CN-122024916-A - Microbial fermentation culture medium optimization method for agricultural feed production

CN122024916ACN 122024916 ACN122024916 ACN 122024916ACN-122024916-A

Abstract

The application provides a microbial fermentation medium optimization method for agricultural feed production, which comprises the steps of grouping and calibrating strain metabolism paths according to a potential decomposition demand list to obtain an optimized metabolism path set, screening nutrition element combinations from a resource information base through the optimized metabolism path set to determine element proportion configuration to obtain an initial medium formula draft, predicting strain growth states according to the initial medium formula draft by a prediction simulation method, iteratively adjusting element proportions if predicted growth activity is insufficient to obtain a refined medium formula, optimizing strain culture condition parameters according to the adaptability enhancement scheme by a machine learning optimization method to obtain stable culture condition settings, generating strain culture protocol sequences through the stable culture condition settings, and determining subsequent raw material decomposition efficiency indexes to obtain an integral fermentation optimization path.

Inventors

ZHAO HANSHENG
WANG ZEGUANG
LI SHANSHAN

Assignees

山东肽元生物技术有限公司

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (8)

1. A method for optimizing a microbial fermentation medium for agricultural feed production, comprising: Acquiring feed raw material composition data and microorganism strain metabolism characteristic parameters, and determining a preliminary matching degree index by comparing the feed raw material composition data with the metabolism characteristic parameters to obtain a potential decomposition demand list of strains on raw materials; grouping and calibrating strain metabolism paths according to the potential decomposition demand list to obtain an optimized metabolism path set; screening nutritional element combinations from a resource information base through the optimized metabolic path set, and determining element proportion configuration to obtain an initial culture medium formula draft; Predicting the growth state of strains by adopting a prediction simulation method aiming at the initial culture medium formula draft, and iteratively adjusting the element proportion if the predicted growth activity is insufficient to obtain a refined culture medium formula; Extracting key nutrition element characteristics from the refined culture medium formula, and determining fermentation environment adaptability indexes by comparing the key nutrition element characteristics with historical fermentation data to obtain an adaptability enhancing scheme; Optimizing strain culture condition parameters by adopting a machine learning optimization method according to the adaptability enhancing scheme to obtain stable culture condition settings; and setting and generating a strain culture protocol sequence through the stable culture condition, and determining a subsequent raw material decomposition efficiency index to obtain an integral fermentation optimization path.
2. The method for optimizing a microbial fermentation medium for agricultural feed production according to claim 1, wherein the step of determining a preliminary matching degree index by comparing the feed raw material composition data with the metabolic characteristic parameters to obtain a list of potential decomposition demands of strains on raw materials comprises: Extracting metabolic characteristic parameters of the microbial strains from a preset database, comparing each component in feed raw material composition data with the metabolic characteristic parameters one by one, calculating the matching degree between each component and the metabolic characteristic parameters, generating a preliminary matching degree index, marking the corresponding component as a high-decomposition demand component if the preliminary matching degree index is lower than a preset threshold, and summarizing all the high-decomposition demand components to form a potential decomposition demand list of the strains on the raw materials.
3. The method for optimizing microbial fermentation media for agricultural feed production according to claim 1, wherein the grouping and calibrating the bacterial species metabolic pathways according to the list of potential decomposition requirements to obtain an optimized metabolic pathway set comprises: Grouping strain metabolic paths corresponding to the potential decomposition demand list by adopting a grouping algorithm to obtain a plurality of metabolic path groups, judging path deviation degree in each metabolic path group, adjusting corresponding path parameters to reduce deviation if the deviation degree exceeds a preset range, and calibrating all metabolic path groups by multiple times to obtain an optimized metabolic path set.
4. The method for optimizing a microbial fermentation medium for agricultural feed production according to claim 1, wherein the steps of screening a combination of nutrient elements from a resource information base through the optimized metabolic path set, determining element proportion configuration, and obtaining an initial medium formula draft include: And acquiring various nutrient element data in the resource information base, screening nutrient elements conforming to the metabolic paths according to the optimized metabolic path set, combining the screened nutrient elements, calculating the proportion relation among the elements, determining element proportion configuration, and generating an initial culture medium formula draft.
5. The method for optimizing a microbial fermentation medium for agricultural feed production according to claim 1, wherein the predicting simulation method is adopted for predicting the growth state of the strain for the initial medium formula draft, and if the predicted growth activity is insufficient, the element proportion is iteratively adjusted to obtain a refined medium formula, comprising: Inputting an initial culture medium formula draft to a metabolic model to perform operation simulation, obtaining a strain growth state prediction result, judging whether the predicted growth activity reaches a preset standard, if the predicted growth activity is insufficient, adjusting the element proportion, then inputting the metabolic model again to perform simulation, and obtaining a refined culture medium formula through repeated iterative adjustment until the predicted growth activity meets the standard.
6. The method of claim 1, wherein the extracting key nutrient features from the refined medium formulation, determining a fermentation environment suitability index by comparing the key nutrient features with historical fermentation data, and obtaining a suitability enhancement scheme comprises: And extracting key nutrition element characteristics in the refined culture medium formula, acquiring an environment adaptation record in the historical fermentation data, comparing the key nutrition element characteristics with corresponding characteristics in the historical fermentation data, calculating fermentation environment adaptation indexes, and generating targeted adjustment measures according to the adaptation indexes to form an adaptation enhancement scheme.
7. The method for optimizing a microbial fermentation medium for agricultural feed production according to claim 1, wherein the optimizing the strain culture condition parameters according to the adaptability enhancing scheme by a machine learning optimization method to obtain a stable culture condition setting comprises: Inputting adaptability enhancing scheme data to a machine learning model for training, outputting a culture condition parameter adjusting value, judging whether the adjusting value causes metabolic direction deviation, rolling back to a previous parameter set if the metabolic direction deviation is caused, continuing training until the adjusting value does not cause metabolic direction deviation, and obtaining stable culture condition setting.
8. The method for optimizing a microbial fermentation medium for agricultural feed production according to claim 1, wherein the step of generating a strain culture protocol sequence by setting the stable culture conditions, determining a subsequent raw material decomposition efficiency index, and obtaining an overall fermentation optimization path comprises the steps of: Generating a strain culture protocol sequence according to the stable culture condition setting, extracting fermentation starting point data from the protocol sequence, calculating subsequent raw material decomposition efficiency indexes by combining the stable culture condition setting, and summarizing all indexes to form an integral fermentation optimization path.

