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Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination

Food Science and Technology

Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination

C. Berghian-grosan and D. A. Magdas

This pilot study explores the transformative potential of combining Raman spectroscopy with Machine Learning algorithms for distinguishing between various fruit distillates based on their trademark, geographical, and botanical origins. Conducted by Camelia Berghian-Grosan and Dana Alina Magdas, the research showcases impressive accuracy rates of 95.5% for trademark differentiation and 90.9% for geographical classification in the Transylvania region.

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Playback language: English
Abstract
This pilot study investigates the use of Raman spectroscopy combined with Machine Learning algorithms to differentiate fruit distillates based on trademark, geographical, and botanical origin. Two spectral Raman ranges (200-600 cm⁻¹ and 1200-1400 cm⁻¹) showed the highest discrimination potential. The approach achieved 95.5% accuracy for trademark differentiation and 90.9% for geographical discrimination within the Transylvania region.
Publisher
Scientific Reports
Published On
Dec 03, 2020
Authors
Camelia Berghian-Grosan, Dana Alina Magdas
Tags
Raman spectroscopy
Machine Learning
fruit distillates
trademark differentiation
geographical classification
Transylvania
botanical origin

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