02004naa a2200205 a 450000100080000000500110000800800410001902400360006010000200009624501460011626000090026252013570027165000170162865300240164565300160166965300210168570000190170670000220172577300510174721353562021-10-15       2021    bl uuuu     u00u1 u    #d7 a10.1007/s12161-021-02144-82DOI1 aLIMA, M. dos S.  aArtifcial neural networkba powerful tool in associating phenolic compounds with antioxidant activity of grape juices.h[electronic resource]  c2021  aIn vitro techniques are essential to assess the antioxidant potential of foods, although methods with diferent action mechanisms make troublesome data analysis. This article describes the use of artifcial neural network (ANN) to associate phenolic compounds with antioxidant activity in vitro (AOX) of grape juices. A multilayer perceptron (MLP) ANN was obtained with 28 phenolics quantifed, as input layers, and AOX measuring by DPPH, ABTS, FRAP, H2O2, and β-carotene/linoleic acid bleaching assay (βCLA) methods, as output layers. To improve discussion in food sciences, the ANN results were compared with Pearson?s correlation and principal component analysis (PCA), methods largely used in food studies. Pearson?s technique showed correlations between antioxidant methods and some of the phenolic compounds, but with limitations. PCA proved to be a more powerful method than Pearson?s correlation, as it positively associated 13 phenolics with four out of fve antioxidant methods. The MLP-ANN allowed simultaneous association of 19 individual phenolics, while a single hidden layer predicted 15 phenolics with simultaneous action in all AOX methods. The power of association was: ANN&gtPCA&gtPearson. It was evidenced that ANN is a powerful tool for screening antioxidants in diferent AOX systems, which is applicable in health interests.  aChemometrics  aAntioxidant methods  aBioactivity  aGrape polyphenol1 aPEREIRA, G. E.1 aFEDRIGO, I. M. T.  tFood Analytical Methods, 14 oct. 2021. Online.