Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection
Mennatullah Shehata1, Sophie Dodd1, Sara Mosca2
1Centre for Soil, Agrifood and Biosciences, Cranfield University, College Road, Cranfield, Bedford MK43 0AL, UK.
Foods (Basel, Switzerland)
|August 10, 2024
Summary
This study developed a rapid, non-invasive sensor method using spatial offset Raman spectroscopy (SORS) and machine learning to detect sugar adulteration in UK honey. The technology accurately identifies adulterant types and levels, aiding honey authentication for producers.
Area of Science:
- Food Science
- Analytical Chemistry
- Spectroscopy
Background:
- Traditional honey authentication methods are costly and time-consuming.
- Developing rapid, non-invasive techniques is crucial for producers.
- Exogenous sugar adulteration poses a significant challenge to honey integrity.
Purpose of the Study:
- To develop non-invasive sensor methods for detecting sugar adulteration in UK honeys.
- To quantify the type and percentage of exogenous sugar adulterants.
- To assess the feasibility of spatial offset Raman spectroscopy (SORS) coupled with multivariate analysis for honey authentication.
Main Methods:
- Spatial offset Raman spectroscopy (SORS) was used to analyze 17 types of UK honey.
- Samples were adulterated with rice and sugar beet syrups at various concentrations (10-50%).
- Multivariate data analysis, including Random Forest, PLS-DA, and XGBoost, was employed for classification and prediction.
Main Results:
- Random Forest achieved high accuracy in classifying pure vs. adulterated honey (<1% misclassification).
- The models successfully identified adulterant types (rice, sugar beet) and levels.
- SORS and Random Forest demonstrated clear separation of pure and adulterated heather honey samples, even at low adulteration levels (20%).
Conclusions:
- Spatial offset Raman spectroscopy (SORS) combined with machine learning is a viable tool for honey authentication.
- The developed method is rapid, non-invasive, and suitable for field deployment.
- This technology offers potential applications throughout the honey supply chain for quality control.
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