强化α-乳清蛋白和β-酪蛋白提升蛋白质与矿物质吸收的评价数据

本数据集主要应用于婴幼儿配方奶粉研发，尤其是在模拟母乳蛋白质亚组成的过程中，评估强化α-乳清蛋白和β-酪蛋白促进婴幼儿蛋白质与矿物质吸收的影响。通过体外消化吸收模型，检测了强化α-乳清蛋白和β-酪蛋白样品消化后的游离氨基酸吸收总量、矿物质（钙、镁、锌）吸收率、转运载体蛋白表达量等重要指标，为配方奶粉中蛋白质精准优化和提升蛋白质和矿物质吸收提供了科学依据。该数据集可指导婴配粉的蛋白质添加、配方升级与功能性优化，特别是提升蛋白质和矿物质的吸收效果，从而为婴幼儿提供更易吸收、更接近母乳的营养支持。（1）实验设计与建模目标
采用婴儿体外胃肠消化模型模拟婴幼儿的消化过程，结合Caco-2细胞吸收模型，评估强化α-乳清蛋白与β-酪蛋白样品对蛋白质及矿物质（钙、镁、锌）吸收的影响。
（2）数据计算与处理规则
实验设立对照组、α-乳白蛋白和β-酪蛋白强化组（2个不同强化量的组别）、α-乳白蛋白组、β-酪蛋白组，计算游离氨基酸吸收总量（单位nM/L）和矿物质（钙、镁、锌）的吸收率，并结合转运载体蛋白表达量进一步分析其对吸收效果的促进作用。表中，各组的“1、2、3” 代表同一个剂量下进行3组相同试验。“α+β、α-La、β-CN”代表 α-乳白蛋白、β-酪蛋白占总蛋白含量的不同，命名不同。“低、高”代表设置的不同的α-乳白蛋白、β-酪蛋白占总蛋白含量。
（3）算法应用与输出
对各组样品在氨基酸吸收总量、矿物质（钙、镁、锌）吸收率及转运载体蛋白表达量方面进行统计分析，计算均值以反映数据集中趋势，计算标准差以描述数据的离散程度，评估不同蛋白质组别对吸收指标的影响。使用SPSS18.0软件对各组数据进行分析，两组间比较采用t检验，三组及以上采用单因素方差分析，以P＜0.05表示组件差异具有统计学意义。通过建立多维度分析模型，评估强化α-乳清蛋白与β-酪蛋白对婴幼儿营养吸收的综合效应，为婴幼儿配方奶粉的科学配方与精准创制提供理论支持。

This dataset is mainly applied to the research and development of infant formula, particularly in the process of simulating the protein sub-composition of human breast milk, to evaluate the effect of fortified α-lactalbumin and β-casein on promoting infant protein and mineral absorption. Using an in vitro digestion and absorption model, key indicators including the total absorbed free amino acids of fortified α-lactalbumin and β-casein samples after digestion, the absorption rates of minerals (calcium, magnesium, zinc), and the expression levels of transport carrier proteins were detected, providing a scientific basis for the precision optimization of proteins in infant formula and the improvement of protein and mineral absorption. This dataset can guide protein addition, formula upgrading and functional optimization of infant formula, especially enhancing the absorption effect of protein and minerals, thereby providing more easily absorbed, more breast milk-mimetic nutritional support for infants.

(1) Experimental Design and Modeling Objectives
An infant in vitro gastrointestinal digestion model was used to simulate the infant digestive process, combined with the Caco-2 cell absorption model, to evaluate the effect of fortified α-lactalbumin and β-casein samples on the absorption of protein and minerals (calcium, magnesium, zinc).

(2) Data Calculation and Processing Rules
The experiment set up a control group, α-lactalbumin and β-casein fortified groups (two groups with different fortification levels), α-lactalbumin group, and β-casein group. The total absorbed free amino acids (unit: nM/L) and the absorption rates of minerals (calcium, magnesium, zinc) were calculated, and combined with the expression levels of transport carrier proteins, the promoting effect on absorption was further analyzed. In the table, "1, 2, 3" for each group represents 3 identical experiments conducted under the same dosage. "α+β, α-La, β-CN" refer to different proportions of α-lactalbumin and β-casein in the total protein content, resulting in different group names. "Low" and "High" represent the set different proportions of α-lactalbumin and β-casein in the total protein content.

(3) Algorithm Application and Output
Statistical analysis was conducted on each group of samples in terms of total amino acid absorption, mineral (calcium, magnesium, zinc) absorption rates, and expression levels of transport carrier proteins. The mean value was calculated to reflect the central tendency of the data, and the standard deviation was calculated to describe the dispersion of the data, to evaluate the effect of different protein groups on absorption indicators. SPSS 18.0 software was used to analyze the data of each group: t-test was used for comparison between two groups, and one-way ANOVA was used for three or more groups, with P < 0.05 indicating statistically significant differences between groups. A multi-dimensional analysis model was established to evaluate the comprehensive effect of fortified α-lactalbumin and β-casein on infant nutritional absorption, providing theoretical support for the scientific formulation and precision development of infant formula.

搜集汇总

背景与挑战

背景概述

该数据集记录了强化α-乳清蛋白和β-酪蛋白在婴幼儿配方奶粉研发中对蛋白质与矿物质吸收的影响，通过体外实验模型检测多项指标，为配方优化提供科学依据。