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

Through this pilot study, the association between Raman spectroscopy and Machine Learning algorithms were used for the first time with the purpose of distillates differentiation with respect to trademark, geographical and botanical origin. Two spectral Raman ranges (region I—200–600 cm(−1) and regio...

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Autores principales: Berghian-Grosan, Camelia, Magdas, Dana Alina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713252/
https://www.ncbi.nlm.nih.gov/pubmed/33273608
http://dx.doi.org/10.1038/s41598-020-78159-8
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author Berghian-Grosan, Camelia
Magdas, Dana Alina
author_facet Berghian-Grosan, Camelia
Magdas, Dana Alina
author_sort Berghian-Grosan, Camelia
collection PubMed
description Through this pilot study, the association between Raman spectroscopy and Machine Learning algorithms were used for the first time with the purpose of distillates differentiation with respect to trademark, geographical and botanical origin. Two spectral Raman ranges (region I—200–600 cm(−1) and region II—1200–1400 cm(−1)) appeared to have the higher discrimination potential for the investigated distillates. The proposed approach proved to be a very effective one for trademark fingerprint differentiation, a model accuracy of 95.5% being obtained (only one sample was misclassified). A comparable model accuracy (90.9%) was achieved for the geographical discrimination of the fruit spirits which can be considered as a very good one taking into account that this classification was made inside Transylvania region, among neighbouring areas. Because the trademark fingerprint is the prevailing one, the successfully distillate type differentiation, with respect to the fruit variety, was possible to be made only inside of each producing entity.
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spelling pubmed-77132522020-12-03 Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination Berghian-Grosan, Camelia Magdas, Dana Alina Sci Rep Article Through this pilot study, the association between Raman spectroscopy and Machine Learning algorithms were used for the first time with the purpose of distillates differentiation with respect to trademark, geographical and botanical origin. Two spectral Raman ranges (region I—200–600 cm(−1) and region II—1200–1400 cm(−1)) appeared to have the higher discrimination potential for the investigated distillates. The proposed approach proved to be a very effective one for trademark fingerprint differentiation, a model accuracy of 95.5% being obtained (only one sample was misclassified). A comparable model accuracy (90.9%) was achieved for the geographical discrimination of the fruit spirits which can be considered as a very good one taking into account that this classification was made inside Transylvania region, among neighbouring areas. Because the trademark fingerprint is the prevailing one, the successfully distillate type differentiation, with respect to the fruit variety, was possible to be made only inside of each producing entity. Nature Publishing Group UK 2020-12-03 /pmc/articles/PMC7713252/ /pubmed/33273608 http://dx.doi.org/10.1038/s41598-020-78159-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Berghian-Grosan, Camelia
Magdas, Dana Alina
Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination
title Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination
title_full Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination
title_fullStr Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination
title_full_unstemmed Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination
title_short Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination
title_sort application of raman spectroscopy and machine learning algorithms for fruit distillates discrimination
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713252/
https://www.ncbi.nlm.nih.gov/pubmed/33273608
http://dx.doi.org/10.1038/s41598-020-78159-8
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