CN111149763B - 一种评价生猪个体蛋白营养状态的方法 - Google Patents
一种评价生猪个体蛋白营养状态的方法 Download PDFInfo
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- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
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Abstract
本发明公开了一种评价生猪个体蛋白营养状态的方法,所述方法是通过同时定量检测血液中生化指标和代谢物中的至少一种来评价生猪个体蛋白营养状态。
Description
技术领域
本发明属于动物营养技术领域,具体涉及一种评价生猪个体蛋白营养状态的方法。
背景技术
近年来,饲料原料的紧缺,人畜争粮状况加剧;超过营养需求的饲料供应也会加重畜禽养殖对环境的压力。为提高饲料报酬,开展精准营养供应变得迫切。精准营养是针对个体不同营养供应状态来供给营养物质的一种精准衔接饲料营养供应和养殖动物营养需求的动物营养策略。动物个体的营养状态是饲料、机体与环境之间相互作用的综合表现,体现了动物自身满足营养需求的能力。目前,生猪的营养状态主要通过外部指标进行测定,如体形外貌直观法、体况分值评价法和体重指数法等,但由于这些指标的主观性、不敏感性及静态性等缺点,导致发现生猪营养不平衡时,其损失将难以弥补和纠正。
蛋白是养殖动物首要的营养物质,如何精准评估个体或亚群体的蛋白营养需求变化及其蛋白营养状态已成为精准营养的基础所在。在目前集约化养殖条件下,由于个体遗传背景变异、环境差异以及健康状况不同等因素的影响,饲喂相同蛋白水平日粮的生猪会出现不同的个体对日粮蛋白营养的需求不同,从而造成不同个体蛋白营养满足程度不一样,使得生猪在采食相同蛋白营养水平日粮后实际生长性能或蛋白营养状态出现较大差异。
血液中的生化指标和营养物质代谢吸收过程中产生的大量代谢物能够快速客观地反映个体变异、营养水平、疾病和环境等因素的影响,并且可通过微创途径对其获得,是理想的蛋白营养状态监测靶点所在组织。目前还没有利用与生猪蛋白营养状态显著相关的血液生化指标和代谢物建立动态回归模型,对生猪蛋白营养状态和营养需求变化进行精准评估的报道。
发明内容
本发明旨在针对目前现有技术的不足,提供一种评价生猪个体蛋白营养状态的方法。
为了达到上述目的,本发明提供的技术方案为:
所述评价生猪个体蛋白营养状态的方法是通过同时定量检测如下血液生化指标和血液代谢物中的至少一种来评价生猪个体蛋白营养状态:尿素、乳酸脱氢酶、肌酸激酶、胰淀粉酶、脯氨酸、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6)、PC(16:0/0:0)、PC(16:0/20:4)、PC(16:1/0:0)、PC(16:1/16:0)、PC(17:0/0:0)、PC(18:0/0:0)、PC(18:0/18:1)、PC(18:0/20:2)、PC(18:0/20:3)、PC(18:2/0:0)、PC(18:2/17:0)、PC(18:3/16:0)、PC(20:3/0:0)、PC(20:3/16:0)、PC(20:4/0:0)、PC(22:6/14:0)、PC(22:6/16:0)、PC(O-16:0/18:2)、PC(O-16:0/18:3)、PC(P-18:0/18:2)、PE(20:4/0:0)、PE(20:4/18:0)、SM(d16:1/16:0)、SM(d16:1/17:0)、SM(d17:1/24:0)、SM(d18:1/20:0)、SM(d18:1/24:0)、SM(d18:2/20:0)。
优选地,所述方法通过定量检测血液中如下A组的血液生化指标和代谢物,或B组的血液生化指标和代谢物,来评价生猪个体蛋白营养状态:
