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Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity
Food Hydrocolloids ( IF 11.0 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.foodhyd.2020.106161
Carlos Sabater , Ana Blanco-Doval , Antonia Montilla , Nieves Corzo

Abstract An enzymatic procedure to obtain modified artichoke pectin and pectic oligosaccharides (POS) (Mw 100–0.3 kDa) has been optimised through an experimental design analysed by artificial neural networks (ANN; R2 0.99), leading to high yields of these products (65.9 ± 2.1 mg 100 mg−1 pectin) at optimal conditions (pH 4.41, reaction time 0.9 h, enzyme dose 17.1 U g−1 pectin), reaching a maximum theoretical desirability of 0.98. Desirability function, variable importance and sensitivity analysis were performed to interpret ANN while residual analysis demonstrated its high predictive power. Hydrolysates were purified by ultrafiltration and retentate and permeate fractions were characterised by MALDI-TOF-MS. Oligosaccharides from di- to hexasaccharides corresponding to galacturonic acid (GalA) oligomers that may be attached to neutral sugars and ferulic acid were determined, and their potential free radical scavenger activity was calculated using an in silico model (72–98% probability). The presence of specific structures in permeate (high free GalA content, GalA oligomers attached to xylose, ferulic acid or rhamnose and arabinose) and retentate fractions explained differences observed in their in vitro antioxidant activities (135.6 and 32.1 μmol Trolox g−1, respectively). The combination of in silico and in vitro methods allows establishing structure-activity relationships for modified pectin and POS fractions.

中文翻译:

使用人工神经网络工具优化获得改性朝鲜蓟果胶和果胶寡糖的酶法。抗氧化活性的计算机模拟和体外评估

摘要 通过人工神经网络 (ANN; R2 0.99) 分析的实验设计优化了获得改性朝鲜蓟果胶和果胶寡糖 (POS) (Mw 100–0.3 kDa) 的酶促程序,导致这些产品的高产率 (65.9 ± 2.1 mg 100 mg-1 果胶)在最佳条件下(pH 4.41,反应时间 0.9 小时,酶剂量 17.1 U g-1 果胶),达到 0.98 的最大理论合意性。进行了意愿函数、变量重要性和敏感性分析来解释人工神经网络,而残差分析则证明了其高预测能力。水解物通过超滤纯化,滞留物和渗透物部分通过 MALDI-TOF-MS 进行表征。测定了与半乳糖醛酸 (GalA) 低聚物相对应的二糖到六糖的寡糖,这些寡糖可能附着在中性糖和阿魏酸上,并使用计算机模型计算其潜在的自由基清除剂活性(概率为 72-98%)。渗透液中特定结构的存在(高游离 GalA 含量,GalA 低聚物与木糖、阿魏酸或鼠李糖和阿拉伯糖相连)和滞留物部分解释了在其体外抗氧化活性中观察到的差异(分别为 135.6 和 32.1 μmol Trolox g-1) . 计算机模拟和体外方法的结合允许建立改性果胶和 POS 级分的构效关系。渗透液中特定结构的存在(高游离 GalA 含量,GalA 低聚物与木糖、阿魏酸或鼠李糖和阿拉伯糖相连)和滞留物部分解释了在其体外抗氧化活性中观察到的差异(分别为 135.6 和 32.1 μmol Trolox g-1) . 计算机模拟和体外方法的结合允许建立改性果胶和 POS 级分的构效关系。渗透液中特定结构的存在(高游离 GalA 含量,GalA 低聚物与木糖、阿魏酸或鼠李糖和阿拉伯糖相连)和滞留物部分解释了在其体外抗氧化活性中观察到的差异(分别为 135.6 和 32.1 μmol Trolox g-1) . 计算机模拟和体外方法的结合允许建立改性果胶和 POS 级分的构效关系。
更新日期:2021-01-01
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