应用绵羊粪便的近红外光谱方程评定其日粮的粗蛋白

利用近红外光谱法(NIRS)对绵羊粪便的扫描值和日粮粗蛋白的化学测定值来建立定标方程式.试验以绵羊为试验动物,日粮主要由各种牧草、作物秸秆和棉花籽壳组成,试验动物日粮设计了78个蛋白水平.在2002年和2003年分别用15只和20只成熟母羊(体重为55±2.4 kg)进行了为期7周的试验.用凯式定氮法测定日粮的粗蛋白(CP)水平是从4.3 %到23.5 %,日粮粗蛋白的定标方程式决定系数R2=0.95,定标标准误差(SEC)=1.08.用12只饲喂美国北部大平原饲草的成年母羊的粪便光谱扫描值和与粪便对应的日粮粗蛋白的化学分析数据来校验粗蛋白预测方程式的有效性,结果显示,决定系数(R2)=0.81,预测标准误差(SEP)=1.51,斜率=0.89,表明利用近红外光谱法(NIRS)发展的粪便近红外光谱方程可以有效预测绵羊日粮的粗蛋白含量.     

应用锦羊粪便的近红外光谱方程评定其日粮的粗蛋白

利用近红外光谱法 (NIRS)对绵羊粪便的扫描值和日粮粗蛋白的化学测定值来建立定标方程式。试验以绵羊为试验动物 ,日粮主要由各种牧草、作物秸秆和棉花籽壳组成 ,试验动物日粮设计了 78个蛋白水平。在 2 0 0 2年和 2 0 0 3年分别用 15只和 2 0只成熟母羊 (体重为 5 5± 2 .4kg)进行了为期 7周的试验。用凯式定氮法测定日粮的粗蛋白 (CP)水平是从 4 .3%到 2 3.5 % ,日粮粗蛋白的定标方程式决定系数R2 =0 .95 ,定标标准误差 (SEC) =1.0 8。用 12只饲喂美国北部大平原饲草的成年母羊的粪便光谱扫描值和与粪便对应的日粮粗蛋白的化学分析数据来校验粗蛋白预测方程式的有效性 ,结果显示 ,决定系数 (R2 ) =0 .81,预测标准误差 (SEP) =1.5 1,斜率 =0 .89,表明利用近红外光谱法 (NIRS)发展的粪便近红外光谱方程可以有效预测绵羊日粮的粗蛋白含量。     

绵羊对低蛋白日粮消化代谢规律的研究

试验选用3只平均体重为44.8kg的杂种公绵羊,采用3×3拉丁方设计。饲喂粗蛋白水平由7.6%～13.0%的12种典型混合日粮。结果表明:随着日粮粗蛋白水平(CP)的提高,日粮粗蛋白(CP)、干物质(DM)、有机物(OM)、酸性洗涤纤维(ADF)和中性洗涤纤维(NDF)消化率逐渐提高,其相关关系分别为CPD(CP的消化率,%)=8.70+4.84CP(%/DM()n=12,R2=0.88,P<0.01);DMD(DM的消化率,%)=45.40+2.14CP(%/DM()n=12,R2=0.65,P<0.01);OMD(OM的消化率,%)=42.85+2.31CP(%/DM()n=12,R2=0.67,P<0.01);ADFD(ADF的消化率,%)=19.08+3.05CP(%/DM()n=12,R2=0.66,P<0.01);NDFD(NDF的消化率,%)=26.81+2.64CP(%/DM)(n=12,R2=0.63,P<0.01)。     

近红外光谱技术快速测定豆粕、玉米真可消化氨基酸含量的研究

本试验应用近红外光谱技术（ＮＩＲＳ）预测了豆粕、玉米中的真可消化氨基酸含量。氨基酸消化率用去盲肠公鸡作试验动物,用修正的Ｓｉｂｂａｌｄ强饲法进行测定。定标的结果表明,豆粕中除与胱氨酸有关的几个方程外,其它氨基酸的定标经检验证明具有良好的预测性能。玉米真可消化氨基酸的定标性能不如豆粕好,目前还不能进行实际的应用,但大部分氨基酸定标方程的相关系数经Ｆ检验达到极显著水平,说明用ＮＩＲＳ预测玉米真可消化氨基酸是可行的。近红外光谱技术提供了一种可用于日常测定鸡真可消化氨基酸的即时分析方法,它能够替代查书面值以及使用耗时而昂贵的体内法测定真可消化氨基酸含量。营养学家可根据真可消化氨基酸数据进行饲料配方,起到准确、及时、低投入、高产出、低污染、高效利用饲料资源的作用。     