A组:尿素、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6);
B组:乳酸脱氢酶、肌酸激酶、胰淀粉酶、脯氨酸、PC(16:0/0:0)、PC(16:0/20:4)、PC(16:1/0:0)、PC(16:1/16:0)、PC(17:0/0:0)、PC(18:0/0:0)、PC(18:0/18:1)、PC(18:0/20:2)、PC(18:0/20:3)、PC(18:2/0:0)、PC(18:2/17:0)、PC(18:3/16:0)、PC(20:3/0:0)、PC(20:3/16:0)、PC(20:4/0:0)、PC(22:6/14:0)、PC(22:6/16:0)、PC(O-16:0/18:2)、PC(O-16:0/18:3)、PC(P-18:0/18:2)、PE(20:4/0:0)、PE(20:4/18:0)、SM(d16:1/16:0)、SM(d16:1/17:0)、SM(d17:1/24:0)、SM(d18:1/20:0)、SM(d18:1/24:0)、SM(d18:2/20:0)。
优选地,所述方法的具体步骤包括:
(1)建立生长猪生长性能与所述日粮中蛋白质水平的回归模型,确定生长性能最佳时日粮蛋白质水平,并设定该日粮蛋白质水平±1%作为生长猪蛋白营养状态参考标准;
(2)构建反映生长猪蛋白营养状态的组合血液生化指标和代谢物含量(本发明中,血液生化指标尿素含量单位为mmol/L,乳酸脱氢酶、肌酸激酶、胰淀粉酶含量的单位为U/L,血液代谢物含量单位为μmol/L)与日粮蛋白质水平的动态回归模型,所述回归模型如下:
A回归模型:
CP=(0.0067x1+0.021x2+0.0028x3+0.011x4-0.0069x5+0.001x6-0.073x7+0.0022x8-0.0014x9+0.0014x10)×100;其中,所述x1至x10分别为血液中尿素、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6)的含量;
B回归模型:
CP=(1.36x1+8.12x2+1.6x3+1.41x4+2.57x5+1.62x6+1.14x7+2.04x8-2.23x9-2.63x10+1.07x11+2.82x12+1.76x13-2.23x14-3.69x15+6.11x16+2.12x17+2.22x18-1.24x19+1.93x20+1.63x21-2.84x22-3.07x23-2.91x24+3.14x25+3.8x26+4.87x27+3.19x28+4.183x29-4.65x30-5.08x31-4.51x32)×10-4;其中,所述x1至x32分别为血液中乳酸脱氢酶、肌酸激酶、胰淀粉酶、脯氨酸、PC(16:0/0:0)、PC(16:0/20:4)、PC(16:1/0:0)、PC(16:1/16:0)、PC(17:0/0:0)、PC(18:0/0:0)、PC(18:0/18:1)、PC(18:0/20:2)、PC(18:0/20:3)、PC(18:2/0:0)、PC(18:2/17:0)、PC(18:3/16:0)、PC(20:3/0:0)、PC(20:3/16:0)、PC(20:4/0:0)、PC(22:6/14:0)、PC(22:6/16:0)、PC(O-16:0/18:2)、PC(O-16:0/18:3)、PC(P-18:0/18:2)、PE(20:4/0:0)、PE(20:4/18:0)、SM(d16:1/16:0)、SM(d16:1/17:0)、SM(d17:1/24:0)、SM(d18:1/20:0)、SM(d18:1/24:0)、SM(d18:2/20:0)的含量;
所述CP为日粮蛋白质水平,所述日粮蛋白质水平是指日粮中蛋白质的质量百分比含量,单位为%;
