A method of analyzing feed comprising steps of: a) digesting a sample of animal feed in vitro using at least one enzyme to generate digested animal feed comprising at least one residual component, wherein the at least one residual component is selected from the group consisting of protein, phosphorous, fat, gross energy, carbohydrates, and fiber, wherein the animal feed composition is selected from the group consisting of Total Mixed Ration, corn, soybean, forage(s), grain(s), distiller grain(s), sprouted grains, legumes, vitamins, amino acids, minerals, molasses, fiber(s), fodder(s), grass(es), hay, straw, silage, kernel(s), leaves, and meal, wherein the sample of animal feed is digested with pepsin followed by digestion with pancreatin at a pH of at least 6, and wherein digesting a sample of animal feed further comprises separating the digested animal feed into a solid component and a liquid component; b) scanning the solid component of the digested animal feed using NIR spectroscopy to generate spectral data; c) comparing the spectral data generated from scanning the solid component of the digested animal feed to a computer model of the at least one residual component to generate a predicted concentration of the at least one residual component of the digested animal feed, wherein the spectral data is compared using a computer implemented method comprising receiving spectral data from the digested sample and comparing the spectral data to the computer model of the residual component to obtain the predicted concentration of the at least one residual component, wherein the computer model is generated by receiving spectral data for each of a plurality of digested animal feed samples, relating the spectral data for each of the plurality of digested animal feed samples to the concentration of the at least one residual component as determined by analytical wet chemistry chemical methods in a corresponding sample of each of the plurality of digested animal feed samples, and establishing a predictive relationship based on the spectral data and the concentration of the at least one residual component in each of the plurality of digested animal feed samples; d) comparing the predicted concentration of the one or more residual compound to a predetermined nutritional profile of the feed component; and e) adjusting the animal feed composition to obtain the predetermined nutritional profile for the animal based on the predicted concentration of the at least one residual component of the digested animal feed. a) digesting a sample of animal feed in vitro using at least one enzyme to generate digested animal feed comprising at least one residual component, wherein the at least one residual component is selected from the group consisting of protein, phosphorous, fat, gross energy, carbohydrates, and fiber, wherein the animal feed composition is selected from the group consisting of Total Mixed Ration, corn, soybean, forage(s), grain(s), distiller grain(s), sprouted grains, legumes, vitamins, amino acids, minerals, molasses, fiber(s), fodder(s), grass(es), hay, straw, silage, kernel(s), leaves, and meal, wherein the sample of animal feed is digested with pepsin followed by digestion with pancreatin at a pH of at least 6, and wherein digesting a sample of animal feed further comprises separating the digested animal feed into a solid component and a liquid component; b) scanning the solid component of the digested animal feed using NIR spectroscopy to generate spectral data; c) comparing the spectral data generated from scanning the solid component of the digested animal feed to a computer model of the at least one residual component to generate a predicted concentration of the at least one residual component of the digested animal feed, wherein the spectral data is compared using a computer implemented method comprising receiving spectral data from the digested sample and comparing the spectral data to the computer model of the residual component to obtain the predicted concentration of the at least one residual component, wherein the computer model is generated by receiving spectral data for each of a plurality of digested animal feed samples, relating the spectral data for each of the plurality of digested animal feed samples to the concentration of the at least one residual component as determined by analytical wet chemistry chemical methods in a corresponding sample of each of the plurality of digested animal feed samples, and establishing a predictive relationship based on the spectral data and the concentration of the at least one residual component in each of the plurality of digested animal feed samples; d) comparing the predicted concentration of the one or more residual compound to a predetermined nutritional profile of the feed component; and e) adjusting the animal feed composition to obtain the predetermined nutritional profile for the animal based on the predicted concentration of the at least one residual component of the digested animal feed.