特征光谱提取法在饲料桑青贮品质近红外光谱快速检测技术中的应用

doi:10.11733/j.issn.1007-0435.2026.05.028

草地学报 ›› 2026, Vol. 34 ›› Issue (5): 1878-1888.DOI: 10.11733/j.issn.1007-0435.2026.05.028

• 技术研发 • 上一篇

特征光谱提取法在饲料桑青贮品质近红外光谱快速检测技术中的应用

李沁雨¹, 杨雪萍¹, 杨富裕^1,2, 倪奎奎¹

1. 中国农业大学草业科学与技术学院, 北京 100193;
2. 贵州大学动物科学学院, 贵州贵阳 550025

收稿日期:2025-06-04 修回日期:2025-10-26 发布日期:2026-05-08
通讯作者: 杨雪萍，E-mail:xueping.yang@cau.edu.cn;倪奎奎，E-mail:nikk@cau.edu.cn
作者简介:李沁雨（2000-），女，汉族，新疆伊犁人，博士研究生，主要从事牧草加工利用研究，E-mail:b20243241329@cau.edu.cn；
基金资助:
畜禽饲料饲草精细化加工技术研究（2021YFD1300300）资助

Rapid Detection of Mulberry Silage Quality Based on Near-Infrared Characteristic Spectral Interval Extraction

LI Qin-yu¹, YANG Xue-ping¹, YANG Fu-yu^1,2, NI Kui-kui¹

1. College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China;
2. College of Animal Science, Guizhou University, Guiyang, Guizhou Province 550025, China

Received:2025-06-04 Revised:2025-10-26 Published:2026-05-08

摘要/Abstract

摘要： 为实现近红外光谱分析技术在饲料桑（Morus alba L.）青贮饲料品质上的应用，本研究收集105份饲料桑青贮样品，基于区间偏最小二乘法（interval Partial Least Squares，iPLS）提取饲料桑青贮的特征光谱区间，并采用偏最小二乘法（Partial Least Squares，PLS）构建了近红外快速检测模型。结果表明，粗蛋白、酸性洗涤纤维、中性洗涤纤维、pH的模型最优，能够用于实际预测（R²cv，R²p>0.8； RPDcv，RPDp>2.5）；干物质、乳酸、缩合单宁的模型较好，可以进行粗略预测（2<RPDcv<2.5，2<RPD<2.5）；水解单宁的模型则需进一步优化（RPDp<2）。因此，基于特征光谱法建立的模型在稳定性及预测精度方面更具优势，可以为饲料桑青贮高效生产与利用提供理论依据与技术支持，同时为近红外分析仪器开发提供思路。

关键词: 饲料桑青贮, 青贮饲料, 近红外光谱, 特征光谱提取, 化学计量学

Abstract: To advance the application of near-infrared （NIR） spectroscopy in assessing the quality of mulberry （Morus alba L.） silage， a total of 105 silage samples were collected. Interval Partial Least Squares （iPLS） was employed to extract informative spectral intervals， and rapid NIR prediction models were subsequently developed using Partial Least Squares （PLS）. The results indicated that the models for crude protein， acid detergent fiber， neutral detergent fiber， and pH exhibited the best performance and were suitable for practical prediction （R²cv， R²p>0.8； RPDcv， RPDp>2.5）. Models for dry matter， lactic acid， and condensed tannins demonstrated moderate performance and were adequate for approximate estimation （2<RPDcv<2.5； 2<RPDp<2.5）. In contrast， the model for hydrolyzable tannins required further refinement （RPDp<2）. Collectively， these findings suggest that models constructed based on characteristic spectral intervals offer enhanced stability and predictive accuracy. This approach provides a theoretical basis and technical support for the efficient production and utilization of mulberry silage， while also offering valuable insights for the development of NIR analytical instrumentation.

Key words: Mulberry silage, Silage, Near-Infrared Spectroscopy （NIRS）, Characteristic spectrum extraction, Chemometrics

中图分类号:

S816.5+3

李沁雨, 杨雪萍, 杨富裕, 倪奎奎. 特征光谱提取法在饲料桑青贮品质近红外光谱快速检测技术中的应用[J]. 草地学报, 2026, 34(5): 1878-1888.

LI Qin-yu, YANG Xue-ping, YANG Fu-yu, NI Kui-kui. Rapid Detection of Mulberry Silage Quality Based on Near-Infrared Characteristic Spectral Interval Extraction[J]. Acta Agrestia Sinica, 2026, 34(5): 1878-1888.

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特征光谱提取法在饲料桑青贮品质近红外光谱快速检测技术中的应用

Rapid Detection of Mulberry Silage Quality Based on Near-Infrared Characteristic Spectral Interval Extraction

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