A method for predicting volatile substances in breast milk and applications thereof

By combining HS-SPME-Arrow and GC-MS technologies with feature combinations and predictive models, the problem of detecting volatile substances in breast milk has been solved. This has enabled high-sensitivity, low-sample-size breast milk flavor analysis, providing information on maternal diets and optimizing breast milk quality and infant formula.

CN122117251APending Publication Date: 2026-05-29BEIJING SANYUAN FOOD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING SANYUAN FOOD
Filing Date
2026-02-03
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing technologies are insufficient for efficiently and comprehensively detecting volatile substances in breast milk, making it difficult to understand the impact of the mother's diet on the flavor of breast milk, which in turn affects the infant's taste preferences and feeding behavior.

Method used

By combining the HS-SPME-Arrow method with GC-MS technology, the content of volatile substances in breast milk can be predicted through feature combination and prediction model. A prediction model based on one-way ANOVA and multiple linear regression analysis is established, and combined with breast milk and maternal dietary information, rapid and accurate detection of volatile substances can be achieved.

Benefits of technology

It achieves highly sensitive detection of volatile substances in breast milk with low sample size, accurately predicts changes in volatile components in breast milk, provides information on maternal diet, and helps optimize breast milk quality and infant formula formulation.

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Abstract

The application provides a method for predicting volatile substances in breast milk and mother's diet and application thereof, comprising: obtaining diet information of a mother, performing nutrition element calculation on the diet information, inputting a prediction model, and predicting the content of volatile substances in breast milk; obtaining volatile substances in breast milk, inputting the prediction model, and predicting the diet information of the mother; the prediction model is obtained by using single factor variance analysis and multivariate linear regression analysis; wherein the volatile substances in breast milk include one or more of caprylic acid, 4-octanone, ethyl caprylate, nonanal, ethylbenzene and decane; and the mother's diet includes one or more of protein, fat, dietary fiber, cholesterol and folic acid. The volatile components in breast milk are separated by using HS-SPME-Arrow for the first time, and combined with chemometrics, so that the change of volatile flavor substances in breast milk can be rapidly and accurately detected.
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