A method and apparatus for modeling gastric emptying characteristics of a compressed ration

Abstract

This invention discloses a method and apparatus for modeling the gastric emptying characteristics of compressed dry food. The method includes: obtaining a set of gastrointestinal digestive enzyme concentration ranges; the set of gastrointestinal digestive enzyme concentration ranges includes salivary amylase concentration range, pepsin concentration range, and pancreatic enzyme concentration range; based on the set of gastrointestinal digestive enzyme concentration ranges, performing measurement processing on the compressed dry food to obtain a set of gastric retention rate values; and performing gastric emptying characteristic modeling on the set of gastric retention rate values ​​to obtain a set of gastric emptying models.

Description

Technical Field

[0001] This invention relates to the fields of food science and data modeling, specifically to a method and apparatus for modeling the gastric emptying characteristics of compressed dry food. Background Technology

[0002] In the fields of food science and nutrition, compressed rations are widely used in outdoor adventures, emergency rescue, and other scenarios due to their small size, high energy density, and ease of storage and portability. However, the digestion and emptying process of compressed rations in the human gastrointestinal tract directly affects their nutrient absorption efficiency, as well as the user's feeling of fullness and gastrointestinal comfort. Therefore, in-depth research on the gastric emptying characteristics of compressed rations is crucial. Existing methods for studying gastric emptying mostly rely on empirical formulas or simple linear models for simulation, making it difficult to fully consider the complex physiological environment within the gastrointestinal tract. In particular, the impact of dynamic changes in gastrointestinal digestive enzyme concentrations on the gastric emptying process of compressed dry food has not been adequately studied or accurately modeled. Furthermore, traditional research methods often neglect the synergistic effects of different combinations of digestive enzyme concentrations on the gastric emptying process, leading to significant errors in the models used in practical applications. This makes it impossible to accurately predict the gastric emptying characteristics of compressed dry food in different individuals' gastrointestinal tracts, thus failing to meet the needs of modern food development and personalized nutritional interventions. Summary of the Invention

[0003] This invention primarily addresses the problem of how to accurately and rapidly model the gastric emptying characteristics of compressed dry food. This invention discloses a method and apparatus for modeling the gastric emptying characteristics of compressed dry food.

[0004] In a first aspect, this invention discloses a method for modeling the gastric emptying characteristics of compressed dry rations, comprising: S1, obtain the set of gastrointestinal digestive enzyme concentration ranges; the set of gastrointestinal digestive enzyme concentration ranges includes the concentration range of salivary amylase, the concentration range of pepsin, and the concentration range of pancreatic enzymes; S2, based on the set of gastrointestinal digestive enzyme concentration ranges, the compressed dry food is measured and processed to obtain a set of gastric retention rate values; S3, perform gastric emptying feature modeling on the set of gastric retention rate values ​​to obtain a set of gastric emptying models.

[0005] The method of measuring compressed dry food based on the set of gastrointestinal digestive enzyme concentration ranges to obtain a set of gastric retention rate values ​​includes: S21, Based on the set of gastrointestinal digestive enzyme concentration values, a digestive enzyme concentration tensor is constructed; the elements of the digestive enzyme concentration tensor are a combination of salivary amylase concentration values, pepsin concentration values, and pancreatic enzyme concentration values. S22, based on the digestive enzyme concentration tensor, an in vitro dynamic human gastrointestinal digestive system is used to simulate the digestion of compressed dry food to obtain a set of gastric retention rate values; the set of gastric retention rate values ​​includes a sequence of gastric retention rate values; the sequence of gastric retention rate values ​​is obtained under a set of combinations of salivary amylase concentration values, pepsin concentration values, and pancreatic enzyme concentration values, and corresponds to an element of the digestive enzyme concentration tensor; S23, update the digestive enzyme concentration tensor using the set of gastric retention rate values.

[0006] The digestive enzyme concentration tensor, constructed based on the set of gastrointestinal digestive enzyme concentration values, includes: S211, the concentration range of each type of enzyme in the set of gastrointestinal digestive enzyme concentration values ​​is evenly divided to obtain a sub-range for each type of enzyme; the median value of each sub-range is taken as the characterization value of the sub-range. S212 combines the characterization values ​​of all subranges of each enzyme class to construct the digestive enzyme concentration tensor.

