Carbohydrates in forage crops can be divided into neutral detergent-insoluble fiber and neutral detergent-soluble carbohydrates (NDSC); the latter includes organic acids (OA), total ethanol:water-soluble carbohydrates (TESC), starch, and neutral detergent-soluble fiber (NDSF). The accurate and efficient estimation of NDSC in forage crops is essential for improving the performance of dairy cattle. In the present study, visible and near-infrared reflectance spectroscopy (NIRS) were applied to evaluate the feasibility of predicting OA, TESC, starch, NDSF, NDSC, and all related constituents used to calculate these 5 carbohydrate fractions in timothy and alfalfa. Forage samples (n = 1,008) of timothy and alfalfa were taken at the first and second harvests at 2 sites in 2007; samples were dried, ground, and then scanned (400 to 2,500 nm) using an NIRSystems 6500 monochromator. A calibration (n = 60) and a validation (n = 15) set of samples were selected for each species and then chemically analyzed. Concentrations of TESC and NDSC in timothy, as well as starch in alfalfa, were successfully predicted, but many other carbohydrate fractions were not predicted accurately when calibrations were performed using single-species sample sets. Both sets of samples were combined to form new calibration (n = 120) and validation (n = 30) sets of alfalfa and timothy samples. Calibration and validation statistics for the combined sets of alfalfa and timothy samples indicated that TESC, starch, and NDSC were predicted successfully, with coefficients of determination of prediction of 0.92, 0.89, and 0.93, and a ratio of prediction to deviation (RPD) of 3.3, 3.1, and 3.6, respectively. The NDSF prediction was classified as moderately successful The NIRS prediction of OA was unsuccessful All related constituents were predicted successfully by NIRS except ethanol-insoluble residual OM, with Our results confirm the feasibility of using NIRS to predict NDSC, its fractions, and other related constituents, except for OA and ethanol-insoluble residual OM, in timothy and alfalfa forage samples.

The object of this study was to explore the potential for support vector machine (SVM) to improve the precision of predicting protein fractions by near infrared reflectance spectroscopy (NIRS). Generally, most protein fractions determined in Cornell Net Carbohydrate and Protein System (CNCPS), especially the neutral detergent insoluble protein (NDFCP) and acid detergent insoluble protein (ADFCP), could not be accurately predicted by the commonly used partial least squares (PLS) method. A recently developed chemometric method, SVM, was applied in NIRS prediction of alfalfa protein fractions in this study. Two hundred thirty alfalfa samples were scanned on a near infrared reflectance spectrophotometer, and analyzed for crude protein (CP), true protein precipitated in tungstic acid (TCP), borate-phosphate buffer-insoluble protein (BICP), NDFCP, and ADFCP. These 5 laboratory proteins and the CNCPS protein fractions A, B1, B2, B3, and C were predicted by NIRS using the PLS and SVM methods. According to PLS-NIRS regression, CP, TCP, BICP, A, and B2 obtained the determination coefficient of prediction (R(p)(2)) of 0.96, 0.91, 0.94, 0.94, and 0.93, and the ratios of standard deviation of prediction samples: standard error of prediction samples (RPD) values were 5.07, 3.31, 3.98, 3.96, and 3.91. Neutral detergent insoluble protein, ADFCP (fraction C), B1, and B3 were predicted with R(p)(2) of 0.75, 0.83, 0.30, and 0.62, and RPD values of 1.98, 2.42, 1.20, and 1.62; Calibrated by the SVM-NIRS method, R(p)(2) values of CP, TCP, BICP, NDFCP, ADFCP(C), A, and B2 achieved 0.99, 0.97, 0.97, 0.90, 0.93, 0.97, and 0.97, respectively. The RPD values of those fractions were 8.68, 8.26, 6.11, 3.08, 3.69, 5.97, and 5.81, respectively. The R(p)(2) and RPD values of fractions B1 and B3 were 2.67 and 0.87 (B1) and 2.51 and 0.75 (B3) directly predicted by SVM-NIRS model. In this study, the chemical analysis results of B1 and B3 were also correlated with calculated results from TCP-BICP and NDFCP-ADFCP, which were predicted by SVM-NIRS models. The B1 protein fraction achieved R(p)(2) and RPD values of 0.87 and 3.61, whereas values for B2 were 0.75 and 2.00. Data suggested that use of SVM methods in NIRS technology could improve the accuracy of predicting protein fractions. This study showed the potential of increasing the NIRS prediction accuracy to a level of practical use for all protein fractions, except B3.

