基于近红外光谱的北京市全株玉米原料康奈尔净碳水化合物-蛋白质体系组分分析与预测
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摘要
本试验旨在基于康奈尔净碳水化合物-蛋白质体系(CNCPS)建立北京市全株玉米原料营养成分数据库,并利用近红外光谱(NIRS)方法建立其营养价值预测模型。试验采集北京市18个牧场89份全株玉米原料样品,测定其营养成分,利用CNCPS 6.5计算各样品碳水化合物(CHO)和蛋白质组成。定标集和验证集根据4∶1的配比关系,分别选用71份和18份全株玉米原料样品作为定标集和验证集评价NIRS模型。结果显示:1) NIRS分析技术对全株玉米原料常规营养成分、CNCPS中蛋白质组成和CHO组成均具有较好的预测能力,且精确度较高。2)干物质(DM)、粗灰分(Ash)、粗蛋白质(CP)、粗脂肪(EE)、中性洗涤纤维(NDF)、酸性洗涤纤维(ADF)、酸性洗涤木质素(ADL)、淀粉(Starch)、中性洗涤不溶蛋白质(NDIP)、酸性洗涤不溶蛋白质(ADIP)、可溶性蛋白质(SP)、CHO、非纤维性碳水化合物(NFC)、可溶性纤维(CB2)、可消化纤维(CB3)、不消化纤维(CC)、可溶性真蛋白质(PA2)、难溶性真蛋白质(PB1)、纤维结合蛋白质(PB2)和非降解蛋白质(PC)的定标决定系数(1-VR)均>0.80,验证决定系数(RSQv)均≥0.84,这些模型均可用于日常快速检测分析。DM、Ash、EE、NDF、ADF、ADL、Starch、NDIP、CHO、NFC、CB2、CB3、PC和PB1的NIRS模型参数均采用二阶导数处理,CP、SP、ADIP、CC、PA2和PB2的NIRS模型参数均采用标准正态变量+二阶导数处理。综上所述,本研究提供了全株玉米原料的基础化学分析数据,并通过NIRS分析技术建立了主要营养成分的快速预测模型,有利于养殖场青贮前对全株玉米原料质量的快速评估。
This study aimed to establish a database of nutrient components of whole-plant corn raw material in Beijing based on the Cornell net carbohydrate and protein system( CNCPS),and found nutrient value prediction models using the method of near infrared reflectance spectroscopy( NIRS). A total of 89 whole-plant corn raw material samples were collected from 18 dairy farms in Beijing,and the nutrient components were determined,then the carbohydrate( CHO) and protein components were calculated by CNCPS 6.5. The calibration set and verification set were based on a 4∶1 ratio,and NIRS models were evaluated using 71 and 18 samples of whole-plant corn raw material as calibration and validation database,respectively. The results showed as follows:1) the conventional nutrient components of whole-plant corn raw material,protein composition and CHO composition in CNCPS system could be quite accurately estimated by NIRS technology. 2) The cross validation determinant coefficients( 1-VR) were >0.8,and the verification decision coefficients( RSQv) were ≥0.84 for the model parameters of dry matter( DM),crude ash( Ash),crude protein( CP),ether extract( EE),neutral detergent fiber( NDF),acid detergent fiber( ADF),acid detergent lignin( ADL),starch( Starch),neutral detergent insoluble protein( NDIP),acid detergent insoluble protein( ADIP),soluble protein( SP),CHO,non fiber carbohydrates( NFC),soluble fiber( CB2),digestible fiber( CB3),indigestible fiber( CC),soluble true protein( PA2),insoluble true protein( PB1),fiber conjugated protein( PB2) and undegradable protein( PC),which suggested that these models could be used for rapidly actual analysis. Model parameters of DM,Ash,EE,NDF,ADF,ADL,Starch,NDIP,CHO,NFC,CB2,CB3,PC and PB1 were processed by second derivative,and CP,SP,ADIP,CC,PA2 and PB2 were processed by standard normal variate+second derivative. In conclusion,the study provide chemical analysis data of hole-plant corn raw material,and establish models for prediction of main nutrient components by NIRS technology. It is beneficial to the evaluation of the quality of whole-plant corn raw material before silage in farms.[Chinese Journal of Animal Nutrition,2019,31( 5) : 2287-2295]
This study aimed to establish a database of nutrient components of whole-plant corn raw material in Beijing based on the Cornell net carbohydrate and protein system( CNCPS),and found nutrient value prediction models using the method of near infrared reflectance spectroscopy( NIRS). A total of 89 whole-plant corn raw material samples were collected from 18 dairy farms in Beijing,and the nutrient components were determined,then the carbohydrate( CHO) and protein components were calculated by CNCPS 6.5. The calibration set and verification set were based on a 4∶1 ratio,and NIRS models were evaluated using 71 and 18 samples of whole-plant corn raw material as calibration and validation database,respectively. The results showed as follows:1) the conventional nutrient components of whole-plant corn raw material,protein composition and CHO composition in CNCPS system could be quite accurately estimated by NIRS technology. 2) The cross validation determinant coefficients( 1-VR) were >0.8,and the verification decision coefficients( RSQv) were ≥0.84 for the model parameters of dry matter( DM),crude ash( Ash),crude protein( CP),ether extract( EE),neutral detergent fiber( NDF),acid detergent fiber( ADF),acid detergent lignin( ADL),starch( Starch),neutral detergent insoluble protein( NDIP),acid detergent insoluble protein( ADIP),soluble protein( SP),CHO,non fiber carbohydrates( NFC),soluble fiber( CB2),digestible fiber( CB3),indigestible fiber( CC),soluble true protein( PA2),insoluble true protein( PB1),fiber conjugated protein( PB2) and undegradable protein( PC),which suggested that these models could be used for rapidly actual analysis. Model parameters of DM,Ash,EE,NDF,ADF,ADL,Starch,NDIP,CHO,NFC,CB2,CB3,PC and PB1 were processed by second derivative,and CP,SP,ADIP,CC,PA2 and PB2 were processed by standard normal variate+second derivative. In conclusion,the study provide chemical analysis data of hole-plant corn raw material,and establish models for prediction of main nutrient components by NIRS technology. It is beneficial to the evaluation of the quality of whole-plant corn raw material before silage in farms.[Chinese Journal of Animal Nutrition,2019,31( 5) : 2287-2295]
引文
[1]吴端钦,张爱忠,姜宁,等.用CNCPS评定反刍动物几种常用精料营养价值的研究[J].中国牛业科学,2009,35(1):9-12.
