Feasibility Of Near Infrared Reflectance Spectroscopy Research Articles

Application of Near-Infrared Reflectance Spectroscopy for Predicting Chemical Composition of Feces in Holstein Dairy Cows and Calves.

Traditional methods for determining the chemical composition of cattle feces are uneconomical. In contrast, near-infrared reflectance spectroscopy (NIRS) has emerged as a successful technique for assessing chemical compositions. Therefore, in this study, the feasibility of NIRS in terms of predicting fecal chemical composition was explored. Cattle fecal samples were subjected to chemical analysis using conventional wet chemistry techniques and a NIRS spectrometer. The resulting fecal spectra were used to construct predictive equations to estimate the chemical composition of the feces in both cows and calves. The coefficients of determination for calibration (RSQ) were employed to evaluate the calibration of the predictive equations. Calibration results for cows (dry matter [DM], RSQ = 0.98; crude protein [CP], RSQ = 0.93; ether extract [EE], RSQ = 0.91; neutral detergent fiber [NDF], RSQ = 0.82; acid detergent fiber [ADF], RSQ = 0.89; ash, RSQ = 0.84) and calves (DM, RSQ = 0.92; CP, RSQ = 0.89; EE, RSQ = 0.77; NDF, RSQ = 0.76; ADF, RSQ = 0.92; ash, RSQ = 0.97) demonstrated that NIRS is a cost-effective and efficient alternative for assessing the chemical composition of dairy cattle feces. This provides a new method for rapidly predicting fecal chemical content in cows and calves.

Studies on 5 Protein Fractions Prediction of Forage Legume Mixture by NIRS

This study was conducted to assess the feasibility of near-infrared reflectance spectroscopy (NIRS) as a rapid and reliable method for the estimation of crude protein (CP) fractions in forage legume mixtures (sudangrass and pea mixture, and kidney bean and potato mixture). A total of 178 samples were collected and their spectral reflectance obtained in the range of 400~2,500 nm. Of these, 50 samples were selected for calibration and validation, and 35 samples were used for calibration of the data set, and the modified partial least square regression (MPLSR) analysis was performed. The correlation coefficient (<TEX>$r^2$</TEX>) and the standard error of cross-validation (SECV) of the calibration models in the CP fractions, A, B1, B2, B3, and C, were 0.94 (1.05), 0.92 (0.74), 0.96 (0.95), 0.91 (0.42), and 0.83 (0.38), respectively. Fifteen samples were used for equation validation, and the <TEX>$r^2$</TEX> and the standard error of prediction (SEP) were 0.87 (1.45), 0.91 (0.49), 0.94 (1.13), 0.36 (0.96), and 0.74 (0.67), respectively. This study showed that NIRS could be an effective tool for the rapid and precise estimation of CP fractions in forage legume mixtures.

Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS)

The aim of this study was to assess the feasibility of near-infrared reflectance spectroscopy (NIRS) for predicting lamb meat fatty acid composition. We compared ground vs. intact non-ground meat samples to determine whether grinding and homogenisation of meat samples improved the performance of the predictions. We used 76 male lambs, of which 32 were pasture-fed and 44 stall-fed with concentrate and hay. The reflectance spectrum of Longissimus lumborum muscle was measured at wavelengths between 400 and 2500nm. Predictions were better with ground than with intact muscle samples. NIRS accurately predicts several individual fatty acids (FA) (16:0, 18:0, 16:1 Δ9 cis, 17:1 Δ9 cis, 18:1 Δ9 cis, 18:1 Δ11 cis and 16:1 Δ9 trans) and several FA groups (total linear saturated FA, total branched saturated FA, total saturated FA, total cis monounsaturated FA (MUFA), total trans MUFA, total MUFA and total polyunsaturated PUFA). These results show the potential of NIRS as a rapid, and convenient tool to predict the major FA in lamb meat.

Rapid Analysis of Layer Manure Using Near-Infrared Reflectance Spectroscopy

Diverse samples (n = 91) were used to investigate the feasibility of near-infrared reflectance spectroscopy (NIRS) technology for rapid analysis of the nutrient composition of layer manure. Near-infrared reflectance spectroscopy calibration models for moisture, organic matter, total N, ammonium N, total P, total K, Cu, Fe, Mg, and Na were developed by using the modified partial least squares method. Results showed that the NIRS method could provide accurate predictions of moisture, organic matter, total N, and ammonium N concentrations, with a correlation coefficient of validation (rv2) and a ratio of SD over the root mean square error of prediction (RPD) of 0.86 (2.68), 0.89 (2.91), 0.88 (2.75), and 0.88 (2.62), respectively. Total P (rv2 = 0.80, RPD = 2.01) could be approximately determined by NIRS. It was difficult to determine total K (rv2 = 0.58, RPD = 1.51), Cu (rv2 = 0.48, RPD = 1.38), Fe (rv2 = 0.55, RPD = 1.47), Mg (rv2 = 0.60, RPD = 1.51), and Na (rv2 = 0.62, RPD = 1.87) by NIRS.