| 检测燕麦干草主要营养成分含量的近红外光谱分析模型 |
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| 引用本文： | 王储,麻冬梅,李跃,屈新月,李智林,胡倩楠,乔葭月,孙彦.检测燕麦干草主要营养成分含量的近红外光谱分析模型[J].草地学报,2022,30(10):2645-2651. |
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| 作者姓名： | 王储 麻冬梅 李跃 屈新月 李智林 胡倩楠 乔葭月 孙彦 |
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| 作者单位： | 1. 中国农业大学草业科学与技术学院, 北京 100093;2. 宁夏大学生态环境学院, 宁夏 银川 750021 |
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| 基金项目： | 宁夏自治区农业育种专项(2019NYYZ0401)；国家牧草产业技术体系项目资助(编号：CARS-34)资助 |
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| 摘 要： | 为探索NIRS技术在测定燕麦(Avena sative)干草品质上的应用，试验于2020—2021年收集了249份不同品种、年限和生长时期的燕麦干草，通过WinISI III定标软件建立燕麦干草主要营养成分的近红外光谱模型。结果显示：粗蛋白(CP)、中性洗涤纤维(NDF)和粗脂肪(EE)预测模型的定标系数(RSQ)和外部验证决定系数(RSQv)均在0.83以上，校正标准误(SEC)、交叉验证误差(SECV)和预测标准误差(RMSEP)均小于0.02，相对标准误差(RPD)均大于3，预测值逼近化学分析的精度具有良好的预测效果。酸性洗涤纤维含量(ADF)建模效果较差，定标系数和外部验证决定系数分别为0.83和0.84，校正标准误(SEC)、交叉验证误差(SECV)和预测标准误差(RMSEP)均小于0.01，接近化学分析精度，且RPD大于2.50。因此，所建ADF模型也可用于近红外预测。
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| 关 键 词： | 燕麦干草 主要营养成分 近红外光谱 |
| 收稿时间： | 2022-03-29 |
Near Infrared Spectroscopic Model for the Detection of the Main Nutrient Components of Oat Hay |
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| WANG Chu,MA Dong-mei,LI Yue,QU Xin-yue,LI Zhi-lin,HU Qian-nan,QIAO Jia-yue,SUN Yan.Near Infrared Spectroscopic Model for the Detection of the Main Nutrient Components of Oat Hay[J].Acta Agrestia Sinica,2022,30(10):2645-2651. |
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| Authors: | WANG Chu MA Dong-mei LI Yue QU Xin-yue LI Zhi-lin HU Qian-nan QIAO Jia-yue SUN Yan |
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| Institution: | 1. College of Grassland Science and Technology, China Agricultural University, Beijing 100093, China;2. College of Ecology and Environment Ningxia University, Yinchuan, Ningxia Province 750021, China |
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| Abstract: | To explore the application of the NIRS technique in determining the quality of oat hay (Avena sative),the trial collected 249 oat hays of different varieties,years,and growth periods from 2020-2021. The NIR spectra of the main nutrients of oat hay were modelled by WinISI III calibration software. The results showed that the calibration coefficients (RSQ) and prediction coefficient (RSQv) of the prediction models for crude protein (CP),neutral detergent fiber (NDF) and ether extract (EE) were all above 0.83,the standard errors of correction (SEC),cross-validation errors (SECV) and prediction standard error (RMSEP) were all less than 0.02,and the RPDs were all greater than 3. The accuracy of the approximate chemical analysis has good predictive effect. Acid detergent fiber content (ADF) was poorly modelled,with a coefficient of determination for both calibration and external validation of 0.83 and 0.84 respectively. The standard error of calibration (SEC),cross-validation error (SECV) and prediction standard error (RMSEP) were all less than 0.01,close to the accuracy of the chemical analysis. The RPD is greater than 2.50,indicating that the proposed ADF model can also be used for NIR prediction. |
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| Keywords: | Oat hay Main nutrients NIRS technology |
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