Chemometrics modeling for rapid prediction of physicochemical properties and hydroxymethylfurfural of honey based on hyperspectral Raman spectroscopy

Hashempour-Baltork, Fataneh; Vali Zade, Somaye; Mirza Alizadeh, Adel; Jannat, Behrooz; Rastegar, Hossein

doi:10.22104/ift.2025.7892.2240

Chemometrics modeling for rapid prediction of physicochemical properties and hydroxymethylfurfural of honey based on hyperspectral Raman spectroscopy

Document Type : Research Article

Authors

¹ Halal Research Center of IRI, Iran Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran.

² Department of Food Safety and Hygiene, School of Public Health, Zanjan University of Medical Sciences, Zanjan, Iran.

10.22104/ift.2025.7892.2240

Abstract

The aim of this study was to investigate the feasibility of rapid prediction of physicochemical properties in honey using Raman spectroscopy and chemometrics models. In this research, 51 honey samples were collected from different regions of Iran, and each sample was analyzed by Raman spectroscopy (in the range of 100–3500 cm⁻¹). The investigated properties included reducing sugars before hydrolysis, sucrose, moisture, and hydroxymethylfurfural (HMF), which were measured using standard laboratory methods. The spectral data, after mean-centering preprocessing, were correlated with the laboratory values of the physicochemical properties, and four independent models based on Partial Least Squares (PLS) regression were developed. The models were validated using the Kennard–Stone algorithm for splitting the data into calibration and test sets, cross-validation with the Leave-One-Out method to optimize the number of latent variables, and indices such as RMSEP, %REP, and RMSECV. The results showed that the predictive models for reducing sugars before hydrolysis and moisture performed excellently with low relative errors (1.64% and 5.30%, respectively). The sucrose model, with a relative error of about 11.95%, was acceptable, while the HMF model showed lower accuracy with a relative error of 25.8%. Overall, combining Raman spectroscopy with PLS models provides a rapid, non-destructive, cost-effective, and environmentally friendly approach for honey quality control, which can play a significant role in monitoring authenticity, distribution, and economic development of this product.

Graphical Abstract

Highlights

Raman spectroscopy combined with chemometrics models provides a rapid, non-destructive, and low-cost method for honey quality control and authenticity assessment.
The developed PLS models accurately predicted honey reducing sugars and moisture with low relative errors (1.64% and 5.30%).
Integration of spectral data and statistical algorithms enabled determination of botanical origin and purity of honey with accuracy above 90%.
Raman spectroscopy-based models demonstrated the ability to detect adulterated honey with validation accuracy exceeding 98%.
This novel approach offers a sustainable, fast, and reliable alternative to costly and time-consuming traditional methods for honey monitoring.

Keywords

Main Subjects

Novel technologies

References

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