(3)测定待测生猪个体血液中A组血液生化指标和代谢物的含量,将测得的结果代入A回归模型,或者,测定待测生猪个体血液中B组血液生化指标和代谢物的含量,将测得的结果代入B回归模型,计算获得待测生猪个体所饲喂日粮蛋白质水平,将测生猪个体所饲喂日粮蛋白质水平与步骤(1)所得的生长猪蛋白营养状态参考标准比对,若在生长猪蛋白营养状态参考标准数值范围内,则表示待测生猪个体蛋白营养状况最佳。
优选地,检测血液时,采用自动生化分析仪、酶联免疫吸附试剂盒等对血液中生化指标进行测定,采用LC/MS、GC/MS等技术对血液代谢物进行分离和检测。
优选地,所述回归模型为偏最小二乘回归模型和LASSO回归模型。
优选地,所述回归模型中回归分析F检验显著性值p<0.05,决定系数R2>0.6。
下面对本发明作进一步说明:
本发明提供了生猪个体蛋白营养状态评估模型2个及营养状态参考标准(日粮蛋白质含量16.3~18.3%),通过测定待测生猪个体的血液生化指标和代谢物含量并代入生猪个体蛋白营养状态评估模型获得日粮蛋白质含量,与生猪个体蛋白营养状态最佳时的日粮蛋白质含量参考标准(16.3~18.3%)进行比对来实现生猪蛋白营养状态的评估;通过生猪个体蛋白营养状态评估模型获得此待测生猪个体获得最佳营养状态所需要的日粮蛋白质含量调整值。
本发明首先用动物样本各个体的血液进行试验,所述动物样本是根据饲喂日粮中的粗蛋白含量进行如下分组的生长猪:无氮日粮组(CP0)、日粮蛋白水平为5%组(CP5)、日粮蛋白水平为9%(CP9)、日粮蛋白水平为12%组(CP12)、日粮蛋白水平为16%组(CP16)、日粮蛋白水平为17%组(CP17)、日粮蛋白水平为18%组(CP18)、日粮蛋白水平为21%组(CP21)、日粮蛋白水平为25%组(CP25)和日粮蛋白水平为30%组(CP30);饲喂所述日粮中,除粗蛋白含量有差别外,其它物质的营养水平相同;所述生猪群体除日粮外的饲养条件均相同;对所述生猪群体中各个体的血液生化指标和代谢物含量进行检测,获得所述每个个体的所述血液生化指标和代谢物含量的数据,将所述数据按所述动物样本的分组统计为CP0、CP5、CP9、CP12、CP16、CP17、CP18、CP21、CP25和CP30组的数据集;
然后对所述数据集进行分析,筛选得到评价生长猪蛋白营养状态的血液生化指标和代谢物:利用Matlab软件对数据集进行R语言编程求解,获得与日粮蛋白水平显著相关的血液生化指标及代谢物组合,分别为①尿素、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6);②乳酸脱氢酶、肌酸激酶、胰淀粉酶、脯氨酸、PC(16:0/0:0)、PC(16:0/20:4)、PC(16:1/0:0)、PC(16:1/16:0)、PC(17:0/0:0)、PC(18:0/0:0)、PC(18:0/18:1)、PC(18:0/20:2)、PC(18:0/20:3)、PC(18:2/0:0)、PC(18:2/17:0)、PC(18:3/16:0)、PC(20:3/0:0)、PC(20:3/16:0)、PC(20:4/0:0)、PC(22:6/14:0)、PC(22:6/16:0)、PC(O-16:0/18:2)、PC(O-16:0/18:3)、PC(P-18:0/18:2)、PE(20:4/0:0)、PE(20:4/18:0)、SM(d16:1/16:0)、SM(d16:1/17:0)、SM(d17:1/24:0)、SM(d18:1/20:0)、SM(d18:1/24:0)、SM(d18:2/20:0);
再建立生长猪生长性能与所述日粮中蛋白质水平的回归模型ADG(g/day)=35.5+35.9×蛋白水平-1.51×蛋白水平2+0.0182×蛋白水平3(决定系数R2=0.945),通过方程求解获得生长性能最佳时所述日粮的蛋白质含量为17.3%,依据生产实际中日粮蛋白水平1%的合理波动范围设定此日粮的蛋白质水平±1%为日粮中蛋白质最佳添加比例,即16.3~18.3%,此范围内生长猪蛋白营养状态最佳,设定此日粮蛋白水平为生长猪蛋白营养状态参考标准;
