聚酯网袋法测定饲料中粗纤维含量的研究

Determination of Crude Fiber Content in Feeds Using Polyester Mesh Bag Method

本研究旨在探索一种快速测定饲料粗纤维(CF)含量的方法,同时评价聚酯网袋法测定饲料中CF含量的准确性和精密度。试验采用聚酯网袋法、传统法(GB/T6434—2006)和Ankom纤维测定仪法,分别测定了豆粕、小麦麸、米糠粕、苜蓿、花生秧等18种饲料中CF的含量;不同地区10个实验室同期用聚酯网袋法测定小麦麸、米糠粕、葵花籽粕、猪配合饲料、兔颗粒配合饲料、苜蓿、花生秧、稻壳8种饲料样品中CF含量,用于检验该法的重复性限和再现性限。结果表明:聚酯网袋法测定饲料中CF含量的结果与传统法差异不显著(P>0.05),表明该法正确度高。实验室间聚酯网袋法测定小麦麸等8种饲料中CF含量的结果重复性变异系数在1.45%~3.64%,再现性变异系数在1.79%~5.64%;重复性限在0.57%~1.99%,再现性限在0.88%~3.47%,表明该法精密度高。综上所述,聚酯网袋法测定饲料中CF的含量,具有成本低、时间短、效率高、正确度和精密度高的优点,可用于大批量样品中CF含量的测定。

This study aimed to explore a rapid method of using polyester mesh bag to determine crude fiber (CF) content in feeds,and to evaluate the accuracy and precision of this method.The CF contents in 18 kinds of feeds such as soybean meal,wheat bran,rice bran,alfalfa and peanut vine and so on were analyzed using the polyester mesh bag method,traditional method(GB/T 6434-2006) and Ankom bag fiber determinator method.The CF contents in 8 kinds of feeds such as wheat bran,rice bran,peanut vine,swine compound feed,rabbit compound feed,alfalfa,peanut vine and rice hull were analyzed in 10 different laboratories using the polyester mesh bag method for assessing repeatability limit and reproducibility limit of this method.The results showed that there were no significant difference between the polyester mesh bag method and traditional method ( P >0.05),indicating that the polyester mesh bag method had higher accuracy.The repeatability variability coefficients of 8 kinds of feeds used in this study,ranged from 1.45% to 3.64%,and the reproducibility variation coefficient was between 1.79% and 5.64%.The repeatability limit was between 0.57% and 1.99%,and the reproducibility limit was between 0.88% and 3.47%,indicating that the polyester mesh bag method had higher precision.In conclusion,the polyester mesh bag method for determining CF contents has the advantages of lower cost,shorter analysis time,higher efficiency and higher accuracy and precision;therefore,this method can determine CF contents of large quantities of samples.