Rebollo-Hernanz Miguel, Cañas Silvia, Taladrid Diego, Benítez Vanesa, Bartolomé Begoña, Aguilera Yolanda, Martín-Cabrejas María A
Department of Agricultural Chemistry and Food Science, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
Institute of Food Science Research, CIAL (UAM-CSIC), 28049 Madrid, Spain.
Foods. 2021 Mar 19;10(3):653. doi: 10.3390/foods10030653.
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. Under these conditions, experimental values for total phenolic compounds, flavonoids, flavanols, proanthocyanidins, phenolic acids, -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--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.
本研究旨在建立并优化从咖啡壳中提取酚类化合物的绿色可持续提取方法。采用响应面法（RSM）和人工神经网络（ANNs）对提取变量（温度、时间、酸度和固液比）对酚类化合物回收率的影响进行建模。所有响应均拟合到RSM和ANN模型，结果显示模型具有较高的估计能力。影响酚类提取的主要因素是温度，其次是固液比和酸度。最佳提取条件为100℃、90分钟、0%柠檬酸和0.02 g咖啡壳/mL。在此条件下，总酚类化合物、黄酮类化合物、黄烷醇、原花青素、酚酸、二酚和体外抗氧化能力的实验值与预测值相符，从而验证了该模型。通过超高效液相色谱-电喷雾串联质谱（UPLC-ESI-MS/MS）确认了绿原酸、原儿茶酸、咖啡酸、没食子酸和山奈酚-3- -半乳糖苷的存在。咖啡壳中的酚类水提取物可作为可持续的食品成分和营养产品。
