Sateesh B Arja, Sireesha Bala Arja, Kumar Ponnusamy, Praveen Kottath Veetil, Simi Paramban, Yoshita Chandru Laungani
Medical Education Electives Can Promote Teaching and Research Interests Among Medical Students
Advances in medical education and practice
view maintaining reputation wineproducing regions among consumers minimising economic losses caused wine fraud achieving purpose datadriven terroir classification use absorbancetransmission fluorescence excitationemission matrix ateem technique shown great potential based molecular fingerprinting sample effects changes wine composition due ageing stability ateem models time addressed however classification wine blends required investigation thus ateem data combined extreme gradient boosting discriminant analysis xgbda algorithm build classification models based range shiraz research wines n 217 five barossa valley subregions four vintages aged bottle several years spectral fingerprinting machine learning approach revealed 100 class prediction accuracy based crossvalidation cv model results vintage year 988 unknown sample prediction accuracy splitting wine samples training test sets obtain classification models modelling prediction subregional production area showed class cv prediction accuracy 995 unknown sample prediction accuracy 938 modelling split dataset inputting subset current ateem data models generated previously barossa subregion wines yielded 100 accurate prediction vintage year 20182020 wines 92 accuracy subregion 2018 wines 91 accuracy subregion using 2021 wine spectral data included original modelling satisfactory results also obtained modelling prediction blended samples vintages subregions significance considering practice wine blending