Near infrared spectroscopy and element concentration analysis for assessing yerba mate (Ilex paraguariensis) samples according to the country of origin

•A new framework for selecting variables for classification of yerba mate according to country of origin.•The method is applied in two different datasets: NIR spectroscopy and elements concentration.•Retained wavelengths/elements are discussed.•The method outperforms other variable selection approaches.

Yerba mate (Ilex paraguariensis) is used to produce a beverage typically consumed in South America countries, and presents peculiar land-based characteristics due to geographical origin. Such characteristics have recently become a matter of interest for many producers as specific features of yerba mate tend to influence product acceptance in new markets, prices and commercial advantages. This scenario justifies the developing of frameworks tailored to correctly classify products according to their authenticity. This paper uses Near Infrared (NIR) spectroscopy and data describing concentration of chemical elements to classify commercial yerba mate samples according to their place of origin. Aimed at enhancing data interpretability, we propose a novel variable selection method that applies quadratic programming to reduce redundant information among the retained variables and maximize their relationship regarding the sample place of origin; sample categorization is then performed using alternative classification techniques. When applied to the NIR dataset, the proposed method retained average 8.79% of the original wavenumbers, while leading to 1.9% more accurate classifications when compared to categorization using the full spectra. As for the elements dataset, we increased average classification accuracy by 3.5% and retained 47.22% of the original elements. The proposed method also outperformed two other approaches for variable selection from the literature. Our findings suggest that variable selection frameworks help to correctly identify the origin and authenticity of yerba mate samples, making model construction and interpretation easier.