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Abstract ObjectivesThis study explored differing levels of macronutrients in breast milk in relation to maternal anemia and hemoglobin. MethodsArchived milk specimens and data from a cross‐sectional sample of 208 breastfeeding mothers in northern Kenya, originally collected in 2006, were analyzed; data included milk fat, maternal hemoglobin concentration, and anemia status (anemia defined as hemoglobin <12 g/dL). Total protein and lactose were measured and energy was calculated. To explore the association between milk outcomes (fat, protein, lactose, and energy) and anemia, regression models were constructed with and without adjustment for maternal age, parity, and time (days) postpartum. The same models were constructed using hemoglobin as a continuous predictor in lieu of dichotomous anemia to explore the role of hemoglobin levels and anemia severity in predicting milk outcomes. ResultsThe group comparison indicated significantly higher milk protein and lower milk fat for anemic mothers relative to nonanemic counterparts. After adjustment for maternal age, parity, and time postpartum, maternal anemia was associated with significantly higher milk protein (P = 0.001) and significantly lower milk fat (P = 0.025). Hemoglobin had a significant inverse relationship with milk protein (P = 0.017) and a marginally significant positive relationship with milk fat (P = 0.060) after adjusting for the maternal variables. Neither anemia nor hemoglobin was significant in predicting lactose or milk energy. ConclusionsMaternal anemia and hemoglobin concentration may be associated with complex changes in milk macronutrients. Future research should clarify the impact of maternal anemia on a range of breast milk components while accounting for other maternal characteristics.
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This content will become publicly available on September 1, 2026
Automated Spectral Preprocessing via Bayesian Optimization for Chemometric Analysis of Milk Constituents
The preprocessing of infrared spectra can significantly improve predictive accuracy for protein, carbohydrate, lipid, or other nutrition components, yet optimal preprocessing selection is typically empirical, tedious, and dataset specific. This study introduces a Bayesian optimization-based framework designed for the automated selection of optimal spectral preprocessing pipelines within a chemometric modeling context. The framework was applied to mid-infrared spectra of milk to predict compositional parameters for fat, protein, lactose, and total solids. A total of 385 averaged spectra corresponding to 198 unique samples was split into a 70/30 ratio (training/test) using a group-aware Kennard-Stone algorithm, resulting in 269 averaged spectra (135 unique samples) for training and 116 spectra (58 unique samples) for testing. Six regression models: Elastic Net, Gradient Boosting Machines (GBM), Partial Least Squares (PLS), RidgeCV Regression, LassoLarsCV, and Support Vector Regression (SVR) were evaluated across three preprocessing conditions: (1) no preprocessing, (2) literature-derived custom preprocessing (e.g., MSC, SNV, and first and second derivatives), and (3) optimized preprocessing via the proposed Bayesian framework. Optimized preprocessing consistently outperformed other methods, with RidgeCV achieving the best performance for all components except lactose, where PLS slightly outperformed it. Improvements in predictive accuracy, particularly in terms of RMSEP were observed across all milk components. The best RMSEP results were achieved for protein (RMSEP = 0.054, R2=0.981) and lactose (RMSEP = 0.026, R2=0.917), followed by fat (RMSEP = 0.139, R2=0.926) and total solids (RMSEP = 0.154, R2=0.960). Literature-based pipelines demonstrated inconsistent effectiveness, highlighting the limitations of transferring preprocessing methods between datasets. The Bayesian optimization approach identified relatively simple yet highly effective preprocessing pipelines, typically involving few steps. By eliminating manual trial and error, this data-driven strategy offers a robust and generalizable solution that streamlines spectral modeling in dairy analysis and can be readily applied to other types of spectroscopic data across various domains.
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- Award ID(s):
- 2345069
- PAR ID:
- 10649822
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Foods
- Volume:
- 14
- Issue:
- 17
- ISSN:
- 2304-8158
- Page Range / eLocation ID:
- 2996
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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