LAUSR

Abstract This work reports the application a sensor array based on modified screen-printed carbon electrodes (SPCE) in conjunction with chemometrics for wine discrimination and classification according to the varietal origin of the grapes. Three SPCEs modified with e nanomaterials such as single-walled carbon nanotubes (SWCNT), multi-walled carbon nanotubes (MWCNT), and polypyrrole (Ppy) doped with 1-decanesulfonic acid sodium salt (DSA) were used. Voltammetric signals obtained with individual sensors and jointed to form a sensor array were used as inputs to develop the qualitative and quantitative models employing principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). Generally, satisfactory results were obtained, with discrimination capabilities of 86.72% for SWCNT/SPCE, 92.31% for MWCNT/SPCE and 78.17% for Ppy/DSA/SPCE for the investigated white wine varieties. The proposed sensors array based on modified SPCEs can be used for wine discrimination with a reasonable accuracy (72.00%). The LDA discriminant model based on sensors array was able to classify the Sauvignon Blanc (100.00%), Columna (91.67%) and Riesling Italian (80.00%) wines with high degree of accuracy.