Geographical and varietal origin differentiation of alcoholic beverages through the association between FT-Raman spectroscopy and advanced data processing strategies

Ariana Raluca Hategan; Maria David; Camelia Berghian-Grosan; Dana Alina Magdas

doi:10.1016/j.fochx.2023.100902

Geographical and varietal origin differentiation of alcoholic beverages through the association between FT-Raman spectroscopy and advanced data processing strategies

Food Chem X. 2023 Sep 25:20:100902. doi: 10.1016/j.fochx.2023.100902. eCollection 2023 Dec 30.

Authors

Ariana Raluca Hategan^{1

2}, Maria David^{1

2}, Camelia Berghian-Grosan¹, Dana Alina Magdas^{1

2}

Affiliations

¹ National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania.
² Faculty of Physics, Babeș-Bolyai University, Kogălniceanu 1, 400084 Cluj-Napoca, Romania.

Abstract

The present work aimed to test the efficiency of FT-Raman spectroscopy for fruit spirits discrimination by developing differentiation models based on two approaches, namely a supervised statistical method (Partial Least Squares Discriminant Analysis), and a Machine Learning technique (Support Vector Machines). For this purpose, a data set comprising 86 Romanian distillate samples was used, which aimed to be differentiated in terms of the raw material used for production (plum, apple, pear and grape) and county of origin (Cluj, Satu Mare and Salaj). Eight distinct preprocessing methods (autoscale, mean center, variance scaling, smoothing, 1^st derivative, 2^nd derivative, standard normal variate and Pareto) followed by a feature selection step were applied to identify the meaningful input data based on which the most efficient classification models can be constructed. Both types of models led to accuracy scores greater than 90% in differentiating the distillate samples in terms of geographical and botanical origin.

Keywords: Fruit spirits; PLS-DA; Raman spectroscopy; Recognition models; SVM.