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Updated: Jan 18, 2026

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Automated Spectral Preprocessing via Bayesian Optimization for Chemometric Analysis of Milk Constituents.

Habeeb Abolaji Babatunde1, Owen M McDougal2, Timothy Andersen1

  • 1Computer Science, Boise State University, Boise, ID 83725, USA.

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|September 13, 2025
PubMed
Summary
This summary is machine-generated.

Automated spectral preprocessing using Bayesian optimization significantly improves milk component prediction accuracy. This data-driven approach streamlines analysis and offers a robust, generalizable solution for spectroscopic data modeling.

Keywords:
Bayesian optimizationPLSchemometricsdairyfoodinfrared spectroscopymachine learningmilkregression analysisspectral preprocessing

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Data Science

Background:

  • Spectral preprocessing is crucial for accurate nutritional component prediction.
  • Optimal preprocessing selection is often empirical, time-consuming, and dataset-specific.

Purpose of the Study:

  • To introduce a Bayesian optimization framework for automated spectral preprocessing pipeline selection.
  • To apply this framework to mid-infrared spectra of milk for predicting compositional parameters.

Main Methods:

  • A Bayesian optimization framework was developed for automated preprocessing selection.
  • Six regression models were evaluated on mid-infrared milk spectra.
  • Preprocessing conditions included no preprocessing, literature-derived methods, and optimized pipelines.

Main Results:

  • Optimized preprocessing consistently outperformed other methods across all milk components.
  • RidgeCV and PLS regression models achieved the best predictive accuracy.
  • Significant improvements in predictive accuracy (RMSEP) were observed for protein, lactose, fat, and total solids.

Conclusions:

  • Bayesian optimization provides a robust, generalizable, and efficient method for selecting spectral preprocessing pipelines.
  • This data-driven strategy eliminates manual trial and error, streamlining spectroscopic analysis.
  • The framework is applicable to dairy analysis and other spectroscopic data domains.