利用Matlab软件R语言编程求解构建所述评价生长猪蛋白营养状态的血液生化指标和代谢物含量与所述日粮蛋白质水平的多元回归模型;可根据模型决定系数R2选择相对应的最优生猪营养状态评价回归模型:
A回归模型:
CP=(0.0067x1+0.021x2+0.0028x3+0.011x4-0.0069x5+0.001x6-0.073x7+0.0022x8-0.0014x9+0.0014x10)×100(x1-x10分别为血液尿素、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6)的含量);
B回归模型:
CP=(1.36x1+8.12x2+1.6x3+1.41x4+2.57x5+1.62x6+1.14x7+2.04x8-2.23x9-2.63x10+1.07x11+2.82x12+1.76x13-2.23x14-3.69x15+6.11x16+2.12x17+2.22x18-1.24x19+1.93x20+1.63x21-2.84x22-3.07x23-2.91x24+3.14x25+3.8x26+4.87x27+3.19x28+4.183x29-4.65x30-5.08x31-4.51x32)×10-4(x1-x32分别为血液中乳酸脱氢酶、肌酸激酶、胰淀粉酶、脯氨酸、PC(16:0/0:0)、PC(16:0/20:4)、PC(16:1/0:0)、PC(16:1/16:0)、PC(17:0/0:0)、PC(18:0/0:0)、PC(18:0/18:1)、PC(18:0/20:2)、PC(18:0/20:3)、PC(18:2/0:0)、PC(18:2/17:0)、PC(18:3/16:0)、PC(20:3/0:0)、PC(20:3/16:0)、PC(20:4/0:0)、PC(22:6/14:0)、PC(22:6/16:0)、PC(O-16:0/18:2)、PC(O-16:0/18:3)、PC(P-18:0/18:2)、PE(20:4/0:0)、PE(20:4/18:0)、SM(d16:1/16:0)、SM(d16:1/17:0)、SM(d17:1/24:0)、SM(d18:1/20:0)、SM(d18:1/24:0)、SM(d18:2/20:0)的含量);
所述CP为所述日粮的蛋白质水平,单位为%;
测定待测生猪个体血液中A组血液生化指标和代谢物的含量,将测得的结果代入A回归模型,或者,测定待测生猪个体血液中B组血液生化指标和代谢物的含量,将测得的结果代入B回归模型,计算获得待测生猪个体所饲喂日粮蛋白质含量,将测生猪个体所饲喂日粮蛋白质水平与生长猪蛋白营养状态参考标准(16.3~18.3%)比对,若在生长猪蛋白营养状态参考标准数值范围内,则表示待测生猪个体营养状况最佳;不在所述范围内则表示所述待测生猪个体营养状况并没有处于最佳状态,需调整蛋白营养水平;根据预测方程计算获得所述待测生猪个体所饲喂日粮蛋白质含量与生猪个体蛋白营养状态最佳时日粮蛋白质含量参考标准(16.3~18.3%)进行比对获得使此生猪个体获得最佳营养状态所需要的日粮蛋白质水平调整值。
本发明中所述动物为猪,亦可推至其他动物和人。
与现有技术相比,本发明的有益效果为:
能通过测定生猪个体血液样品的任一蛋白营养状态评价指标(血液生化指标及代谢物组合)的含量并代入生猪个体蛋白营养状态评估模型的预测方程中获得日粮蛋白质含量,与生猪个体蛋白营养状态最佳时的日粮蛋白质含量参考标准(16.3~18.3%)进行比对来实现生猪蛋白营养状态的评估,并获得使此生猪个体获得最佳蛋白营养状态所需要的日粮蛋白质含量调整值;利用此生猪个体蛋白营养状态评估模型能迅速掌握生猪蛋白营养状态变化,了解动物的营养需求量,以便及时采取应对措施;对机体健康、动物精准化饲养和精细化管理具有重要的理论和实践意义。
附图说明
图1为日粮蛋白水平对生长猪日增重的影响;图中:ADG:日增重;CP:日粮蛋白质水平(%)。组间不同上标表明差异显著(p<0.05,n=6)。
