Fernández-Ahumada Elvira, Garrido-Varo Ana, Guerrero-Ginel José Emilio
Department of Animal Production, ETSIAM, University of Córdoba, N-IV, Km. 396, Campus Rabanales, Edif. Producción Animal, 14014, Córdoba, Spain.
J Agric Food Chem. 2008 May 14;56(9):3185-92. doi: 10.1021/jf073534t. Epub 2008 Apr 12.
Near-infrared calibrations were developed for the instantaneous prediction of the chemical and ingredient composition of intact compound feeds. Two rather different instruments were compared (diode array vs grating monochromator). The grating monochromator was used in a static mode in the laboratory, whereas the diode-array instrumentbetter adapted to online analysiswas placed on a conveyor belt to simulate measurements at a feed mill plant. Modified partial least squares (MPLS) equations were developed using the same set of samples analyzed in the two instruments. Sample set 1 ( N = 398) was used to predict crude protein (CP) and crude fiber (CF), while sample set 2 ( N = 393) was used for the prediction of one macroingredient (sunflower meal, SFM) and one microingredient (mineral-vitamin premix, MVP). The standard error of cross-validation (SECV) and the coefficient of determination (R2) values for CF were better using the monochromator instrument. However, results obtained for CP, SFM, and MVP using the samples analyzed in the diode-array instrument showed similar or even greater accuracy than those obtained using samples analyzed in the grating monochromator. The excellent predictive ability [R2> 0.95; RPD (ratio of standard deviation to SECV) > 3] obtained for CP, CF, and SFM opens the way for the online use of NIRS diode-array instruments for surveillance and monitoring in the manufacture, processing, and marketing of compound feeds. R2, RPD, and SECV values for MVP showed similar performance for both instruments. Although RPD values did not reach the minimum recommended for quantitative analysis, results are encouraging for an ingredient present in feed compounds in such very low amounts.
已开发出近红外校准方法,用于即时预测完整复合饲料的化学和成分组成。比较了两种截然不同的仪器(二极管阵列仪与光栅单色仪)。光栅单色仪在实验室中以静态模式使用,而更适合在线分析的二极管阵列仪则放置在传送带上,以模拟饲料加工厂的测量。使用在两种仪器中分析的同一组样品建立了改进的偏最小二乘法(MPLS)方程。样本集1(N = 398)用于预测粗蛋白(CP)和粗纤维(CF),而样本集2(N = 393)用于预测一种常量成分(向日葵粕,SFM)和一种微量成分(矿物质-维生素预混料,MVP)。使用单色仪仪器时,CF的交叉验证标准误差(SECV)和决定系数(R2)值更好。然而,使用二极管阵列仪分析的样品获得的CP、SFM和MVP结果显示,其准确性与使用光栅单色仪分析的样品相似,甚至更高。CP、CF和SFM获得的出色预测能力[R2> 0.95;RPD(标准偏差与SECV之比)> 3]为近红外光谱二极管阵列仪在复合饲料制造、加工和销售中的在线监测和监控开辟了道路。两种仪器的MVP的R2值、RPD值和SECV值表现相似。尽管RPD值未达到定量分析推荐的最小值,但对于饲料化合物中含量极低的成分,结果令人鼓舞。