Effects of an alternative bleaching method for yerba mate leaves on total phenolic content: evaluation and prediction with soft-computing approaches.

Abstract

The effects of an alternative bleaching method on the total phenolic content (TPC) and antioxidant activity (AA) of yerba mate extracts were evaluated. Traditional bleaching ('zapeco') was compared with submerging the leaves in water followed by a hot air oven-drying process. Seven different approaches, i.e. linear model (LM), response surface model (RSM), Mamdani, Larsen, adaptive neuro-fuzzy inference system (ANFIS) with the product (Prod) and the minimum (Min) operators, and ANFIS with automatically membership functions (Auto), were employed to compare the TPC of yerba mate extracts based on drying temperature and AA assays. The results showed that if leaves were bleached followed by drying at higher temperatures, we obtained higher AA and TPC values. For submerging bleaching treatment, RSM model delivered the best accuracy measures with a mean absolute error (MAE), average absolute percentage error (MAPE), and mean squared error (MSE) of 0.128, 0.006, and 0.028, respectively. The ANFIS Auto model was the best for traditional bleaching treatment, with MAE, MAPE, and MSE of 0.490, 0.013, and 0.612, respectively. The results suggest a second-order linear relation between drying temperature, AA assays to TPC, and a high level of relation complexity of drying temperature, AA assays, and TPC. The evaluated soft-computing approaches have the excellent ability to estimate TPC from bleached leaves. © 2022 Society of Chemical Industry.