杜寒杂交绵羊可消化有机物采食量与尿嘌呤衍生物排出量的相关关系研究

试验旨在研究杜寒杂交绵羊在不同日粮精粗比条件下可消化有机物采食量和尿嘌呤衍生物排出量的相关关系.选择12只体况健康的9月龄杜寒杂交绵羊公羊(47.21 kg±3.35 kg),试验日粮为12种不同精粗比的全混合颗粒饲料,采用12×4不完全拉丁方设计,进行4期消化代谢试验,每期19d,其中预饲期14d,正试期5d,采用全收粪、尿法.结果表明:随日粮精粗比例的升高,可消化有机物采食量显著升高(P＜0.05);干物质、有机物和蛋白质消化率均与干物质采食量存在正线性相关(R2=0.45,P＜0.05;R2=0.50,P＜0.05;R2=0.64,P＜0.05);随日粮精粗比例升高,尿嘌呤衍生物排出量显著升高(P＜0.05),尿囊素、尿酸、黄嘌呤和次黄嘌呤排出量占总尿嘌呤衍生物排出量的比例变化范围分别为69.92％～84.76％、2.89 ％～7.58％和9.39％～25.04％.尿嘌呤衍生物排出量与可消化有机物采食量存在线性相关(P＜0.05),相关方程为Y=13.03X+0.62(R2=0.80).     

不同中性洗涤纤维与非纤维性碳水化合物比值饲粮对肉用绵羊甲烷排放的影响

本试验旨在研究不同中性洗涤纤维(NDF)与非纤维性碳水化合物(NFC)比值(NDF/NFC)饲粮对肉用绵羊甲烷排放的影响。试验采用4×4完全拉丁方试验设计,将16只杜泊×小尾寒羊杂交羯羊随机分成4组,每组4只,按维持水平饲喂NDF/NFC分别为3.02(饲粮1)、2.32(饲粮2)、1.58(饲粮3)、1.04(饲粮4)的全混合颗粒饲粮(玉米秸秆为粗饲料来源)。试验共进行4期,每期18 d,包括3 d的调整期、7 d的预试期和8 d的正试期,在正试期内测定甲烷产量、饲粮总能和营养物质表观消化率。结果表明:饲粮2的甲烷日排放量显著高于饲粮3和4(43.43 L/d vs.38.88和35.98 L/d;P0.05)。与饲粮1相比,饲粮2和3的每千克干物质采食量(DMI)甲烷排放量显著增加(38.00 L/kg DMI vs.42.24、41.69 L/kg DMI;P0.05),但是饲粮2、3和4之间差异不显著(P0.05)。随着NDF/NFC的降低,每千克可消化有机物(DOM)的甲烷排放量逐渐降低,饲粮4的每千克DOM的甲烷排放量显著低于饲粮1、2和3(58.78 L/kg DOM vs.75.00、73.35和64.11 L/kg DOM;P0.05)。随着NDF/NFC的降低,每千克中性洗涤纤维采食量(NDFI)或酸性洗涤纤维采食量(ADFI)的甲烷排放量逐渐增加,且各饲粮之间差异显著(P0.05)。综上所述,结合各营养物质表观消化率和甲烷排放效率,在维持水平下,采用NDF/NFC为1.04的玉米秸秆饲粮作为肉用绵羊甲烷减排的饲粮最合适。     

吉生羊草饲喂绵羊营养价值的评定

本研究采用消化试验,测定了6只东北细毛羊对吉生羊草的消化利用情况。结果表明:绵羊对吉生羊草的消化能为9.90MJ/kg,根据公式推算,代谢能为8.12MJ/kg。吉生羊草饲喂绵羊的消化率:干物质50.10%,有机物52.70%,粗蛋白63.40%,粗脂肪45.10%,粗纤维57.80%,无氮浸出物52.12%。     

近红外光谱分析技术在饲料质量监测上的应用

<正> 随着我国集约化饲养业及饲料工业的发展,配合饲料的质量愈来愈受到人们的重视。通常的饲料质量监测依靠传统的化学方法,该法既费时又繁琐。对样品均进行破坏性前处理,需熟练的操作人员及昂贵的化学试剂,难以实现快速监测饲料质量的目的。本世纪70年代兴起的农产品及饲料的有机成分的快速分析方法——近红外光谱(NIRS,下同)分析技术为快速监测饲料质量提供了新的手段。NIRS仪首先由美国农业部K.H.Norris开发。该法只需在测试前对样品进行粉碎,应用被测样品的定标软件,在近1分钟内可测出样品的成分含量。该技术具有快速、简便、相对准确等优点,已广泛应用于谷实类、油料籽实、粗饲料、食品等成分分析和质量监测等方面。     

