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Factors Affecting Growth Performance, Carcass Characteristics, Fatty Acid Profile Variation, and Iodine Value Prediction of Growing-Finishing Pigs

机译:影响成年猪生长性能,Characteristics体特性,脂肪酸谱变化和碘值预测的因素

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摘要

There were three objectives for this study: to quantify the impact of diet, sex, season, and market pull on fatty acid composition and iodine value of grow finish pigs. Develop equations for rapid and accurate prediction of iodine values of pork carcass to assess pork fat quality. Predict iodine value of two relevant adipose depots in growing-finishing pigs by near infrared reflectance spectroscopy to assess pork fat quality. A single experiment was conducted in a RCBD with two replications, utilizing crossbred pigs (n =480; initial BW = 48.6 +/- 6.7 kg) which were blocked by BW and sex. Treatments arranged in a 2 x 2 factorial with dietary fat source (TA and CO), and level of RAC inclusion (0 vs 10 mg/kg). Each treatment group consisted of 12 pens (6 barrow pens and 6 gilt pens) with 5 pigs per pen per replication. Pigs were harvested in 1 of 3 marketing pulls (d 56, 64, 76), in which the four heaviest pens per each treatment were removed. Fat tissue samples removed from the belly (posterior to the sternum, ventral edge of belly) and backfat (at the adjacent area of the first lumbar vertebra). Fatty acid profile was determined via gas chromatography (GC) and near infrared (NIR) spectroscopy. Chapter 1 observed that dietary fat supplementation did not affect (P > 0.05) live growth performance or carcass characteristics, while dietary RAC inclusion increased final BW, ADG, G:F, LMA, HCW, and FFLP. Total fatty acid (FA) concentrations of saturated FA (SFA), monounsaturated FA (MUFA), and MUFA:PUFA were higher (P < 0.01) in belly and back fat from pigs fed TA. Total polyunsaturated FA (PUFA) and the UFA:SFA concentrations in belly and back fat were higher (P 0.01) iodine value (IV) of belly and back fat depots at a greater magnitude then TA. Sex, season, and market pull also affected (P < 0.05) the concentrations of total SFA, PUFA, UFA:SFA and IV from belly and back fat depots. Increasing the degree of unsaturation of supplemental dietary fats negatively affected fat quality of growing-finishing pigs; while supplemental fat had the greatest effect on fat quality, the additional factors of sex, season, and market pull impacted fat quality in some measure. Single variable regression models utilizing dietary IVP (Loin BF: R2 = 0.84; Belly: R 2= 0.79) and C18:2, (Loin BF: R2 = 0.90; Belly: R 2= 0.87) had the highest coefficient of determination to predict iodine value of loin BF and belly fat sample sites. The inclusion of other live growth performance and carcass characteristic measures resulted in only a marginal improvement in R2. Pearson Correlation Coefficients between GC and NIR for IV between loin BF (r = 0.93; P < 0.001) and belly (r = 0.92; P< 0.001) fat depots, were highly correlated