奶牛全混合日粮常规营养成分含量近红外快速检测模型的构建与应用

曹明月; 郑爱荣; 娄渊志; 李贤; 马爱华; 兰剑; 兰尊海

doi:10.3969/j.issn.1006-267x.2020.07.052

动物营养学报 >

2020 , Vol. 32 >Issue 7: 3420 - 3427

DOI: https://doi.org/10.3969/j.issn.1006-267x.2020.07.052

研究简报 SHORT COMMUNICATION

奶牛全混合日粮常规营养成分含量近红外快速检测模型的构建与应用

曹明月 ,
郑爱荣 ,
娄渊志 ,
李贤 ,
马爱华 ,
兰剑 ,
兰尊海

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1. 河南海瑞正检测技术有限公司, 郑州 450001;
2. 河南省饲草饲料站, 郑州 450008;
3. 延津县农业农村局, 延津 453200;
4. 驻马店市兽药饲料(动物产品)质量检验监测中心, 驻马店 463000;
5. 河南牧业经济学院, 郑州 450046

曹明月(1991-),女,河南周口人,硕士,从事饲料检测技术研究。E-mail:835910061@qq.com

收稿日期: 2020-01-15

网络出版日期: 2020-07-15

基金资助

河南省畜牧业发展资金（奶业发展专项）

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Construction and Application of Near Infrared Reflectance Spectroscope Rapid Detection Model for Routine Nutritional Components Contents of Total Mixed Ration in Dairy Cattle

CAO Mingyue ,
ZHNEG Airong ,
LOU Yuanzhi ,
LI Xian ,
MA Aihua ,
LAN Jian ,
LAN Zunhai

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1. Henan Hai-Rui-Zheng Testing Technology Co., Ltd., Zhengzhou 450001, China;
2. Forage Feeds Service Station of Henan Province, Zhengzhou 450008, China;
3. Yanjin Agricultural and Rural Rureau, Yanjin 453200, China;
4. Zhumadian Veterinary Drug Feed(Animal Products) Quality Inspection and Monitoring Center, Zhumadian 463000, China;
5. Henan University of Animal Husbandry and Economy, Zhengzhou 450046, China

Received date: 2020-01-15

Online published: 2020-07-15

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摘要

本研究主要利用近红外光谱（NIRS）技术构建奶牛全混合日粮（TMR）常规营养成分含量快速检测模型，并探究其应用的可行性。选取542份奶牛TMR为研究对象，随机分成定标集（n=481）和验证集（n=61）。运用偏最小二乘法，结合4种散射校正和10种导数处理方法，建立奶牛TMR干物质（DM）、粗蛋白质（CP）、粗脂肪（EE）、淀粉（Starch）、中性洗涤纤维（NDF）、酸性洗涤纤维（ADF）、氨基酸总量（total AA）7种常规营养成分含量NIRS预测模型，并验证其模型的准确度。结果表明：CP含量的预测决定系数（RSQ_V）和外部验证相对分析误差（RPD_V）最高，而DM、NDF、ADF、total AA含量略低于CP；DM、CP、NDF、ADF、total AA含量的RSQ_V均大于0.83，RPD_V均大于2.50，表明这5个指标可达到精准预测效果，能达到实际生产检测应用的水平；而EE含量的RSQ_V和RPD_V分别为0.788和2.192，分别低于0.80和2.50，表明EE含量的模型仅能达到粗略估测水平，暂不能用于实际生产应用的准确测定。而Starch含量的RSQ_V和RPD_V分别为0.562和1.504，分别低于0.66和2.00，表明该预测模型效果不理想，不能用于Starch含量的预测。综上所述，本研究初步建立TMR的NIRS预测模型可精准预测DM、CP、NDF、ADF、total AA 5个指标的含量，只能粗估预测EE含量，尚不能预测Starch含量，这为TMR数据库的建设和实际生产的快速测定提供了便利。

关键词： 近红外光谱技术; 全混合日粮; 常规营养成分

本文引用格式

曹明月 , 郑爱荣 , 娄渊志 , 李贤 , 马爱华 , 兰剑 , 兰尊海 . 奶牛全混合日粮常规营养成分含量近红外快速检测模型的构建与应用[J]. 动物营养学报, 2020 , 32(7) : 3420 -3427 . DOI: 10.3969/j.issn.1006-267x.2020.07.052

Abstract

This experiment was aimed to establish the prediction model of routine nutritional components contents of total mixed ration (TMR) by near infrared reflectance spectroscope (NIRS) technique, and explore its feasibility. A population of 542 TMR samples were divided into calibration set (n=481) and verification set (n=61). Using partial least squares (PLS) method with four spectral pretreatments and ten derivative treatments, the prediction models of 7 kinds of routine nutrient[dry matter (DM), crude protein(CP), ether extract (EE), Starch, neutral detergent fiber (NDF), acid detergent fiber (ADF) and total amino acid (total AA)] contents in TMR were established and verified the accuracy of models. The results showed that the coefficient of determination for validation (RSQ_V) and ratio of performance to deviation for validation (RPD_V) of CP content were the highest, while those of DM, NDF, ADF and total AA contents were slightly lower than those of CP. RSQ_V and RPD_V of DM, CP, NDF, ADF and total AA contents were more than 0.83 and 2.50, respectively, indicating that the modeling effect of the five indexes were good and could be used to detect the actual content. However, the RSQ_V of EE content was 0.788 and less than 0.80, while the RPD_V was 2.192 and less than 2.50. It showed that the model of EE content could be predicted roughly and could not be used to detect the actual content. The RSQ_V of Starch was 0.562 and less than 0.66, while the RPD_V was 1.504, which was less than 2.00. It showed that the predictive effect was unsatisfying and the contents of Starch could not be predicted. It is concluded that the contents of DM, CP, NDF, ADF and total AA in TMR can be accurately predicted by NIRS technology, the contents of EE can be predicted roughly, while the contents of Starch cannot be predicted, which improves the convenience for the rapid test and database of the TMR nutrient.

Key words： NIRS; TMR; routine nutritional components

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