摘要
本研究旨在利用近红外光谱(NIRS)技术结合改良偏最小二乘法(MPLS)建立牛、羊精料补充料中7种常规营养成分[水分、有机物(OM)、粗蛋白质(CP)、中性洗涤纤维(NDF)、酸性洗涤纤维(ADF)、粗脂肪(EE)和粗灰分(Ash)]和5种矿物元素(钾、磷、钠、镁和铁)含量的快速预测模型.结果表明:1)常规营养成分中,Ash含量预测模型的预测决定系数(R2P)和相对预测偏差(RPD)分别为0.74和1.96,仅能用于快速筛选分析;水分、OM、CP、NDF、ADF和EE含量预测模型的RP2和RPD分别为0.90～0.98和2.95～6.66,均可用于实际定量分析.2)矿物元素中,钾和铁含量预测模型的RP2分别为0.70和0.73,RPD分别为1.84和1.83,可用于快速筛选分析;磷、钠和镁含量预测模型的RP2分别为0.56、0.31和0.63,RPD分别为1.50、1.12和1.65,需进一步调整优化才能用于实际生产.综上所述,本研究成功建立了牛、羊精料补充料营养成分及矿物元素含量的NIRS快速预测模型,多数成分预测准确性高,部分矿物元素模型需进一步优化.
Abstract
The aim of this study was to establish the rapid prediction models for the contents of seven conven-tional nutrients[moisture,organic matter(OM),crude protein(CP),neutral detergent fiber(NDF),acid detergent fiber(ADF),ether extract(EE)and ash(Ash)]and five mineral elements(potassium,phosphor-us,sodium,magnesium and iron)in cattle and sheep concentrate supplements by near infrared spectroscopy(NIRS)combined with modified partial least squares(MPLS).The results showed as follows:1)in conven-tional nutrients,the coefficient of determination in prediction(RP2)and relative prediction deviation(RPD)of Ash content prediction model were 0.74 and 1.96,respectively,which could be used for rapid screening analy-sis;the RP2 and RPD of moisture,OM,CP,NDF,ADF and EE content prediction models were 0.90 to 0.98 and 2.95 to 6.66,respectively,which could be used for actual quantitative analysis.2)In mineral elements,the RP of potassium and iron content prediction models were 0.70 and 0.73,respectively,and the RPD were 1.84 and 1.83,respectively,which could be used for rapid screening analysis;the RP2 of phosphorus,sodium and magnesium content prediction models were 0.56,0.31 and 0.63,respectively,and the RPD were 1.50,1.12 and 1.65,respectively,which needed to be further adjusted and optimized for practical production.In conclusion,this study successfully establish NIRS rapid prediction models for the nutrient and mineral element content of cattle and sheep concentrate supplements,most of the component predictions have high accuracy,but some mineral element models need further optimization.[Chinese Journal of Animal Nutrition,2024,36(12):8088-8099]
维普
万方数据