[2]曲永利,吴健豪,张永根.用CNCPS体系评定黑龙江省西部地区奶牛饲料营养价值[J].中国畜牧杂志,2009,45(11):42-45.
[3]白扎嘎尔.近红外光谱分析技术(NIRS)在反刍动物饲料分析中的应用研究[D].硕士学位论文.呼和浩特:内蒙古农业大学,2010.
[4] AOAC.Official methods of analysis of AOAC International,16th edition.Volume 1[J]. Trends in Food Science&Technology,1995,6(11):382-382.
[5] SNIFFEN C J,O’CONNOR J D,VAN SOEST P J,et al.A net carbohydrate and protein system for evaluating cattle diets:Ⅱ.Carbohydrate and protein availability[J]. Journal of Animal Science,1992,70(11):3562-3577.
[6] KRISHNAMOORTHY U,SNIFFEN C J,STERN M D,et al.Evaluation of a mathematical model of rumen digestion and an in vitro simulation of rumen proteolysis to estimate the rumen-undegraded nitrogen content of feedstuffs[J].British Journal of Nutrition,1983,50(3):555-568.
[7] VAN AMBURGH M E,COLLAO-SAENZ E A,HIGGS R J,et al. The cornell net carbohydrate and protein system:updates to the model and evaluation of version 6. 5[J]. Journal of Dairy Science,2015,98(9):6361-6380.
[8]周军琴.基于近红外光谱技术快速检测稻米营养品质和加工精度的研究[D].硕士学位论文.兰州:甘肃农业大学,2016.
[9]王芳彬.基于CNCPS和近红外光谱技术评定油菜秸秆营养价值[D].硕士学位论文.兰州:甘肃农业大学,2016.
[10]李玉鹏.近红外反射光谱快速评定棉粕营养价值的研究[D].硕士学位论文.兰州:甘肃农业大学,2016.
[11]褚小立.化学计量学方法与分子光谱分析技术[M].北京:化学工业出版社,2011.
[12]王保平,董晓燕,董宽虎,等.有机酸对全株玉米青贮有氧稳定性的影响[J].草地学报,2013,21(5):991-997.
[13] ROW C A.Corn plant maturity effect on yield and nutritional quality; corn silage inoculation on performance of cattle fed silage w ith or w ithout live yeast added[D].M aster’s thesis.Lincoln:University of Nebraska,2015.
[14]和立文.全株玉米青贮品质评价及其对肉牛育肥性能和牛肉品质的影响[D].博士学位论文.北京:中国农业大学,2017.
[15]李红亚,李文,李术娜,等.解淀粉芽孢杆菌复合菌剂对玉米秸秆的降解作用及表征[J].草业学报,2017,26(6):153-167.
[16] MALLEY D F,RONIKE H,FINDLAY D L,et al.Feasibility of using near-infrared reflectance spectroscopy for the analysis of C,N,P and diatoms in lake sediments[J]. Journal of Paleolimnology,1999,21(3):295-306.
[17]李洁,王建福,吴建平,等.可见/近红外漫反射光谱分析法测定牧草营养成分研究[J].家畜生态学报,2014,35(4):54-58.
[18] MODRONO S, SOLDADO A, MARTINEZFERNANDEZ A,et al. Handheld NIRS sensors for routine compound feed quality control:real time analysis and field monitoring[J]. Talanta,2017,162:597-603.
[19]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(5):1115-1123.
[20]张鹏,卜登攀,张养东,等.康奈尔净碳水化合物-净蛋白质体系(CNCPS)在反刍动物上的应用研究[J].中国奶牛,2014(5):7-10.
