Belanche A, Weisbjerg M R, Allison G G, Newbold C J, Moorby J M
Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, SY23 3EE, Aberystwyth, United Kingdom.
J Dairy Sci. 2013;96(12):7867-80. doi: 10.3168/jds.2013-7127. Epub 2013 Oct 4.
Currently, rapid methods are needed for feed analysis. This study examined the potential of Fourier-transform infrared (FTIR) spectroscopy to predict the nutritional value of a wide range of feeds for ruminants, as an alternative to the in situ technique. Moreover, we investigated whether universal equations could be developed that would allow the low-cost determination of crude protein (CP) concentrations and their kinetics of degradation into the rumen. Protein nutritional values of 663 samples comprising 80 different feed types were determined in terms of concentrations of CP, water-soluble CP (CP(WS)), total-tract mobile bag CP digestibility (CP(TTD)), and in situ CP degradability, including the rumen soluble fraction (CP(A)), the degradable but not soluble fraction (CP(B)), rate of CP(B) degradation (CP(C)), effective degradability (CP(ED)), and potential degradability (CPPD). Infrared spectra of dry samples were collected by attenuated total reflectance from 4000 to 600 cm(-1). Models were developed by partial least squares (PLS) regression in a randomly selected subset of samples, and the precision of the equations was confirmed by using an external validation set. Analysis by FTIR spectroscopy was sufficiently sensitive to allow the accurate prediction of sample CP concentration (R(2)=0.92) and to classify feeds according to their CPWS concentrations using universal models (R(2)=0.78) that included all sample types. Moreover, substantial improvements in predictions were observed when samples were subdivided in groups. Models for forages led to accurate predictions of CP(WS) and fractions CP(A) and CP(B) (R(2)>0.83), whereas models for CP(TTD) and CP(ED) could be used for screening purposes (R(2)>0.67). This study showed that models for protein-rich concentrates alone could also be used for screening according to the feed concentrations of CP(WS), CP(TTD), CP(ED), CP(A), and CP(B), but models for energy-rich concentrates gave relatively poor predictions. The general difficulty observed in predicting CP(C) is because of a low correlation between FTIR spectra and the kinetics of CP degradation, which may be the result of large variation in the reference method (i.e., in situ degradation studies) and perhaps also because of the presence of compounds that can modify the CP degradation pattern in the rumen. In conclusion, FTIR spectroscopy should be considered as a low-cost alternative in the feed evaluation industry.
目前,饲料分析需要快速方法。本研究考察了傅里叶变换红外(FTIR)光谱法作为原位技术的替代方法,预测反刍动物多种饲料营养价值的潜力。此外,我们研究了是否可以开发通用方程,以便低成本测定粗蛋白(CP)浓度及其在瘤胃中的降解动力学。根据CP、水溶性CP(CP(WS))、全消化道移动袋CP消化率(CP(TTD))和原位CP降解率,包括瘤胃可溶部分(CP(A))、可降解但不溶部分(CP(B))、CP(B)降解率(CP(C))、有效降解率(CP(ED))和潜在降解率(CPPD),测定了包含80种不同饲料类型的663个样品的蛋白质营养价值。通过衰减全反射收集干燥样品在4000至600 cm(-1)范围内的红外光谱。通过偏最小二乘法(PLS)回归在随机选择的样品子集中建立模型,并使用外部验证集确认方程的精度。FTIR光谱分析足够灵敏,能够准确预测样品CP浓度(R(2)=0.92),并使用包括所有样品类型的通用模型(R(2)=0.78)根据CPWS浓度对饲料进行分类。此外,将样品分组后,预测有显著改善。牧草模型能够准确预测CP(WS)以及CP(A)和CP(B)部分(R(2)>0.83),而CP(TTD)和CP(ED)模型可用于筛选目的(R(2)>0.67)。本研究表明,仅富含蛋白质的浓缩饲料模型也可用于根据CP(WS)、CP(TTD)、CP(ED)、CP(A)和CP(B)的饲料浓度进行筛选,但富含能量的浓缩饲料模型预测效果相对较差。预测CP(C)时普遍存在的困难是由于FTIR光谱与CP降解动力学之间的相关性较低,这可能是由于参考方法(即原位降解研究)变化较大,也可能是由于存在能够改变瘤胃中CP降解模式的化合物。总之,FTIR光谱法应被视为饲料评估行业中一种低成本的替代方法。