IN VITRO METHOD OF PREDICTING RUMEN DIGESTIBLE PROTEIN

    公开(公告)号:US20240255516A1

    公开(公告)日:2024-08-01

    申请号:US18561078

    申请日:2022-05-12

    CPC classification number: G01N33/6803 G01N2333/962

    Abstract: The present disclosure relates to in vitro methods of predicting rumen digestible protein of a feed sample. An in vitro method of predicting rumen digestible protein of a feed sample includes forming an aqueous test composition including the feed sample, a buffer, and one or more digestive enzymes. The method includes incubating the test composition. The method includes removing one or more test samples of the test composition at time points during the incubating, the time points including about 0 h and at least one time point after about 0 h. The method includes measuring soluble protein content of the one or more test samples. The method includes using the measured soluble protein content of the one or more test samples to predict a rate and/or extent of rumen protein digestion of the feed sample.

    MICROBIOME ANALYTICS SUCH AS FOR ANIMAL NUTRITION MANAGEMENT

    公开(公告)号:US20230298688A1

    公开(公告)日:2023-09-21

    申请号:US18000161

    申请日:2021-05-27

    CPC classification number: G16B20/00 G16B40/20 G16H50/20

    Abstract: A method of training a microbiota model engine to identify biomarkers for predicting food safety or animal growth includes obtaining data that is indicative of an assay of candidate biomarkers obtained the gastrointestinal tracts of a set of animals, where the assay is performed at specified intervals in the lifecycle of the animals and the animals manifest specified characteristics at the specified intervals. The method further includes training the microbiota model engine using the data to generate a prediction based on at least one of a food safety or an animal growth criterion and obtaining, from the trained microbiota model engine, a set of features used by the microbiota model engine to generate the prediction. The method additionally includes identifying a subset of biomarkers from amongst the candidate biomarkers from the set of features and providing the subset of biomarkers for generating food safety or animal growth predictions.

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