日粮精料水平对绵羊瘤胃内容物亚油酸及其氢化产物组成的影响

选用4只装有永久性瘤胃瘘管的杂交一代(小尾寒羊×无角陶赛特)羯羊(平均体重45.0kg),采用4×4拉丁方设计,研究日粮精料水平对绵羊瘤胃内容物中亚油酸及其氢化产物组成的影响。在日粮中添加大豆油,调整粗脂肪和亚油酸含量分别至(7.3±0.1)%和(25.4±0.9)mg/g(干物质基础),日粮精料水平分别为30.2,39.3,48.8和57.7%(干物质基础)。试验结果表明,57.7%精料日粮组瘤胃内容物中亚油酸的含量(mg/gDM)及其在总18C脂肪酸中的比例最高(P<0.01),反11C18∶1、顺9,反11CLA的含量及二者在总18C脂肪酸中的比例最低(P<0.01);各组瘤胃内容物中C18∶0的含量及其在总18C脂肪酸中的比例没有显著差异(P>0.05)。瘤胃内容物中亚油酸、顺9,反11CLA和反11C18∶1的含量与日粮NDF水平呈极显著线性相关(P<0.01)。亚油酸的氢化效率随日粮精料水平的提高而显著降低(P<0.05)。     

用近红外反射光谱方法测定乳清粉的品质性状

本文用近红外反射光谱(NIRS)直接测定乳清粉的蛋白质、脂肪、灰份含量，均有很好的效果。同时对定标光谱进行不同的数学处理和散射处理，并采用不同的统计方法来优化这种成份的NIRS分析。建模时标准误很小而决定系数高。校正标准误、检验工作标准误、校正决定系数以及检验决定系数蛋白质依次分别为0．589，1．448，0．999和0．977，脂肪分别为0.257，0．570，0．989和0．938，灰份分别为0．178，0．206，0．990和0．988。同时对乳清粉品质的NIRS测定作了讨论。     

近红外光谱技术在兽药检测中的应用前景

综述了近红外光谱法的基本原理和发展,以及近红外光谱技术在药物检测中的应用现状,并对其在兽药检测中的应用前景进行展望.     

Near Infrared Reflectance Spectroscopy for the Determination of Free Gossypol in Cottonseed Meal

Gossypol is a toxic polyphenolic compound produced by the pigment glands of the cotton plant. The free gossypol content of cottonseed meal (CSM) is commonly determined by the American Oil Chemists’ Society (AOCS) wet chemistry method. The AOCS method, however, is laboratory-intensive, time-consuming, and therefore, not practical for quick field analyses. To determine if the free gossypol content of CSM could be predicted by near infrared reflectance spectroscopy (NIRS), CSM samples were collected from all over the world. All CSM samples were ground and a portion of each analyzed for free gossypol by the AOCS procedure (reference data) and by NIRS (reflectance data). Both reflectance and reference data were combined in a calibration. The coefficient of determination (r²) and standard error of prediction (SEP) were used to assess the calibration accuracy. The r² was 0.728, and the SEP was 0.034 for the initial calibration that included samples from all over the world. However, the r² and SEP improved to 0.921 and 0.014, respectively, if the calibration was made using CSM samples only from the United States. These results indicate that a general prediction equation can be developed to predict the free gossypol content of CSM by NIRS. From a practical standpoint, NIRS technology provides a method for quickly assessing whether a particular batch of CSM has a free gossypol content low enough to be suitable for use in poultry diets.     

利用近红外漫反射(NIR)技术快速检测多维饲料中维生素E的含量

本文尝试将近红外漫反射技术应用于预测多维预混料中维生素E的含量,并分析此方法的可行性。利用125个多维预混料小规模定标集进行定标,获得定标方程,并将20个多维预混料预测结果与高效液相色谱(HPLC)法的检测结果进行比较,结果表明用近红外漫反射技术预测多维预混料中维生素E含量可行。     

Prediction of Forage Energy Content by Near Infrared Reflectance Spectroscopy and Summative Equations