[0007] The process of modeling gastric emptying characteristics on the set of gastric retention rate values ​​yields a set of gastric emptying models, including: S31, the set of gastric retention rate values ​​is used as the fourth dimension variable of the gastric emptying tensor, and the digestive enzyme concentration tensor is used as the first to third dimension variables of the gastric emptying tensor to construct the gastric emptying tensor. S32, calculate the order feature of the gastric emptying tensor to obtain the order value for each combination of values; S33, calculate the coefficients of the gastric emptying tensor to obtain the polynomial coefficient values ​​for each combination of values; S34. Based on the order value and polynomial coefficient value of each value combination, a gastric emptying model of the value combination is constructed. S35. Using all combinations of gastric emptying models, a set of gastric emptying models is constructed.

[0008] The calculation of the order feature of the gastric emptying tensor to obtain the order value for each combination of values ​​includes: S321, perform fourth-order tensor singular value decomposition on the gastric emptying tensor to obtain the first mode 1 tensor; S322, Subtract the first mode 1 tensor and the digestive enzyme concentration tensor to obtain the difference tensor; S323, Perform F-norm calculation on the difference tensor to obtain the difference quantization value. ct ; S324, perform fusion order calculation on the gastric retention rate value sequence corresponding to each value combination in the gastric emptying tensor to obtain the order value of the value combination.

[0009] The expression for calculating the fusion order is: , in, Let Y(i,j,k,:) be the mean, variance, and range of the gastric retention rate corresponding to the combination of values ​​(i,j,k). This indicates rounding down, and N represents the order of the combination of values.

[0010] The calculation of coefficients for the gastric emptying tensor yields polynomial coefficient values ​​for each combination of values, including: S331, Perform REMD decomposition on the vector of the fourth dimension variable of the gastric emptying tensor to obtain the transformation tensor; S332, Perform coefficient vector calculation on the transformation tensor and digestive enzyme concentration tensor to obtain the coefficient vector; S333, sort the values ​​of the coefficient vector from high to low, and take the first N+1 largest elements in the coefficient vector as the polynomial coefficient values ​​of the value combination.

[0011] A second aspect of the present invention discloses a device for modeling the gastric emptying characteristics of compressed dry food, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the gastric emptying feature modeling method for compressed dry food.

[0012] In a third aspect of this invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, which, when invoked by a computer, are used to execute the gastric emptying feature modeling method for compressed dry food.

[0013] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the gastric emptying feature modeling method for compressed dry food.

[0014] The beneficial effects of this invention are as follows: This invention obtains a comprehensive set of gastrointestinal digestive enzyme concentration ranges and constructs a digestive enzyme concentration tensor. It fully considers the impact of different combinations of concentrations of various digestive enzymes, such as salivary amylase, pepsin, and pancreatic enzymes, on the gastric emptying process of compressed dry food. Compared to traditional single-factor or simple linear models, this invention can more realistically and accurately simulate the complex digestive environment of the gastrointestinal tract, greatly improving the accuracy of gastric emptying feature modeling. Simultaneously, by utilizing an in vitro dynamic human gastrointestinal digestive system for simulated digestion, combined with the construction and analysis of the gastric emptying tensor, it achieves multi-dimensional and refined modeling of the gastric emptying process, overcoming the limitations of traditional research methods. The gastric emptying model set constructed by the method of this invention can provide a scientific basis for the optimization of compressed food formulations and the improvement of processes. It helps researchers to adjust the composition and structure of compressed food in a targeted manner, making it more in line with the human body's digestion and absorption patterns, improving nutrient utilization efficiency, and enhancing gastrointestinal tolerance in consumers. Furthermore, this model set can also be applied to the field of personalized nutritional assessment and dietary guidance. Based on the characteristics of gastrointestinal digestive enzyme concentrations in different individuals, it can predict the gastric emptying status of compressed food in their bodies, and develop personalized dietary plans for special populations (such as outdoor workers, patients with digestive system diseases, etc.), demonstrating broad application prospects and significant social benefits. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0016] To better understand the content of this invention, an embodiment is provided here.

[0017] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0018] In a first aspect, this invention discloses a method for modeling the gastric emptying characteristics of compressed dry rations, comprising: S1, obtain the set of gastrointestinal digestive enzyme concentration ranges; the set of gastrointestinal digestive enzyme concentration ranges includes the concentration range of salivary amylase, the concentration range of pepsin, and the concentration range of pancreatic enzymes; S2, based on the set of gastrointestinal digestive enzyme concentration ranges, the compressed dry food is measured and processed to obtain a set of gastric retention rate values; S3, perform gastric emptying feature modeling on the set of gastric retention rate values ​​to obtain a set of gastric emptying models.