具体实施方式
下述实施例中所使用的实验方法、材料和试剂如无特殊说明,均为常规方法、材料和试剂,均可从商业途径得到。
1.材料和方法
1)试验动物
选择体重没有显著差异的35kg左右的二元杂(长白×大约克)生长猪60头,随机分为10组,每组6头(n=6),单栏饲养30天,自由饮水但统一定量采食(平均日采食为1.5kg)。
2)试验日粮
依据NRC(2012)标准,设计了10组不同蛋白水平日粮,无氮日粮组(CP0)、日粮蛋白水平为5%组(CP5)、日粮蛋白水平为9%组(CP9)、日粮蛋白水平为12%组(CP12)、日粮蛋白水平为16%组(CP16)、日粮蛋白水平为17%组(CP17)、日粮蛋白水平为18%组(CP18)、日粮蛋白水平为21%组(CP21)、日粮蛋白水平为25%组(CP25)和日粮蛋白水平为30%组(CP30),每组日粮能量相同。日粮组成及营养成分见表1。
表1试验日粮组成及营养水平(干物质基础)
a预混料组成(%):磷酸二氢钙,31.575;石粉,15;乳酸钙,30;食盐,10;氯化胆碱(50%),2.5;防霉剂,2.5;抗氧化剂,1.25;436多维(猪多维),1;CuSO4·5H2O,0.75;硫酸亚铁FeSO4·H2O,0.75;硫酸锌ZnSO4·H2O,0.5;硫酸锰MnSO4·H2O,0.25;有机铬(0.2%),0.375;碘酸钙(1%碘),0.05;有机硒(0.2%),0.375;金霉素(15%),1.25;耐高温植酸酶10000U,0.25;复合酶(888),0.75;包膜VC(90%),0.25;维生素E粉(50%),0.125;枯草芽孢(微生态制剂),0.5。
b营养成分(计算值):标准可消化磷STTD P(%),0.28;钠Sodium(%),0.16;氯Chlorine(%),0.25;盐Salt(%),0.41;铜Copper(ppm),75.6;铁Iron(ppm),90;锌Zinc(ppm),71;锰Manganese(ppm),29.5;铬Chromium(ppm),0.3;碘Iodine(ppm),0.2;硒Selenium(ppm),0.3。
3)试验方法
在试验的开始和结束,分别测定每栏猪的体重。计算平均日增重。动物饲养试验结束后采集血液,利用自动生化分析仪测定血液总蛋白、尿素、血氨、谷丙转氨酶、谷草转氨酶、谷氨酰氨基转移酶、胰淀粉酶、肌酐、葡萄糖、甘油三酯、总胆固醇、高密度脂蛋白、低密度脂蛋白、胆碱酯酶、肌酸激酶、乳酸脱氢酶、免疫球蛋白G和免疫球蛋白M的含量;利用UPLC-QTOF/MS技术对血液极性代谢物和脂质代谢物进行分离和检测,利用MultiQuant数据分析软件对血液极性代谢物和脂质代谢物进行定量。
4)数据分析
利用Matlab软件对数据进行多元回归分析,F检验显著性值p<0.05表示自变量与因变量存在显著回归关系,每个变量的回归系数t检验显著性值Pr(>|t|)<0.05表示该变量能显著影响因变量,决定系数R2>0.6表示所有自变量能解释因变量变化的百分比为60%,越接近1模型拟合优度越好;
2.试验结果
1)生长猪蛋白营养状态评价指标的筛选
对生长猪血液生化指标、血液极性代谢物和脂质代谢物含量数据进行分析,由于各指标数据差异较大,在数据处理之前,对数据进行标准化处理。之后利用Matlab软件对数据集进行R语言编程求解,筛选得到评价生长猪蛋白营养状态的血液生化指标和代谢物组合,分别为①尿素、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6);②乳酸脱氢酶、肌酸激酶、胰淀粉酶、脯氨酸、PC(16:0/0:0)、PC(16:0/20:4)、PC(16:1/0:0)、PC(16:1/16:0)、PC(17:0/0:0)、PC(18:0/0:0)、PC(18:0/18:1)、PC(18:0/20:2)、PC(18:0/20:3)、PC(18:2/0:0)、PC(