between determination methods. Similarly, Pearson Correlation Coefficients between loin BF and belly sample site locations for IV determined by GC (r = 0.88; P < 0.001) and NIR (r = 0.88; P < 0.001) were highly correlated suggest either location would be suitable for predicting IV. NIR calibration statistics for fatty acid proportions amongst fat depots were quite variable with R2 values ranging from 0.26 -- 0.96. Difference between fat depots was the largest contributor to the variation in the coefficient of determination, belly fat had comparatively lower R2 values and Ratio of Performance to Deviation (RPD) values generally < 1 when compared to loin BF. NIR calibration models were however, able accurately and precisely predict major FA constituents known to influence pork fat quality; C18:2n6, (Loin BF: R2 = 0.96, RPD = 5.31; Belly: R2= 0.91, RPD = 3.32), PUFA (Loin BF: R2 = 0.95, RPD = 4.68; Belly: R2= 0.87, RPD = 2.77), and IV (Loin BF: R2 = 0.93, RPD = 3.87; Belly: R2= 0.89, RPD = 3.04). The accuracy of NIR standard curves for IV loin BF and belly fat samples proved that NIR could be used to screen pork carcass fat quality and provide a rapid, cheap, non-destructive alternative to traditional IV determination methods of GC or titration.
机译:这项研究有三个目标:量化日粮,性别,季节和市场需求对成年猪的脂肪酸组成和碘值的影响。制定方程式,以快速准确地预测猪cas体的碘值,以评估猪肉脂肪质量。通过近红外反射光谱法预测生长肥育猪中两个相关脂肪库的碘值,以评估猪肉的脂肪质量。在RCBD中进行了一次实验,重复两次,使用了被体重和性别限制的杂交猪(n = 480;初始体重= 48.6 +/- 6.7 kg)。治疗方法按2 x 2分解因子进行安排,其中膳食脂肪来源(TA和CO)以及RAC的含量(0 vs 10 mg / kg)。每个治疗组由12头猪圈(6头猪圈笔和6头金猪圈)组成,每只猪每只重复猪5头。在3次市场推广中的1次(第56、64、76天)中收获了猪,其中每次处理去除了四个最重的围栏。从腹部(胸骨后方,腹部腹侧边缘)和后脂肪(在第一个腰椎的相邻区域)中取出脂肪组织样本。通过气相色谱(GC)和近红外(NIR)光谱测定脂肪酸谱。第1章观察到,膳食脂肪补充剂不会影响(P> 0.05)活的生长性能或car体特性,而膳食RAC的加入会增加最终的体重,ADG,G:F,LMA,HCW和FFLP。饲喂TA的猪的腹部和背部脂肪中的饱和FA(SFA),单不饱和FA(MUFA)和MUFA:PUFA的总脂肪酸(FA)浓度较高(P <0.01)。腹部和背部脂肪库中的总多不饱和脂肪酸(PUFA)和UFA:SFA浓度高于(TA),腹部和背部脂肪库中的碘值(IV)更高(P 0.01)。性别,季节和市场吸引力也影响(P <0.05)腹部和背部脂肪仓库中总SFA,PUFA,UFA:SFA和IV的浓度。增加膳食补充脂肪的不饱和度会对生长肥育猪的脂肪质量产生负面影响;补充脂肪对脂肪质量的影响最大,而性别,季节和市场吸引力等其他因素在一定程度上影响了脂肪质量。利用膳食IVP(腰部BF:R2 = 0.84;腹部:R 2 = 0.79)和C18:2(腰部BF:R2 = 0.90;腹部:R 2 = 0.87)的单变量回归模型具有最高的预测预测系数腰部BF和腹部脂肪样本部位的碘值。包括其他活体生长表现和car体特征指标在内,仅导致R2的少量改善。腰肉BF(r = 0.93; P <0.001)和腹部(r = 0.92; P <0.001)的脂肪库之间IV的GC和NIR之间的皮尔逊相关系数在测定方法之间高度相关。同样,通过GC(r = 0.88; P <0.001)和NIR(r = 0.88; P <0.001)确定的腰部BF和腹部样品部位IV之间的皮尔逊相关系数高度相关,表明这两个位置均适合于预测IV 。 NIR校准的脂肪库中脂肪酸比例的统计数据变化很大,R2值在0.26-0.96之间。脂肪库之间的差异是测定系数变化的最大原因,与腰部BF相比,腹部脂肪的R2值相对较低,性能偏差比(RPD)值通常<1。但是,NIR校准模型能够准确,准确地预测已知会影响猪肉脂肪质量的主要脂肪酸成分。 C18:2n6,(腰部BF:R2 = 0.96,RPD = 5.31;腹部:R2 = 0.91,RPD = 3.32),PUFA(腰部BF:R2 = 0.95,RPD = 4.68;腹部:R2 = 0.87,RPD = 2.77)和IV(腰部BF:R2 = 0.93,RPD = 3.87;腹部:R2 = 0.89,RPD = 3.04)。静脉内腰肉BF和腹部脂肪样品的NIR标准曲线的准确性证明,NIR可用于筛选猪car体脂肪质量,并提供一种快速,廉价,无损的替代方法来替代传统的IV气相色谱或滴定测定方法。

著录项

  • 作者

    Tennant, Travis Clay.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Animal sciences.;Agriculture.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 173 p.
  • 总页数 173
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:54:30

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