[0019] The method of measuring compressed dry food based on the set of gastrointestinal digestive enzyme concentration ranges to obtain a set of gastric retention rate values ​​includes: S21, Based on the set of gastrointestinal digestive enzyme concentration values, a digestive enzyme concentration tensor is constructed; the elements of the digestive enzyme concentration tensor are a combination of salivary amylase concentration values, pepsin concentration values, and pancreatic enzyme concentration values. S22, based on the digestive enzyme concentration tensor, the compressed dry food is simulated for digestion using an in vitro dynamic human gastrointestinal digestive system to obtain a set of gastric retention rate values; the set of gastric retention rate values ​​includes a sequence of gastric retention rate values; the sequence of gastric retention rate values ​​is obtained by a combination of salivary amylase concentration values, pepsin concentration values, and pancreatic enzyme concentration values. S23, update the digestive enzyme concentration tensor using the set of gastric retention rate values.

[0020] The digestive enzyme concentration tensor, constructed based on the set of gastrointestinal digestive enzyme concentration values, includes: S211, the concentration range of each type of enzyme in the set of gastrointestinal digestive enzyme concentration values ​​is evenly divided to obtain a sub-range for each type of enzyme; the median value of each sub-range is taken as the characterization value of the sub-range. S212, combine the characterization values ​​of all subranges of each type of enzyme to construct the digestive enzyme concentration tensor; The digestive enzyme concentration tensor X The expression is: , in, It represents the average concentration of the combination of the values ​​of the salivary amylase concentration in the i-th subrange, the pepsin concentration in the j-th subrange, and the pancreatic enzyme concentration in the k-th subrange. It also represents the element of the digestive enzyme concentration tensor with dimension [i,j,k].

[0021] The average concentration is obtained by averaging the values ​​corresponding to the concentrations of salivary amylase, pepsin, and pancreatic enzymes.

[0022] The step of updating the digestive enzyme concentration tensor using the set of gastric retention rate values ​​involves using the mean of the gastric retention rate sequence corresponding to each combination of salivary amylase, pepsin, and pancreatic enzyme concentration values ​​in the set of gastric retention rate values, and then updating the digestive enzyme concentration tensor corresponding to that combination of values. X Update the element values ​​in the database.

[0023] The process of modeling gastric emptying characteristics on the set of gastric retention rate values ​​yields a set of gastric emptying models, including: S31, the set of gastric retention rate values ​​is used as the fourth dimension variable of the gastric emptying tensor, and the digestive enzyme concentration tensor is used as the first to third dimension variables of the gastric emptying tensor to construct the gastric emptying tensor. S32, calculate the order feature of the gastric emptying tensor to obtain the order value for each combination of values; S33, calculate the coefficients of the gastric emptying tensor to obtain the polynomial coefficient values ​​for each combination of values; S34. Based on the order value and polynomial coefficient value of each value combination, a gastric emptying model of the value combination is constructed. S35. Using all combinations of gastric emptying models, a set of gastric emptying models is constructed.

[0024] The expression for the gastric emptying tensor Y is: , in, The element representing the gastric emptying tensor with dimensions [i,j,k,l] is also the l-th element of the sequence of gastric retention rate values ​​obtained by combining the values ​​of the i-th subrange of salivary amylase concentration, the j-th subrange of pepsin concentration, and the k-th subrange of pancreatic enzyme concentration.

[0025] The calculation of the order feature of the gastric emptying tensor to obtain the order value for each combination of values ​​includes: S321, perform fourth-order tensor singular value decomposition on the gastric emptying tensor to obtain the first mode 1 tensor; S322, Subtract the first mode 1 tensor and the digestive enzyme concentration tensor to obtain the difference tensor; S323, Perform F-norm calculation on the difference tensor to obtain the difference quantization value. ct ; S324, perform fusion order calculation on the gastric retention rate value sequence corresponding to each value combination in the gastric emptying tensor to obtain the order value of the value combination.

[0026] The expression for calculating the fusion order is: , in, Let Y(i,j,k,:) be the mean, variance, and range of the gastric retention rate corresponding to the combination of values ​​(i,j,k). This indicates rounding down, and N represents the order of the combination of values.