18:2/17:0)、PC(18:3/16:0)、PC(20:3/0:0)、PC(20:3/16:0)、PC(20:4/0:0)、PC(22:6/14:0)、PC(22:6/16:0)、PC(O-16:0/18:2)、PC(O-16:0/18:3)、PC(P-18:0/18:2)、PE(20:4/0:0)、PE(20:4/18:0)、SM(d16:1/16:0)、SM(d16:1/17:0)、SM(d17:1/24:0)、SM(d18:1/20:0)、SM(d18:1/24:0)、SM(d18:2/20:0)。
2)生长猪蛋白营养状态参考标准的设定
从图1可知,日粮蛋白水平显著影响生长猪的生长性能(p<0.05),生长猪的日增重随着日粮蛋白质水平的增加呈现先上升后下降的趋势;相关性分析也表明生长猪日增重与日粮蛋白水平存在显著相关性(r=0.952),建立生长猪生长性能与日粮蛋白质水平的回归模型ADG(g/day)=35.5+35.9×蛋白水平-1.51×蛋白水平2+0.0182×蛋白水平3(决定系数R2=0.945),通过方程求解获得生长猪生长性能最佳时日粮蛋白质水平为17.3%,依据生产实际中日粮蛋白水平1%的合理波动范围设定17.3±1%为日粮中蛋白质最佳添加比例,即16.3~18.3%,此范围内生长猪蛋白营养状态最佳,因此设定此日粮蛋白水平为生长猪蛋白营养状态参考标准。
3)生长猪蛋白营养状态评价模型的构建
利用筛选得到的生长猪蛋白营养状态评价指标进行多元回归建模,以研究日粮蛋白营养水平与蛋白营养状态评价指标之间的关系,表2列出了回归分析后生长猪蛋白营养状态预测回归方程,构建标准为回归模型F检验显著性值p<0.05和决定系数R2>0.6。表1中最优回归模型决定系数R2可达到1,预测方程为:
CP=(0.0067x1+0.021x2+0.0028x3+0.011x4-0.0069x5+0.001x6-0.073x7+0.0022x8-0.0014x9+0.0014x10)×100(x1-x10分别为血液中尿素、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6)的含量)。
表2生长猪蛋白营养状态预测回归方程
注:x为血液生化指标和代谢物含量,CP为日粮蛋白水平(%),R2为决定系数,p为显著性值。
3.结论
血液生化指标及代谢物可以作为评价生猪个体蛋白营养状态的标志物,其中血液生化指标及代谢物组合分别为①尿素、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6);②乳酸脱氢酶、肌酸激酶、胰淀粉酶、脯氨酸、PC(16:0/0:0)、PC(16:0/20:4)、PC(16:1/0:0)、PC(16:1/16:0)、PC(17:0/0:0)、PC(18:0/0:0)、PC(18:0/18:1)、PC(18:0/20:2)、PC(18:0/20:3)、PC(18:2/0:0)、PC(18:2/17:0)、PC(18:3/16:0)、PC(20:3/0:0)、PC(20:3/16:0)、PC(20:4/0:0)、PC(22:6/14:0)、PC(22:6/16:0)、PC(O-16:0/18:2)、PC(O-16:0/18:3)、PC(P-18:0/18:2)、PE(20:4/0:0)、PE(20:4/18:0)、SM(d16:1/16:0)、SM(d16:1/17:0)、SM(d17:1/24:0)、SM(d18:1/20:0)、SM(d18:1/24:0)、SM(d18:2/20:0)。
这些标志物能够很好地预测生猪的日粮蛋白水平,来反映蛋白营养对生猪机体造成的影响,利用这些标志物作为预测因子构建生猪蛋白营养状态评估模型,其中最优多元预测方程为:
