南瓜废料中生物活性化合物的包封：多目标优化研究。
Encapsulation of bioactive compounds extracted from pumpkin waste: the multi-objective optimisation study.
机构信息
Faculty of Technology, University of Novi Sad, Novi Sad, Serbia.
Institute of General and Physical Chemistry, University of Belgrade, Belgrade, Serbia.
出版信息
J Microencapsul. 2022 Jun;39(4):380-393. doi: 10.1080/02652048.2022.2094485. Epub 2022 Jul 5.
AIM
Artificial neural network (ANN) development to find optimal carriers (pea protein-P, maltodextrin-M, and inulin-I) mixture for encapsulation of pumpkin waste bioactive (β-carotene and phenolics).
METHODS
Freeze-drying encapsulation and encapsulates characterisation in terms of bioactive contents and encapsulation efficiencies, water activity, hygroscopicity, densities, flowability, cohesiveness, particle size (laser diffraction), solubility, colour (CIELab), morphological (SEM), stability and release properties.
RESULTS
Optimal encapsulates, OE-T (with highest total bioactive contents; P, M, and I of 53.9, 46.1, and 0%w/w) and OE-EE (with highest bioactive encapsulation efficiencies; P, M, and I of 45.5, 32.0, and 22.5%w/w) had particle diameters of 94.561 ± 1.341 µm and 90.206 ± 0.571 µm, the span of 1.777 ± 0.094 and 1.588 ± 0.089, highest release at pH 7.4 of phenolics of 71.03%w/w after 72 h and 66.22%w/w after 48 h, and β-carotene of 43.67%w/w after 8 h and 48.62%w/w after 6 h, respectively.
CONCLUSION
ANN model for prediction of encapsulates' preparation, showed good anticipation properties (with gained determination coefficients of 1.000).
目的
利用人工神经网络（ANN）开发寻找最佳载体（豌豆蛋白-P、麦芽糊精-M 和菊粉-I）混合物，用于包封南瓜废弃物生物活性物质（β-胡萝卜素和酚类化合物）。
方法
采用冷冻干燥包埋法，并从生物活性物质含量和包埋效率、水分活度、吸湿性、密度、流动性、内聚性、粒径（激光衍射）、溶解度、颜色（CIELab）、形态（SEM）、稳定性和释放性能等方面对包埋物进行了表征。
结果
最佳包埋物 OE-T（总生物活性含量最高；P、M 和 I 的含量分别为 53.9%、46.1%和 0%w/w）和 OE-EE（生物活性包埋效率最高；P、M 和 I 的含量分别为 45.5%、32.0%和 22.5%w/w）的粒径分别为 94.561±1.341μm 和 90.206±0.571μm，跨度分别为 1.777±0.094 和 1.588±0.089，在 pH 7.4 下，72 小时后酚类化合物的释放率最高，达到 71.03%w/w，48 小时后达到 66.22%w/w，8 小时后β-胡萝卜素的释放率最高，达到 43.67%w/w，6 小时后达到 48.62%w/w。
结论
ANN 模型可用于预测包埋物的制备，具有良好的预测性能（获得的决定系数为 1.000）。
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