Abstract
This study employed both response surface methodology (RSM) and artificial neural network (ANN) to optimize the technological conditions for citric acid-assisted extraction of bioactive components from Lonicera caerulea pomace. We optimized the contents of polyphenols and crude polysaccharides in the extract based on extraction volume, temperature, and time. The ANN model exhibited superior predictability and accuracy compared to the RSM model. Using citric acid solution with a pH of 2 as the extraction agent, the optimal conditions were a solid-to-liquid ratio of 1:36, an extraction temperature of 63 °C, and an extraction time of 4 h. Scanning electron microscopy (SEM) showed that citric acid-assisted extraction destroyed the surface microstructure of Lonicera caerulea pomace, facilitating the release of bound substances. Furthermore, a storage stability experiment demonstrated that retention rates of polyphenols, polysaccharides, anthocyanins, flavonoids, and antioxidant capacity in powdered Lonicera caerulea pomace extract remained above 90% after 4 weeks at 4 °C in the dark. Anthocyanin, a primary polyphenol in Lonicera caerulea, correlated significantly with antioxidant activity, as confirmed by PCA results.
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