Description

Microbial fermentation culture medium optimization method for agricultural feed production Technical Field The invention relates to the technical field of information, in particular to a method for optimizing a microbial fermentation medium for agricultural feed production. Background In the field of agricultural cultivation, the improvement of the feed formula is directly related to the growth efficiency and health condition of livestock, and is a core link for improving cultivation benefit and sustainable development. The high-quality feed can reduce the cultivation cost and reduce the environmental pollution, so that how to optimize the feed ingredients and functions through technical innovation becomes an important subject to be solved in industry. Especially in the development of fermented feeds, the improvement of the nutritional value and digestibility of feeds by microbial fermentation technology has been widely considered as a direction of future development. However, in the current preparation process of fermented feeds, there is a general problem that the cultivation and application of microbial strains are not targeted, resulting in unstable fermentation effects. Many methods do not adequately take into account their metabolic characteristics and environmental suitability in the cultivation of strains, and often suffer from insufficient activity of the strain or undesirable fermentation products. This problem affects not only the conversion efficiency of the nutritional ingredients in the feed, but also may lead to waste of resources and increase of production costs. In a deeper level, the prior art often neglects the metabolic regulation of the strain in the culture stage, and fails to provide a good starting point for the subsequent fermentation process. Focusing on the technical difficulty, the design of the culture medium of probiotics in the fermented feed becomes a key bottleneck. The composition of the culture medium directly determines the growth state and metabolic direction of the strain, and if the strain cannot be precisely matched with the requirements of the strain, it is difficult for the strain to form specific metabolic capacity at an early stage. Further, the lack of such a match may affect the efficiency of the bacterial species to decompose the feed material during fermentation, e.g., some bacterial strains may not be able to effectively decompose proteins and thus produce active substances beneficial to livestock growth. Taking actual business as an example, in pig feed fermentation, if the strain fails to have the capability of decomposing specific proteins in advance, nutritional ingredients in the fermented feed may not be fully absorbed by pigs, resulting in slow growth speed or frequent health problems. Therefore, how to regulate the metabolic direction of the strain in the strain culture stage by optimizing the culture medium components and ensure that the strain can efficiently decompose feed raw materials in subsequent fermentation becomes a key problem in the improvement of the agricultural cultivation feed formula. Disclosure of Invention The invention provides a microbial fermentation medium optimization method for agricultural feed production, which mainly comprises the following steps: Obtaining feed raw material composition data and microorganism strain metabolism characteristic parameters, determining a preliminary matching degree index by comparing the feed raw material composition data with the metabolism characteristic parameters, obtaining a potential decomposition demand list of strains on raw materials, grouping and calibrating strain metabolism paths according to the potential decomposition demand list, obtaining an optimized metabolism path set, screening nutrition element combinations from a resource information base through the optimized metabolism path set, determining element proportion configuration, obtaining an initial culture medium formula draft, predicting strain growth state according to the initial culture medium formula draft by adopting a prediction simulation method, iteratively adjusting element proportion if predicted growth activity is insufficient, obtaining a refined culture medium formula, extracting key nutrition element characteristics from the refined culture medium formula, determining fermentation environment adaptability index by comparing the key nutrition element characteristics with historical fermentation data, obtaining an adaptability enhancement scheme, optimizing strain culture condition parameters according to the adaptability enhancement scheme by adopting a machine learning optimization method, obtaining a stable culture condition setting, generating strain culture medium decomposition efficiency index by adopting the stable culture condition setting, and obtaining an integral fermentation optimization path. Further, the preliminary matching degree index is determined by comparing the feed raw material composition