The mineral concentration of forage grasses plays a significant role in 2 metabolic disorders in dairy cattle production, namely, hypocalcemia (milk fever) and hypomagnesemia (grass tetany). Risks of occurrence of these 2 metabolic disorders can be evaluated by determining the dietary cation-anion difference (DCAD) and the grass tetany (GT) index of forages and specific rations. The objective of this study was to evaluate the feasibility of predicting timothy (Phleum pratense L.) mineral concentrations of Na, K, Ca, Mg, Cl, S, and P, the DCAD, and the GT index by near-infrared reflectance spectroscopy (NIRS). Timothy samples (n = 1,108) were scanned using NIRS and analyzed for the concentration of 7 mineral elements. Calculations of the DCAD were made using 3 different formulas, and the GT index was also calculated. Samples were divided into calibration (n = 240) and validation (n = 868) sets. The calibration, cross-validation, and prediction for mineral concentrations, the DCAD, and the GT index were performed using modified partial least squares regression. Concentrations of K, Ca, Mg, Cl, and P were successfully predicted with coefficients of determination of prediction (R(P)2) of 0.69 to 0.92 and coefficients of variation of prediction (CV(P)) ranging from 6.6 to 11.4%. The prediction of Na and S concentrations failed, with respective R(P)2 of 0.58 and 0.53 and CV(P) of 82.2 and 12.9%. The 3 calculated DCAD and the GT index were predicted successfully, with R(P)2 >0.90 and CV(P) <20%. Our results confirm the feasibility of using NIRS to predict K, Ca, Mg, and Cl concentrations, as well as the DCAD and the GT index, in timothy.

, J. 2009. Carbohydrates in alfalfa-timothy mixtures predicted with near infrared reflectance spectroscopy equations developed for single species. Can. J. Anim. Sci. 89: 279Á283. Our objective was to evaluate the feasibility of using near infrared reflectance spectroscopy (NIRS) equations previously developed with a calibration set that included samples of both timothy and alfalfa to predict carbohydrate fractions in mixed samples of both species. Timothy and alfalfa mixed samples were prepared with the alfalfa proportion ranging from 0 to 100%, with increments of 4%. With previously developed NIRS equations based on samples of single species of timothy and alfalfa, concentrations of total ethanol soluble carbohydrates (TESC), starch, and neutral detergent soluble carbohydrates (NDSC) of the mixed samples were predicted successfully, but concentrations of organic acids (OA) and neutral detergent soluble fiber (NDSF) were unsuccessfully predicted. Adding 13 mixed samples to the initial calibration set of around 110 samples of pure timothy and alfalfa samples improved the accuracy of already successful predictions for TESC, starch, and NDSC, and resulted in a successful prediction for NDSF in timothy and alfalfa mixtures.Key words: Near infrared reflectance spectroscopy, sugars, Phleum pratense, Medicago sativa Nie, Z., Tremblay, G. F., Be´langer, G., Berthiaume, R., Castonguay, Y., Bertrand, A., Michaud, R., Allard, G. et Han, J. 2009. Glucides des associations de luzerne et de fle´ole des pre´s pre´dits par spectroscopie dans le proche infrarouge avec des e´quations de´veloppe´es pour les espe`ces pures. Can. J. Anim. Sci. 89: 279Á283. Notre objectif e´tait d'e´valuer la faisabilited 'utiliser des e´quations NIRS de´veloppe´es avec des e´chantillons purs de fle´ole des pre´s et de luzerne pour pre´dire les fractions glucidiques dans des associations des deux espe`ces. Des e´chantillons de fle´ole des pre´s et de luzerne ont e´teḿ e´lange´s afin que la proportion de luzerne dans l'association varie entre 0 et 100% avec des paliers d'augmentation de 4%.

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