Revalorization of Coffee Husk: Modeling and Optimizing the Green Sustainable Extraction of Phenolic Compounds

Abstract

This study aimed to model and optimize a green sustainable extraction method of phenolic compounds from the coffee husk. Response surface methodology (RSM) and artificial neural networks (ANNs) were used to model the impact of extraction variables (temperature, time, acidity, and solid-to-liquid ratio) on the recovery of phenolic compounds. All responses were fitted to the RSM and ANN model, which revealed high estimation capabilities. The main factors affecting phenolic extraction were temperature, followed by solid-to-liquid ratio, and acidity. The optimal extraction conditions were 100 °C, 90 min, 0% citric acid, and 0.02 g coffee husk mL^-1. Under these conditions, experimental values for total phenolic compounds, flavonoids, flavanols, proanthocyanidins, phenolic acids, o-diphenols, and in vitro antioxidant capacity matched with predicted ones, therefore, validating the model. The presence of chlorogenic, protocatechuic, caffeic, and gallic acids and kaemferol-3-O-galactoside was confirmed by UPLC-ESI-MS/MS. The phenolic aqueous extracts from the coffee husk could be used as sustainable food ingredients and nutraceutical products.

Keywords: antioxidant capacity; artificial neural networks; coffee by-products; phenolic compounds; response surface methodology.

Figure 1

Coffee cherry anatomy (A) from the outside to the inside (skin, pulp, mucilage, parchment (comprising the coffee husk), silverskin, and coffee bean) and appearance of dried coffee husk once separated from the coffee bean (B).

Figure 2

Representative 3D plots illustrating the behavior of total phenolic compounds (TPC) extraction (A). Responses (mg g⁻¹) are graphed against two paired variables: T (temperature in °C), acidity (% citric acid), t (time in min) and S/L (solid:liquid ratio in g mL⁻¹); the topology of the multilayer feed-forward neural network for TPC (B), and scatter plot between the experimental and predicted yield by artificial neural networks (ANNs) for training, validation, testing, and overall data fitting for TPC (C).

Figure 3

Superimposed chromatograms of the extract at optimal conditions, free, and bound phenolics and the chemical structures of gallic, protocatechuic, chlorogenic, caffeic acids and kaempferol-3-O-galactoside, major phenolic compounds found in the coffee husk (A), biplot (scores of samples and load factors of each variable) of the principal component analysis (PCA) (B), Variable importance in projection (VIP) scores from partial least squares analysis (PLSA) (C), agglomerative hierarchical cluster analysis coupled to heatmap (from the lowest () to the highest () value for each parameter) (D) showing the associations among the measured parameters and classifying phenolic extracts from coffee husk according to them, and the ten most significant coefficients from principal components regression, PCR (E) and principal least squares regression, PLS-R (F). Circles in different colors indicate minor phenolic or phenolic family, green (), major phenolic, blue ().

Figure 4

Heatmap depicting the Pearson correlation coefficients from the associations among in vitro determinations of phenolic families and the different phenolic compounds quantified using UPLC-ESI-MS/MS.