Utility of Near-Infrared Reflectance Spectroscopy to Predict Nutrient Composition and In Vitro Digestibility of Total Mixed Rations

Abstract

Total mixed ration (TMR) samples (n = 110) were analyzed for dry matter (DM), crude protein (CP), soluble CP, neutral detergent fiber (NDF), NDF CP, starch, ash, fat, total ethanol-soluble carbohydrate, and nonfiber carbohydrate (NFC). Rapidly and slowly degraded and undegraded in situ CP fractions and in vitro DM, organic matter, and NDF digestibility were determined on each TMR. The TMR were scanned using near-infrared reflectance spectroscopy (NIRS); spectra were retained with NIRS calibration and cross-validation statistics were determined using partial least squares regression methods. The CP, NDF, starch, in vitro DM, and in vitro indigestible NDF contents of TMR were predicted by NIRS with good degrees (R2 >0.85) of accuracy with proportionally low standard errors of prediction. Moderate utility of NIRS to predict the NFC (R2 = 0.83) and fat content (R2 = 0.81) of TMR was observed. Rapidly, slowly, and undegraded in situ CP fractions in TMR were not well predicted by NIRS. Similarly, soluble CP, NDF CP, total ethanol-soluble carbohydrate, and in vitro NDF digestibility (% of NDF) were not well predicted by NIRS. Ratios of nutrient range to reference laboratory method error were calculated and found to be positively related (R2 = 0.84) to NIRS predictability of a given TMR nutrient, suggesting some laboratory procedures were not precise enough to yield suitable NIRS predictions. Data suggest that NIRS has utility to predict basic nutrients such as CP, NDF, starch, NFC, and fat in TMR. However, difficulty was observed using NIRS in predicting key biological nutrients in TMR such as in situ CP fractions and in vitro NDF digestibility. Difficulty of NIRS in predicting these nutrients is related to the level of reference method error in relationship to the range of nutrient values in TMR, but other sources of prediction error may exist.

The development of near-infrared spectroscopy (NIRS) calibration for prediction of ash content in legumes on the basis of two different reference methods

“…NIRS calibration development must be based on widely-accepted analytical procedures, but these are frequently poorly-defined chemically and sometimes do not relate to spectroscopic data (Shenk & Westerhaus, 1994). Theoretically, inorganic substances do not absorb energy in the near infrared (NIR) region but some papers have demonstrated that NIRS can reliably predict the ash content of feed (Deaville & Flinn, 2000;Mentink, Hoffman, & Bauman, 2006;Osborne, 2001;Park, Agnew, Gordon, & Steen, 1998;Pérez-Marín, Garrido-Varo, Guerrero-Ginel, & Gómez-Cabrera, 2004). Since ash content cannot be directly measured by NIRS, it is assumed that it is predicted by correlation with the total amount of organic compounds and water present because of the large number of wavelengths used in the process of calibration development that give significant information (Clarke, Mayland, & Lamb, 1987;Frankhuizen, 2001;Garnsworthy, Wiseman, & Fegeros, 2000;Osborne, 2007).…”

The Role iNDF in the Regulation of Feed Intake and the Importance of Its Assessment in Subtropical Ruminant Systems (the Role of iNDF in the Regulation of Forage Intake)

“…These relationships have been used as predictors of various OM components [62,63] with universal calibration equations developed from large (>100) sample sets. The prediction accuracy is dependent on a significant and strong correlation (R 2 > 0.85) of the spectral data and wet chemistry [64]. Robust NIRS calibrations have been developed using largely C4 forages for the prediction for iNDF [65].…”

Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing

“…Recently, Mayer et al [14] suggested near infrared spectroscopy (NIRS) as an alternative technique to determine the BMP FM . NIRS is widely used in feed and food quality monitoring [19][20][21][22][23][24][25][26] and is accepted since many years as routine method for determining forage quality parameters of various crops. In the bioenergy sector NIRS has been applied to estimate sulphur content in biodiesel [27], for predicting digestibility of maize silage [28][29][30] and fermentation parameters of silages [31], and for the assessment of in situ degradability parameters of crude protein and dry matter characteristics [32,33].…”