/品质成分/预测模型/偏最小二乘法/种质资源Key words
Oat hay/Near-infrared spectroscopy/Quality components/Prediction model/Partial least squares/Germplasm resources分类
农业科技引用本文复制引用
薛敏,李嘉若,郭玉帆,孟雨,王渝,胡晓莹,杜会龙,何强..多环境燕麦干草主要品质成分近红外光谱模型构建及应用[J].草地学报,2026,34(8):2889-2898,10.
