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- W3205167211 abstract "The application of artificial intelligence, i.e. Machine Learning, represents a step forward in food metabolomics/authentication approaches. The present study demonstrates the applicability of the FT-Raman spectroscopy in association with Machine Learning algorithms, for trademark fingerprint assessment of fruit spirits. Thus, based on the best obtained model, a prediction ability of 96.2% was achieved. A good geographical origin prediction of investigated distillates was attained, despite that this classification was a very challenging one taking into account that the separation was made inside close Transylvanian areas. For botanical recognition, the obtained differentiation was quite modest suggesting that the differentiations which appear among the spirits prepared from various fruits are too subtle to be unequivocal detected through this proposed approach. In order to improve the prediction ability, different spectral ranges (Stokes and anti-Stokes) were considered and the best spectral interval for each classification model was identified and distinct Machine Learning algorithms were tested." @default.
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- W3205167211 date "2022-03-01" @default.
- W3205167211 modified "2023-10-18" @default.
- W3205167211 title "Fruit spirits fingerprint pointed out through artificial intelligence and FT-Raman spectroscopy" @default.
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- W3205167211 doi "https://doi.org/10.1016/j.foodcont.2021.108630" @default.
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