ArticleSpectrochimica acta. Part A, Molecular and biomolecular spectroscopy2025
Optimizing number of Raman spectra using an artificial neural network guided Monte Carlo simulation approach to analyze human cortical bone.
Article in Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning reconstruction of digit bone Raman spectra enables noninvasive transcutaneous detection of systemic osteoporosis.Biomedical optics express · 2026Article
- Recent Advances in Raman Spectral Classification with Machine Learning.Sensors (Basel, Switzerland) · 2026Review
- Prediction of biomechanical properties of ex vivo human femoral cortical bone using Raman spectroscopy and machine learning algorithms.Bone reports · 2025Article
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Authors and funding
4 authors.
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Abstract
This study presents a novel methodology for optimizing the number of Raman spectra required per sample for human bone compositional analysis. The methodology integrates Artificial Neural Network (ANN) and Monte Carlo Simulation (MCS). We demonstrate the robustness of ANN in enabling prediction of Raman spectroscopy-based bone quality properties even with limited spectral inputs. The ANN algorithms tailored to individual sex and age groups, which enhance the specificity and accuracy of predictions in bone quality properties. In addition, ANN guided MCS systematically explores the variability and uncertainty inherent in different sample sizes and spectral datasets, leading to the identification of an optimal number of spectra per sample for characterizing human bone tissues. The findings suggest that as low as 2 spectra are sufficient for biochemical analysis of bone, with R
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