Ultrasound-assisted extraction of pectin from artichoke by-products. An artificial neural network approach to pectin characterisation

Abstract Artichoke (Cynara scolymus L.) by-products can be used as a good source of pectin. The aim of this work was to compare different pectin extraction methods: power ultrasound (US), enzymes, combination of US and enzymes (US + E), and acids (nitric and sodium citrate). After 6 h, pectin yield was higher when US was applied in combination with Celluclast®1.5 L (up to 13.9%). Structural characterisation showed that US-extracted pectins had lower weight-average molecular weight (Mw) values (146–155 kDa) than pectin extracted with US + E (160–267 kDa) and acid-extracted pectin (329–352 kDa). Monomeric composition reflected that pectin extracted with acids had the highest galacturonic acid (GalA) contents (82.2–90.2%) and the lowest degree of branching [Rha/GalA] (0.026–0.031). Structural characteristics of the different pectins were modelled using two artificial neural networks (ANN) considering composition parameters (Model I) and pectin FT-IR spectra (Model II). In addition, a third ANN was built to determine differences and similarities in the GC-MS spectra of monomeric composition (identified and unidentified monosaccharides) (Model III) showing characteristic patterns with high accuracy rates (above 95% on the test set). Structural differences depending on the extraction method of pectin have been established and these models could be applied to pectin from other sources.