APPLICATION OF AI AND MACHINE LEARNING TO ANALYZE PROTEIN CONTENT IN U.S. COMMERCIAL BABY FOODS.

Malick T Yedjou, Kevine M Makoudjou, Ingrid K Tchakoua, Solange S Tchounwou, Clement G Yedjou

Abstract read

In one paragraph

Article in International journal of science academic research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Malick T YedjouDepartment of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.

Kevine M MakoudjouDepartment of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.

Ingrid K TchakouaDepartment of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.

Solange S TchounwouDepartment of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.

Clement G YedjouDepartment of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.

Funding

Wastewater surveillance for addressing environmental health disparitiesU54MD013376 · NIMHD · MORGAN STATE UNIVERSITY · PI Timothy J Meeker · 2019 to 2026

$33.5M

NIMHD NIH HHS U54 MD013376

6 · The paper itself

Abstract

Proteins are essential macronutrients that support the growth, development, and maintenance of tissues in children. Nutrient requirements vary with age, weight, and physiological needs, making age-specific dietary planning critical. Adequate protein intake promotes both physical growth and cognitive development, while diverse sources such as lean meats, dairy, legumes, and nuts help meet varying nutritional needs and encourage lifelong healthy eating habits. This study analyzed a nutritional dataset of 244 baby foods using artificial intelligence (AI) and machine learning to assess protein content, categorizing items into three groups based on protein content: low (0.0-5.9 g/day), moderate (6.0-10.9 g/day), and high (11.0-15.0 g/day). The majority (n = 202) fell into the low-protein range, followed by 22 in the moderate range and 20 in the high range. Age-specific protein requirements, expressed in grams per kilogram of body weight (g/kg), were assessed for four age groups: 0-6 months (1.52 g/kg; 12.6-15.8 g/day; 5.5-6.0 kg), 7-9 months (1.20 g/kg; 9.0-10.2 g/day; 7.5-8.5 kg), 10-12 months (1.00 g/kg; 8.5-9.5 g/day; 8.5-9.5 kg), and 1-3 years (1.05 g/kg; 12.6-15.8 g/day; 12.0-15.0 kg). Low-protein foods may be insufficient for infants with reduced breastmilk or formula intake, while high-protein foods often rich in meat, dairy, or fortified products can help meet upper-range requirements. These findings underscore the need for careful alignment of complementary food protein levels with age-specific nutritional guidelines to support optimal growth and development in early childhood.

Indexed as

AIBaby foodsInfantsmachine learningProtein contentYoung children

Identifiers

PMID40959415

PMCPMC12435971

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.