Application of multivariate regression and artificial neural network modelling for prediction of physical and chemical properties of medicinal plants aqueous extracts

• Published in: Journal of applied research on medicinal and aromatic plants Vol. 16; p. 100229
• Main Authors: TUŠEK, Ana JURINJAK, JURINA, Tamara, BENKOVIĆ, Maja, VALINGER, Davor, BELŠČAK-CVITANOVIĆ, Ana, KLJUSURIĆ, Jasenka GAJDOŠ
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.03.2020
• Subjects: Artificial neural network; Medicinal plants aqueous extracts; Multivariate regression; Medicinal plants aqueous extracts; Multivariate regression; Artificial neural network
• ISSN: 2214-7861; 2214-7861
• DOI: 10.1016/j.jarmap.2019.100229

•Multivariate regressions and ANN were used for prediction of aqueous extracts properties.•Piecewise regression was efficient for prediction of TPC.•ANN modelling was efficient for simultaneous predictions of TDS, EY, TPC and AOX. In recent years, multivariate modelling techniques have been employed with the aim of analysing, describing, and generally interpreting multidimensional data obtained from experiments. The objective of this study was to evaluate the applicability of multiple linear regression, nonlinear regression, piecewise linear regression, and artificial neural network modelling for the prediction of the physical properties (total dissolved solids, extraction yield), and chemical properties (total phenolic content and antioxidant activity) of the aqueous extracts of nine medicinal plants (dandelion, camomile, lavender, lemon balm, marigold, mint, nettle, plantain, and yarrow), prepared in dynamic experiments based on the extraction conditions (time and temperature), and plant species. Results indicated that simple multivariate regression models could be used for prediction of physical and chemical properties of medicinal plants aqueous extracts (the highest R2 were obtained for total phenolic content), while the artificial neural network proved a very effective tool (R2 > 0.9) for simultaneous prediction of both physical and chemical properties of medicinal plants aqueous extracts.