CP=(0.0067x1+0.021x2+0.0028x3+0.011x4-0.0069x5+0.001x6-0.073x7+0.0022x8-0.0014x9+0.0014x10)×100(R2=1,x1-x10分别为血液中尿素、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6)的含量)。
Claims (4)
1.一种评价生猪个体蛋白营养状态的方法,其特征在于,所述方法的具体步骤包括:
(1)建立生长猪生长性能与日粮中蛋白质水平的回归模型,确定生长性能最佳时日粮蛋白质水平,并设定该日粮蛋白质水平±1%作为生长猪蛋白营养状态参考标准;
(2)构建反映生长猪蛋白营养状态的组合血液生化指标和血液代谢物含量与日粮蛋白质水平的动态回归模型,所述回归模型如下:
A回归模型:
CP=(0.0067x1+0.021x2+0.0028x3+0.011x4-0.0069x5+0.001x6-0.073x7+0.0022x8-0.0014x9+0.0014x10)*100;其中,所述x1至x10分别为血液中尿素、β-氨基异丁酸、肌肽、犬尿氨酸、γ-氨基正丁酸、甲基L-组氨酸、N-乙酰鸟氨酸、Cer(d18:0/24:1)、PC(0:0/14:0)、PC(0:0/22:6)的含量;
B回归模型:
CP=(1.36x1+8.12x2+1.6x3+1.41x4+2.57x5+1.62x6+1.14x7+2.04x8-2.23x9-2.63x10+1.07x11+2.82x12+1.76x13-2.23x14-3.69x15+6.11x16+2.12x17+2.22x18-1.24x19+1.93x20+1.63x21-2.84x22-3.07x23-2.91x24+3.14x25+3.8x26+4.87x27+3.19x28+4.183x29-4.65x30-5.08x31-4.51x32)*10-4;其中,所述x1至x32分别为血液中乳酸脱氢酶、肌酸激酶、胰淀粉酶、脯氨酸、PC(16:0/0:0)、PC(16:0/20:4)、PC(16:1/0:0)、PC(16:1/16:0)、PC(17:0/0:0)、PC(18:0/0:0)、PC(18:0/18:1)、PC(18:0/20:2)、PC(18:0/20:3)、PC(18:2/0:0)、PC(18:2/17:0)、PC(18:3/16:0)、PC(20:3/0:0)、PC(20:3/16:0)、PC(20:4/0:0)、PC(22:6/14:0)、PC(22:6/16:0)、PC(O-16:0/18:2)、PC(O-16:0/18:3)、PC(P-18:0/18:2)、PE(20:4/0:0)、PE(20:4/18:0)、SM(d16:1/16:0)、SM(d16:1/17:0)、SM(d17:1/24:0)、SM(d18:1/20:0)、SM(d18:1/24:0)、SM(d18:2/20:0)的含量;
所述CP为日粮蛋白质水平,所述日粮蛋白质水平是指日粮中蛋白质的质量百分比含量,单位为%;
(3)测定待测生猪个体血液中A组血液生化指标和血液代谢物的含量,将测得的结果代入A回归模型,或者,测定待测生猪个体血液中B组血液生化指标和血液代谢物的含量,将测得的结果代入B回归模型,计算获得待测生猪个体所饲喂日粮蛋白质水平,将待测生猪个体所饲喂日粮蛋白质水平与步骤(1)所得的生长猪蛋白营养状态参考标准比对,若在生长猪蛋白营养状态参考标准数值范围内,则表示待测生猪个体蛋白营养状况最佳。
2.如权利要求1所述的方法,其特征在于,检测血液时,采用自动生化分析仪或酶联免疫吸附实验对血液中生化指标进行测定,采用LC/MS或GC/MS技术对血液代谢物进行分离和检测。
3.如权利要求1所述的方法,其特征在于,所述回归模型为偏最小二乘回归模型和LASSO回归模型。
4.如权利要求1所述的方法,其特征在于,所述回归模型中回归分析F检验显著性值P<0.05,决定系数R2>0.6。
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