[21]周俊华,邹彩霞,梁贤威,等.应用康奈尔净碳水化合物-蛋白质体系评定广西水牛常用粗饲料的营养价值[J].动物营养学报,2011,23(12):2190-2197.
[22]杨方,解成威,刘大森,等.基于近红外光谱的玉米籽粒CNCPS组分分析及预测研究[J].光谱学与光谱分析,2010,30(2):348-352.
[23]王艳龙,王胜宝,李新生.近红外光谱技术在农业领域的应用及展望[J].安徽农学通报,2012,18(15):33-35.
[24] HIGGS R J,CHASE L E,ROSS D A,et al.Updating the Cornell net carbohydrate and protein system feed library and analyzing model sensitivity to feed inputs[J]. Journal of Dairy Science,2015,98(9):6340-6360.
[2]曲永利,吴健豪,张永根.用CNCPS体系评定黑龙江省西部地区奶牛饲料营养价值[J].中国畜牧杂志,2009,45(11):42-45.
[3]白扎嘎尔.近红外光谱分析技术(NIRS)在反刍动物饲料分析中的应用研究[D].硕士学位论文.呼和浩特:内蒙古农业大学,2010.
[4] AOAC.Official methods of analysis of AOAC International,16th edition.Volume 1[J]. Trends in Food Science&Technology,1995,6(11):382-382.
[5] SNIFFEN C J,O’CONNOR J D,VAN SOEST P J,et al.A net carbohydrate and protein system for evaluating cattle diets:Ⅱ.Carbohydrate and protein availability[J]. Journal of Animal Science,1992,70(11):3562-3577.
[6] KRISHNAMOORTHY U,SNIFFEN C J,STERN M D,et al.Evaluation of a mathematical model of rumen digestion and an in vitro simulation of rumen proteolysis to estimate the rumen-undegraded nitrogen content of feedstuffs[J].British Journal of Nutrition,1983,50(3):555-568.
[7] VAN AMBURGH M E,COLLAO-SAENZ E A,HIGGS R J,et al. The cornell net carbohydrate and protein system:updates to the model and evaluation of version 6. 5[J]. Journal of Dairy Science,2015,98(9):6361-6380.
[8]周军琴.基于近红外光谱技术快速检测稻米营养品质和加工精度的研究[D].硕士学位论文.兰州:甘肃农业大学,2016.
[9]王芳彬.基于CNCPS和近红外光谱技术评定油菜秸秆营养价值[D].硕士学位论文.兰州:甘肃农业大学,2016.
[10]李玉鹏.近红外反射光谱快速评定棉粕营养价值的研究[D].硕士学位论文.兰州:甘肃农业大学,2016.
[11]褚小立.化学计量学方法与分子光谱分析技术[M].北京:化学工业出版社,2011.
[12]王保平,董晓燕,董宽虎,等.有机酸对全株玉米青贮有氧稳定性的影响[J].草地学报,2013,21(5):991-997.
[13] ROW C A.Corn plant maturity effect on yield and nutritional quality; corn silage inoculation on performance of cattle fed silage w ith or w ithout live yeast added[D].M aster’s thesis.Lincoln:University of Nebraska,2015.
[14]和立文.全株玉米青贮品质评价及其对肉牛育肥性能和牛肉品质的影响[D].博士学位论文.北京:中国农业大学,2017.
[15]李红亚,李文,李术娜,等.解淀粉芽孢杆菌复合菌剂对玉米秸秆的降解作用及表征[J].草业学报,2017,26(6):153-167.
[16] MALLEY D F,RONIKE H,FINDLAY D L,et al.Feasibility of using near-infrared reflectance spectroscopy for the analysis of C,N,P and diatoms in lake sediments[J]. Journal of Paleolimnology,1999,21(3):295-306.
[17]李洁,王建福,吴建平,等.可见/近红外漫反射光谱分析法测定牧草营养成分研究[J].家畜生态学报,2014,35(4):54-58.
[18] MODRONO S, SOLDADO A, MARTINEZFERNANDEZ A,et al. Handheld NIRS sensors for routine compound feed quality control:real time analysis and field monitoring[J]. Talanta,2017,162:597-603.
[19]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(5):1115-1123.
[20]张鹏,卜登攀,张养东,等.康奈尔净碳水化合物-净蛋白质体系(CNCPS)在反刍动物上的应用研究[J].中国奶牛,2014(5):7-10.
[21]周俊华,邹彩霞,梁贤威,等.应用康奈尔净碳水化合物-蛋白质体系评定广西水牛常用粗饲料的营养价值[J].动物营养学报,2011,23(12):2190-2197.
[22]杨方,解成威,刘大森,等.基于近红外光谱的玉米籽粒CNCPS组分分析及预测研究[J].光谱学与光谱分析,2010,30(2):348-352.
[23]王艳龙,王胜宝,李新生.近红外光谱技术在农业领域的应用及展望[J].安徽农学通报,2012,18(15):33-35.
[24] HIGGS R J,CHASE L E,ROSS D A,et al.Updating the Cornell net carbohydrate and protein system feed library and analyzing model sensitivity to feed inputs[J]. Journal of Dairy Science,2015,98(9):6340-6360.