Evidence map›Paper›PMID 40408619›Full record

ArticlePloS one2025

Chemometrics-aided surface-enhanced Raman spectrometric detection and quantification of GH and TE hormones in blood.

Annah M Ondieki, Zephania Birech, Kenneth A Kaduki, Peter W Mwangi, Moses Juma, Boniface M Chege

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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Annah M OndiekiLaser Physics and Spectroscopy Research Group, Department of Physics, University of Nairobi, Nairobi, Kenya.ORCID https://orcid.org/0000-0003-4096-2813
Zephania BirechLaser Physics and Spectroscopy Research Group, Department of Physics, University of Nairobi, Nairobi, Kenya.ORCID https://orcid.org/0000-0001-7445-1603
Kenneth A KadukiLaser Physics and Spectroscopy Research Group, Department of Physics, University of Nairobi, Nairobi, Kenya.
Peter W MwangiDepartment of Medical Physiology, University of Nairobi, Nairobi, Kenya.
Moses JumaLaser Physics and Spectroscopy Research Group, Department of Physics, University of Nairobi, Nairobi, Kenya.
Boniface M ChegeDepartment of Medical Physiology, University of Nairobi, Nairobi, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Growth hormone (GH) and testosterone (TE) levels in blood are crucial indicators of human health and performance in clean sports. Deviations from normal levels can signal serious health issues, such as fertility problems, cancer, or pituitary tumors. Existing detection methods for these hormones are often costly, time-consuming, and lack portability. In this study, we explored the potential of Surface-Enhanced Raman Spectroscopy (SERS) in distinguishing blood samples from Sprague Dawley (SD) rats injected with exogenous GH, TE and both hormones from those not injected. Then, used artificial neural network (ANN) models trained, and validated in predicting levels of these hormones in blood. Blood samples from SD rats injected with GH, TE, both hormones, and non-injected rats were analyzed using the SERS method upon 785 nm laser excitation. The recorded Raman spectra from blood of GH and TE injected and non-injected rats displayed hormone-specific band intensity variations. Additionally, Principal Component Analysis (PCA) showed temporal changes in band intensities post-injection, suggesting hormone-induced biochemical alterations. In particular, Raman bands centered around 1378 cm⁻¹ for all groups, 658 cm⁻¹ for GH, and 798 cm⁻¹ for GH and TE displayed significant intensity variations. The ANN models, trained using PCA scores from blood samples with varied hormone concentrations, achieved high predictive accuracy with coefficients of determination (R² > 87.71%) and low root mean square error (RMSE < 0.6436). Elevated hormone levels were initially observed in injected rats, gradually declining over time, with results aligning closely to those obtained via ELISA kits. This work showed that the SERS method can provide rapid (~2 minutes), hormone-independent detection with minimal sample preparation. This approach demonstrated the SERS method's potential for rapid, reliable hormone detection and with customized calibration may be applied in sports doping control, clinical diagnostics, and broader biomedical research.

Indexed as

ChemometricsGrowth HormoneSpectrum Analysis, RamanTestosteroneAnimalsHumansMaleNeural Networks, ComputerPrincipal Component AnalysisRatsRats, Sprague-DawleyGrowth HormoneTestosterone

Identifiers

PMID40408619
PMCPMC12101856

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