Rattanamato Benjamard, Kanha Nattapong, Thongchai Prem, Rakariyatham Kanyasiri, Klangpetch Wannaporn, Osiriphun Sukhuntha, Laokuldilok Thunnop
Faculty of Agro-Industry, Chiang Mai University, Chiang Mai 50100, Thailand.
Office of Research Administration, Chiang Mai University, Chiang Mai 50200, Thailand.
Foods. 2025 Apr 27;14(9):1528. doi: 10.3390/foods14091528.
This study aims to optimize the parameters for the ultrasound-assisted extraction of cellulose nanocrystals (CNCs) from scented pandan leaf waste and to enhance the properties of edible films reinforced with CNC. The CNC extraction conditions were optimized using response surface methodology (central composite design) by varying two independent variables, including amplitude (25.86% to 54.14%) and ultrasonication time (11.89 min to 33.11 min). The optimal extraction conditions were 50% amplitude and 30 min ultrasonication, providing CNCs with the highest extraction yield (29.85%), the smallest crystallite size (5.85 nm), and the highest crystallinity index (59.32%). The extracted CNCs showed favorable physicochemical properties, including a zeta potential of -33.95 mV, an average particle diameter of 91.81 nm, and a polydispersity index of 0.26. Moreover, sweet potato starch (SPS)-based films incorporating various CNC concentrations (0, 2, 4, 6, and 8%) were fabricated. Increasing CNC concentrations improved key film properties, including thickness, moisture content, water vapor permeability, tensile strength, light transmittance, and color. Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) analyses confirmed hydrogen bonding, crystallinity, and uniform CNC distribution within the film as CNC content increased. These findings highlight ultrasound-assisted extraction as an efficient method for producing high-quality CNCs from pandan leaf waste, offering sustainable nanofillers to enhance biodegradable edible films.
本研究旨在优化从香兰叶废料中超声辅助提取纤维素纳米晶体（CNCs）的参数，并增强用CNCs增强的可食用薄膜的性能。通过响应面法（中心复合设计），通过改变两个自变量，包括振幅（25.86%至54.14%）和超声处理时间（11.89分钟至33.11分钟），对CNC提取条件进行了优化。最佳提取条件为50%振幅和30分钟超声处理，可提供具有最高提取率（29.85%）、最小微晶尺寸（5.85纳米）和最高结晶度指数（59.32%）的CNCs。提取的CNCs表现出良好的物理化学性质，包括ζ电位为-33.95毫伏、平均粒径为91.81纳米和多分散指数为0.26。此外，制备了含有不同CNC浓度（0、2、4、6和8%）的基于甘薯淀粉（SPS）的薄膜。增加CNC浓度改善了关键的薄膜性能，包括厚度、水分含量、水蒸气透过率、拉伸强度、透光率和颜色。傅里叶变换红外光谱（FT-IR）、X射线衍射（XRD）和扫描电子显微镜（SEM）分析证实，随着CNC含量的增加，薄膜内存在氢键、结晶度和均匀的CNC分布。这些发现突出了超声辅助提取作为一种从香兰叶废料中生产高质量CNCs的有效方法，为增强可生物降解的可食用薄膜提供了可持续的纳米填料。