In livestock nutrition, summative models (SM) are displacing empirical models as a preferred method to predict energy content of forages. The objective of this study was to determine the effect of using near infrared reflectance spectroscopy (NIRS) to determine nutrient subcomponents required of SM on overall ability to predict energy content of corn and legume-grass silages. Corn (n = 90) and legume-grass (n = 70) silages were collected and analyzed for CP, ADF CP, NDF, NDF CP, in vitro (IV) digestible (d) NDF, ash, and fat by standard laboratory techniques. Samples were scanned on a Model 6500 NIRS, and calibration equations were developed for each nutrient. The TDN contents of corn and legume-grass silages were then estimated using a SM, where the model nutrients were determined by laboratory or NIRS methods. The predicted TDN content of corn and legume-grass silages was compared to IV d OM to assess overall utility. The NIRS calibrations were adequate (R² > 0.90) for CP and NDF for both corn and legume-grass silages with standard errors of calibration (SEC) < 0.55 for CP and < 1.09 for NDF. Near infrared calibrations for ADF CP, NDF CP, fat, and ash were less accurate in both corn and legume-grass silages with R² < 0.75. Calibrations for IV d NDF in corn and legume-grass silages had R² = 0.87 and 0.79, respectively, but possible co-dependency with NDF is speculated. The relationship between corn and legume-grass silage SM TDN and IV d OM was excellent when model nutrients were determined by laboratory procedures. The TDN estimates when NIRS was used to determine all SM nutrients were superior to older empirical models, but SM TDN estimates using NIRS-determined nutrients were less accurate as compared with SM TDN prediction when model nutrients were determined by laboratory procedures. In particular, using NIRS to predict IV d NDF and ash for use in SM lead to the greatest challenge in TDN prediction in both corn and legume-grass silages.     

利用近红外光谱法快速测定青贮玉米饲料中NDF与ADF含量

本试验应用近红外漫反射光谱(NIRS)技术,采用偏最小二乘回归法(PLS),在国内首次建立了适合不同品种、适配范围广的近红外漫反射光谱测定青贮玉米中性洗涤纤维(NDF)和酸性洗涤纤维(ADF)含量的稳定的校正模型。本试验选取132种青贮玉米样品,采用中心化+一阶导数+多元散射校正预处理方法,谱区为950～1650nm,建立了青贮玉米NDF和ADF校正模型。其校正决定系数(R2cal)分别达到0.9781和0.9905,交叉验证决定系数(R2val)分别为0.9745和0.9806,交叉验证误差(SECV)分别为1.55和1.03。因此,此模型可以用来快速准确的测定青贮玉米饲料中NDF和ADF的含量。     

近红外漫反射光谱定量分析天然牧草营养成分

为了快速测定内蒙古锡林郭勒盟草原天然牧草的营养成分,试验选用内蒙古锡林郭勒盟草原2016年5-11月份的主要牧草及混合牧草样品共407份,研究利用近红外漫反射全光谱扫描技术结合实验室检测数据,用修正偏最小二乘法（MPLS）,进行粗蛋白（CP）、中性洗涤纤维（NDF）、酸性洗涤纤维（ADF）、木质素（ADL）、粗灰分（Ash）、粗脂肪（EE）、钙（Ca）、磷（P）的定标和验证。结果表明：Ca、NDF、DM、CP、Ash的外部验证相对分析误差RPD（SD/SEP）均 > 3,NIRS预测值与化学值的相关系数RSQ均在0.9以上,说明这5个指标的定标效果较好, 建立的定标模型可以用于实际检测;ADF外部验证相对分析误差2.5     

近红外技术在牧草方面的应用

通过对近红外光谱法的基本原理、特点、近红外光谱仪的发展及其在国外牧草应用上的研究情况作一阐述,来推动近红外技术在我国牧草研究中的应用.     

Mid and Near Infrared Spectroscopy to Identify Illegal Treatments in Beef Cattle

Veterinary Research Communications -     

Fecal Near-Infrared Reflectance Spectroscopy to Predict Leymus chinensis of Diets From Penned Sheep in North China

Selective foraging among free-ranging herbivores can make measuring botanical composition of diets challenging. Using near-infrared reflectance spectroscopy (NIRS) on feces for predicting botanical components of individual animal diets is a novel method for studying diet selection. This study was conducted to determine the ability of fecal NIRS to predict the percentage of consumption of Leymus chinensis (Trin.) Tzvel., a dominant species in north China, by sheep (Ovis aries L.). The calibration data set consisted of 47 diets of known L. chinensis composition, paired with corresponding fecal spectra. These pairs were generated in a trial using restricted feeding. Validation pairs (n = 9) were collected in a similar trial that used ad libitum feeding. Derived coefficients of determination (R²) and standard error of calibration were 0.99% and 2.2% for partial least squares (PLS) regression and 0.89% and 7.3% for stepwise regression, respectively. Derived coefficients of determination (r²) and standard error of prediction were 0.78% and 4.8% for PLS regression and 0.90% and 3.2% for stepwise regression, respectively. PLS regression resulted in better calibration than stepwise regression, but when the calibration data set was small, stepwise regression improved the precision and accuracy of predictions compared with the PLS regression. Results of the present study show that a fecal NIRS equation developed from a restricted feeding trial could be used to predict the percentage of L. chinensis in fecal materials collected from voluntary feeding trials.