A method of developing a computer model for analyzing feed comprising steps of: (a) digesting in vitro a plurality of samples of animal feed each having an animal feed composition to generate a plurality of digested animal feed samples, wherein each of the plurality of digested animal feed samples comprises at least one residual component, wherein the at least one residual component is selected from the group consisting of protein, phosphorous, fat, gross energy and carbohydrates, wherein each animal feed composition comprises corn, soybean, grain(s), distiller grain(s), legumes, or molasses, wherein digesting comprises two phases, wherein a phase is conducted at a pH of less than 7 and includes use of at least one enzyme comprising pepsin, and wherein another phase is conducted at a pH of at least 6 and includes at least one enzyme comprising pancreatin; (b) separating each of the plurality of digested animal feed samples obtained in step (a) into a solid component and a liquid component, (c) scanning the solid component of each of the plurality of digested animal feed samples obtained in step (b) using NIR spectroscopy to generate spectral data for each of the plurality of digested animal feed samples; (d) determining the concentration of the at least one residual component in each of the plurality of digested animal feed samples using a wet chemistry method; and (e) generating a computer model by establishing a predictive relationship between the concentration of the at least one residual component of each of the plurality of digested animal feed samples to the spectral data of a corresponding sample of the plurality of digested animal feed samples, wherein generating a computer model comprises a computer implemented method comprising steps of: i) receiving spectral data for each of the plurality of digested animal feed samples; ii) relating the spectral data for each of the plurality of digested animal feed samples to the concentration of the at least one residual component in a corresponding sample of the plurality of digested animal feed samples; and iii) establishing a predictive relationship based on the spectral data and the concentration of the at least one residual component of the plurality of digested animal feed samples to generate the computer model. (a) digesting in vitro a plurality of samples of animal feed each having an animal feed composition to generate a plurality of digested animal feed samples, wherein each of the plurality of digested animal feed samples comprises at least one residual component, wherein the at least one residual component is selected from the group consisting of protein, phosphorous, fat, gross energy and carbohydrates, wherein each animal feed composition comprises corn, soybean, grain(s), distiller grain(s), legumes, or molasses, wherein digesting comprises two phases, wherein a phase is conducted at a pH of less than 7 and includes use of at least one enzyme comprising pepsin, and wherein another phase is conducted at a pH of at least 6 and includes at least one enzyme comprising pancreatin; (b) separating each of the plurality of digested animal feed samples obtained in step (a) into a solid component and a liquid component, (c) scanning the solid component of each of the plurality of digested animal feed samples obtained in step (b) using NIR spectroscopy to generate spectral data for each of the plurality of digested animal feed samples; (d) determining the concentration of the at least one residual component in each of the plurality of digested animal feed samples using a wet chemistry method; and (e) generating a computer model by establishing a predictive relationship between the concentration of the at least one residual component of each of the plurality of digested animal feed samples to the spectral data of a corresponding sample of the plurality of digested animal feed samples, wherein generating a computer model comprises a computer implemented method comprising steps of: i) receiving spectral data for each of the plurality of digested animal feed samples; ii) relating the spectral data for each of the plurality of digested animal feed samples to the concentration of the at least one residual component in a corresponding sample of the plurality of digested animal feed samples; and iii) establishing a predictive relationship based on the spectral data and the concentration of the at least one residual component of the plurality of digested animal feed samples to generate the computer model. i) receiving spectral data for each of the plurality of digested animal feed samples; ii) relating the spectral data for each of the plurality of digested animal feed samples to the concentration of the at least one residual component in a corresponding sample of the plurality of digested animal feed samples; and iii) establishing a predictive relationship based on the spectral data and the concentration of the at least one residual component of the plurality of digested animal feed samples to generate the computer model.