[0027] In terms of order feature calculation, by employing steps such as fourth-order tensor singular value decomposition and calculation of differential quantification values, combined with the fusion order calculation formula, the mean, variance, and range of the gastric retention rate value sequence are taken into consideration. This allows for a deeper exploration of the intrinsic characteristics of each value combination in the gastric emptying tensor. This calculation method fully considers the statistical characteristics of the data. Compared to the traditional method of simply setting the order, it can more accurately reflect the complex dynamic changes in the gastric emptying process under different combinations of digestive enzyme concentrations. This enables the constructed gastric emptying model to better reflect the actual gastric emptying process, significantly improving the model's simulation accuracy of real physiological phenomena, thereby providing a more reliable theoretical basis for the digestion research of compressed dry food.

[0028] The calculation of coefficients for the gastric emptying tensor yields polynomial coefficient values ​​for each combination of values, including: S331, Perform REMD decomposition on the vector of the fourth dimension variable of the gastric emptying tensor to obtain the transformation tensor; S332, Perform coefficient vector calculation on the transformation tensor and digestive enzyme concentration tensor to obtain the coefficient vector; S333, sort the values ​​of the coefficient vector from high to low, and take the first N+1 largest elements in the coefficient vector as the polynomial coefficient values ​​of the value combination.

[0029] The expression for calculating the coefficient vector is: , , in, To transform the elements of a tensor with dimension [i,j,k,l], Let P be the geometric mean of the sequence of the first three dimensions of the transformation tensor [i,j,k], and let P be the value of the fourth dimension of the transformation tensor. Let [i, j, k] be the first element of the coefficient vector of the combination of values ​​corresponding to the elements of dimension [i, j, k] in the digestive enzyme concentration tensor. l Each element.

[0030] In terms of coefficient calculation, REMD decomposition and coefficient vector calculation formulas are used to calculate the coefficient vector based on the element characteristics of the transform tensor, and the first... N The largest element is used as the polynomial coefficient value. This calculation method effectively filters out key factors that significantly affect the gastric emptying process, accurately captures the nonlinear relationship between different combinations of digestive enzyme concentrations and gastric emptying, and avoids irrelevant or minor factors interfering with model construction. The obtained polynomial coefficient values ​​enable the gastric emptying model to more accurately describe the digestion and emptying patterns of compressed dry food in the gastrointestinal tract, improve the model's predictive ability and generalization performance, and thus provide more scientific and effective support for practical applications such as model-based compressed dry food formulation optimization and personalized nutrition program development.

[0031] For each element of the digestive enzyme concentration tensor, perform coefficient vector calculation to obtain the polynomial coefficient value for each combination of values.

[0032] The REMD decomposition of the vector of the fourth dimension variable of the gastric emptying tensor is expressed as follows: in, This represents the sequence of the first three dimensions of the transformation tensor as [i,j,k], which is also the transformation sequence obtained from REMD decomposition.

[0033] The first mode 1 tensor and the digestive enzyme concentration tensor have the same dimension.

[0034] The fourth-order tensor singular value decomposition can be achieved using a higher-order SVD algorithm.

[0035] REMD stands for Robust Empirical Mode Decomposition.

[0036] The F-norm refers to the Frobenius norm.

[0037] Based on the order and polynomial coefficients of each value combination, a gastric emptying model for the value combinations is constructed, wherein the expression of the gastric emptying model is: , in, Here is the expression for the gastric emptying model, where x represents the time value. These are the polynomial coefficient values, sorted from highest to lowest according to the magnitude of the coefficient vector values.

[0038] In all embodiments of the present invention, the variables involved in all computational expressions or mathematical functions have been dimensionlessized before computation.

[0039] In all embodiments of the present invention, the values ​​of the independent variables in the input of all computational expressions or mathematical functions meet the reasonable requirements of the input range of the computational expressions or mathematical functions, and can ensure that the computational expressions or mathematical functions can be calculated smoothly without violating physical laws or mathematical rules.

[0040] A second aspect of the present invention discloses a device for modeling the gastric emptying characteristics of compressed dry food, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the gastric emptying feature modeling method for compressed dry food.

[0041] In a third aspect of this invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, which, when invoked by a computer, are used to execute the gastric emptying feature modeling method for compressed dry food.

[0042] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the gastric emptying feature modeling method for compressed dry food.

[0043] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for modeling the gastric emptying characteristics of compressed dry rations, characterized in that, include: S1, obtain the set of gastrointestinal digestive enzyme concentration ranges; the set of gastrointestinal digestive enzyme concentration ranges includes the concentration range of salivary amylase, the concentration range of pepsin, and the concentration range of pancreatic enzymes; S2, based on the set of gastrointestinal digestive enzyme concentration ranges, the compressed dry food is measured and processed to obtain a set of gastric retention rate values; S3, perform gastric emptying feature modeling on the set of gastric retention rate values ​​to obtain a set of gastric emptying models.

