首页    期刊浏览 2024年09月18日 星期三
登录注册

文章基本信息

  • 标题:Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination
  • 本地全文:下载
  • 作者:Camelia Berghian-Grosan ; Dana Alina Magdas
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2020
  • 卷号:10
  • 期号:1
  • 页码:1-9
  • DOI:10.1038/s41598-020-78159-8
  • 出版社:Springer Nature
  • 摘要: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.
  • 其他摘要:Abstract 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.
国家哲学社会科学文献中心版权所有