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Botanical honey recognition and quantitative mixture detection based on Raman spectroscopy and machine learning
Dana Alina Magdas1, Camelia Berghian-Grosan1
1National Institute for Research and Development of Isotopic and Molecular Technologies, Donat, 67-103, 400293 Cluj-Napoca, Romania.
Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|February 9, 2023
Summary
New models combining Raman spectroscopy and Artificial Intelligence (AI) accurately identify honey varieties and detect mixtures. This approach shows significant potential for practical honey recognition applications.
Area of Science:
- Analytical Chemistry
- Food Science
- Spectroscopy
Background:
- Spectroscopic techniques offer market potential for honey recognition, driven by portable equipment advancements.
- Raman spectroscopy coupled with Artificial Intelligence (AI), specifically Machine Learning algorithms, is an emerging, efficient method for food and beverage analysis.
Purpose of the Study:
- To develop novel recognition models for rapid and accurate botanical differentiation of honey varieties.
- To assess the capability of AI-driven Raman spectroscopy for detecting honey mixtures and estimating their composition.
Main Methods:
- Utilized Raman spectroscopy for data acquisition from various honey samples.
- Developed and applied Machine Learning algorithms to build predictive recognition models.
- Evaluated model performance using precision, sensitivity, and specificity metrics.
Main Results:
- Achieved correct prediction percentages exceeding 81% for differentiating honey varieties.
- Successfully detected the presence of honey mixtures.
- Provided an estimative percentage of components within detected honey mixtures.
Conclusions:
- The combined approach of Raman spectroscopy and AI demonstrates significant potential for practical honey recognition.
- This method enables both qualitative identification of honey types and quantitative estimation of honey mixtures.
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