Extraction of phenolic compounds from lucuma (Pouteria lucuma) seeds with natural deep eutectic solvents: modelling using response surface methodology and artificial neural networks - Puma-Isuiza, Gustavo; García-Chacón, Juliana María; Osorio, Coralia; Betalleluz-Pallardel, Indira; Chue, Jorge; Inga, Marianela (Frontiers in Sustainable Food Systems, 2024)
The present study aimed to extract polyphenolic compounds from lucuma (Pouteria lucuma) seeds using natural deep eutectic solvents (NADES) as a green, efficient, and environmentally friendly extraction. This was optimized by using the Response Surface Method (RSM) and comparing its predictive capacity with Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). Four NADES were prepared by mixing lactic acid (LA) with each of the following reagents: sodium acetate (SA), urea (U), glucose (G), and ammonium acetate (AA), separately. The yield of total phenolic compounds (TPC) obtained from lucuma seeds with each NADES was measured as an optimization criterion with the Box-Benhken design. The following factors were evaluated: time, temperature, and the lucuma seed flour (LSF): NADES ratio. The response variables were TPC and antioxidant activity. The LA-AA extract was selected because it exhibited the highest TPC value and was analyzed by UHPLC–MS (Ultra-performance Liquid Chromatography-Mass Spectrometry). From the RSM, the optimal extraction parameters were 80 min, 52°C, and LSF: NADES ratio of 8:100 (w/v), obtaining a TPC value of 3601.51 ± 0.51 mg GAE/100 g LFS. UHPLC–MS analysis evidenced the formation of epigallocatechin isomers from epigallocatechin gallate. The predictive ability of ANNs compared to RSM was demonstrated.