[1]杜雪燕,王迅,柴沙驼,等.基于近红外光谱的天然牧草CNCPS组分分析与预测[J].江苏农业学报,2015,(05):1115-1123.[doi:doi:10.3969/j.issn.1000-4440.2015.05.027]
　DU Xue-yan,WANG Xun,CHAI Sha-tuo,et al.Analysis and prediction of natural pasture CNCPS(cornell net carbohydrate and protein system) components by near infrared reflectance spectroscopy (NIRS)[J].,2015,(05):1115-1123.[doi:doi:10.3969/j.issn.1000-4440.2015.05.027]
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基于近红外光谱的天然牧草CNCPS组分分析与预测()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2015年05期
页码:
1115-1123
栏目:
畜牧兽医·水产养殖
出版日期:
2015-10-31

文章信息/Info

Title:
Analysis and prediction of natural pasture CNCPS(cornell net carbohydrate and protein system) components by near infrared reflectance spectroscopy (NIRS)
作者:
杜雪燕; 王迅; 柴沙驼; 刘书杰
（1.青海大学/青海省高原放牧家畜营养与饲料科学重点实验室/青海高原牦牛研究中心，青海西宁810016）
Author(s):
DU Xue-yan; WANG Xun; CHAI Sha-tuo; LIU Shu-jie
(1.Qinghai University/Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province/Qinghai Plateau Yak Research Center, Qinghai Province, Xining 810016,China)
关键词:
近红外光谱技术(NIRS); 天然牧草; CNCPS组分; 营养价值
Keywords:
near infrared reflectance spectroscopy (NIRS); natural pasture; cornell net carbohydrate and protein system (CNCPS); nutritional value
分类号:
S816.1
DOI:
doi:10.3969/j.issn.1000-4440.2015.05.027
文献标志码:
A
摘要:
从青海省河南县高山嵩草草地采集天然牧草样品66个，研究近红外光谱技术测定天然牧草净碳水化合物和净蛋白质体系(CNCPS)组分的可行性。选用修正的偏最小二乘法(MPLS)建模，筛选最佳的光谱和数学处理方法，建立了天然牧草中粗蛋白质(CP)、可溶性蛋白质(SP)、非蛋白氮(PA)、快速降解真蛋白(PB1)、中速降解真蛋白(PB2)、慢速降解真蛋白(PB3)、结合粗蛋白(PC)和中性洗涤纤维(NDF)、酸性洗涤纤维(ADF)、酸性洗涤木质素(ADL)、总碳水化合物(CHO)、非结构性碳水化合物(CNSC)、糖类(CA)、淀粉和果胶(CB1)、可利用纤维(CB2)、不可利用纤维(CC)等的近红外定量分析模型。结果显示，CP、PC、NDF、ADF、CHO、CNSC、CA的交叉验证决定系数(1-VR)分别为0.989、0.870、0.975、0.932、0.964、0.966、0.846，交叉验证相对分析误差(RPDCV)分别为9.336、2.913、6.353、3.758、5.306、5.521、2.603，其他指标的1-VR均小于0.9，RPDCV均小于2.5。可见，近红外技术可以用于天然牧草CNCPS组分快速测定，CP、PC、NDF、ADF、CHO、CNSC、CA含量预测模型的预测能力较好，PA、PB1、PB2、PB3、CB1、CB2、CC含量预测模型需要进一步研究以提高精度。
Abstract:
Sixty-six samples collected from alpine grassland of Kobresia hastily in Henan county, Qinghai province were used to investigate the feasibility of predicting the CNCPS (cornell net carbohydrate and protein system) composition of natural pasture by near infrared reflectance spectroscopy (NIRS). Using modified partial least squares (MPLS) regression method, the models of CP(crude protein), SP(soluble protein), PA(non-protein nitrogen)， PB1(rapidly degradable crude protein), PB2(intermediately degradable crude protein), PB3(slowly degradable crude protein), PC(bound crude protein), and models of NDF(neutral detergent fiber), ADF(acid detergent fiber), ADL(acid detergent lignin), CHO(total carbohydrate), CNSC(non-structural carbohydrates), CA(sugars), CB1(starch and pectin), CB2 (available fiber), CC(not available fiber) were built. The results showed 1-VR (cross validation determination coefficient) for CP, PC, NDF, ADF, CHO, CNSC, and CA were 0.989, 0.870, 0.975, 0.932, 0.964, 0.966 and 0.846, respectively, and RPDCV (ratios of standard deviation of reference analysis data to SECV) were 9.336, 2.913, 6.353, 3.758, 5.306, 5.521, and 2.603, respectively. Models with 1-VR less than 0.9 and RPDCV less than 2.5 were not ideal. The results indicated that CNCPS components of natural pasture could be fastly and accurately predicted by NIRS, and the models established were applicable for the predictions of CP, PC, NDF, ADF, CHO, CNSC and CA. Further studies should be focusing on improving the precision of the models for PA, PB1, PB2, PB3, CB1, CB2, and CC.〖JP〗

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备注/Memo

备注/Memo:
收稿日期：2015-02-06 基金项目：公益性行业（农业）科研专项（201303062-1）；国家自然科学基金（地方科学基金）项目（41461081） 作者简介：杜雪燕（1988-），甘肃临洮人，硕士研究生，研究方向为动物营养与饲料科学。（E-mail）duxueyan2012@126.com 通讯作者：柴沙驼，（E-mail）chaishatuo@163.com
更新日期/Last Update: 2015-10-31