2. The method for modeling gastric emptying characteristics of compressed dry rations as described in claim 1, characterized in that, The method of measuring compressed dry food based on the set of gastrointestinal digestive enzyme concentration ranges to obtain a set of gastric retention rate values ​​includes: S21, Based on the set of gastrointestinal digestive enzyme concentration values, a digestive enzyme concentration tensor is constructed; the elements of the digestive enzyme concentration tensor are a combination of salivary amylase concentration values, pepsin concentration values, and pancreatic enzyme concentration values. S22, based on the digestive enzyme concentration tensor, an in vitro dynamic human gastrointestinal digestive system is used to simulate the digestion of compressed dry food to obtain a set of gastric retention rate values; the set of gastric retention rate values ​​includes a sequence of gastric retention rate values; the sequence of gastric retention rate values ​​is obtained under a set of combinations of salivary amylase concentration values, pepsin concentration values, and pancreatic enzyme concentration values, and corresponds to an element of the digestive enzyme concentration tensor; S23, update the digestive enzyme concentration tensor using the set of gastric retention rate values.

3. The method for modeling gastric emptying characteristics of compressed dry rations as described in claim 1, characterized in that, The digestive enzyme concentration tensor, constructed based on the set of gastrointestinal digestive enzyme concentration values, includes: S211, the concentration range of each type of enzyme in the set of gastrointestinal digestive enzyme concentration values ​​is evenly divided to obtain a sub-range for each type of enzyme; the median value of each sub-range is taken as the characterization value of the sub-range. S212 combines the characterization values ​​of all subranges of each enzyme class to construct the digestive enzyme concentration tensor.

4. The method for modeling gastric emptying characteristics of compressed dry rations as described in claim 1, characterized in that, The process of modeling gastric emptying characteristics on the set of gastric retention rate values ​​yields a set of gastric emptying models, including: S31, the set of gastric retention rate values ​​is used as the fourth dimension variable of the gastric emptying tensor, and the digestive enzyme concentration tensor is used as the first to third dimension variables of the gastric emptying tensor to construct the gastric emptying tensor. S32, calculate the order feature of the gastric emptying tensor to obtain the order value for each combination of values; S33, calculate the coefficients of the gastric emptying tensor to obtain the polynomial coefficient values ​​for each combination of values; S34. Based on the order value and polynomial coefficient value of each value combination, a gastric emptying model of the value combination is constructed. S35. Using all combinations of gastric emptying models, a set of gastric emptying models is constructed.

5. The method for modeling gastric emptying characteristics of compressed dry rations as described in claim 4, characterized in that, The calculation of the order feature of the gastric emptying tensor to obtain the order value for each combination of values ​​includes: S321, perform fourth-order tensor singular value decomposition on the gastric emptying tensor to obtain the first mode 1 tensor; S322, Subtract the first mode 1 tensor and the digestive enzyme concentration tensor to obtain the difference tensor; S323, Perform F-norm calculation on the difference tensor to obtain the difference quantization value. ct ; S324, perform fusion order calculation on the gastric retention rate value sequence corresponding to each value combination in the gastric emptying tensor to obtain the order value of the value combination.

6. The method for modeling gastric emptying characteristics of compressed dry rations as described in claim 5, characterized in that, The expression for calculating the fusion order is: , in, Let Y(i,j,k,:) be the mean, variance, and range of the gastric retention rate corresponding to the combination of values ​​(i,j,k). This indicates rounding down, and N represents the order of the combination of values.

7. The method for modeling gastric emptying characteristics of compressed dry rations as described in claim 5, characterized in that, The calculation of coefficients for the gastric emptying tensor yields polynomial coefficient values ​​for each combination of values, including: S331, Perform REMD decomposition on the vector of the fourth dimension variable of the gastric emptying tensor to obtain the transformation tensor; S332, Perform coefficient vector calculation on the transformation tensor and digestive enzyme concentration tensor to obtain the coefficient vector; S333, sort the values ​​of the coefficient vector from high to low, and take the first N+1 largest elements in the coefficient vector as the polynomial coefficient values ​​of the value combination.

8. A device for modeling the gastric emptying characteristics of compressed dry rations, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the gastric emptying feature modeling method for compressed dry food as described in any one of claims 1 to 7.

9. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the computer, are used to execute the gastric emptying feature modeling method for compressed dry food as described in any one of claims 1 to 7.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the gastric emptying feature modeling method for compressed dry food as described in any one of claims 1 to 7.

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