School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu 212013, China.
School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu 212013, China; Institute of Food Physical Processing, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu 212013, China.
Ultrason Sonochem. 2022 Sep;89:106116. doi: 10.1016/j.ultsonch.2022.106116. Epub 2022 Aug 6.
This study aimed to investigate the impact of multi-mode S-type ultrasound treatment on the protein extraction level of walnut dregs. The structural properties of the walnut protein (WP) were characterized, and the correlation between protein structure and extraction level was analyzed. The in situ real-time monitoring model for the ultrasound-assisted WP extraction process was established by a miniature fiber near-infrared (NIR) spectrometer. Results showed that the protein yield, purity, and comprehensive extraction index (CEI) of extracted WP were 71.07 %, 72.69 %, and 71.72, respectively, under optimal conditions (dual-frequency 20/28 kHz, ultrasonic treatment duration 30 min, and ultrasound power density 120 W/L). The secondary structure of extracted WP displayed that the proportion of α-helix and β-sheet reduced, while the contents of β-turn and random coil increased after ultrasonic treatment. Besides, sonication decreased the disulfide bond content and increased free sulfhydryl (-SH) and surface hydrophobicity compared to the control. The microstructures of WP confirmed that appropriate sonication could unfold the protein aggregates and reduce the particle size. The extraction level of WP is positively correlated with the -SH content (p < 0.01). The quantitative prediction model of Si-PLS for -SH content in the ultrasound-assisted WP extraction process was established and performed a good correction and prediction performance (Rc = 0.9736; RMSECV = 0.446 μmol/L; Rp = 0.9342; RMSEP = 0.807 μmol/L). This study exploited a high-efficiency way for the WP extraction industry, and provided theoretical support for the development of the intelligent system in industrial protein extraction process.
本研究旨在探讨多模式 S 型超声处理对核桃渣中蛋白质提取水平的影响。对核桃蛋白（WP）的结构特性进行了表征，并分析了蛋白质结构与提取水平之间的相关性。通过微型光纤近红外（NIR）光谱仪建立了超声辅助 WP 提取过程的原位实时监测模型。结果表明，在最优条件下（双频 20/28 kHz、超声处理时间 30 min 和超声功率密度 120 W/L），提取 WP 的蛋白产率、纯度和综合提取指数（CEI）分别为 71.07%、72.69%和 71.72%。提取 WP 的二级结构显示，超声处理后α-螺旋和β-折叠的比例降低，而β-转角和无规卷曲的含量增加。此外，与对照组相比，超声处理降低了二硫键含量，增加了游离巯基（-SH）和表面疏水性。WP 的微观结构证实，适当的超声处理可以展开蛋白质聚集体并减小粒径。WP 的提取水平与 -SH 含量呈正相关（p < 0.01）。建立了 Si-PLS 对超声辅助 WP 提取过程中 -SH 含量的定量预测模型，具有良好的校正和预测性能（Rc = 0.9736；RMSECV = 0.446 μmol/L；Rp = 0.9342；RMSEP = 0.807 μmol/L）。本研究为 WP 提取工业探索了一种高效的方法，为工业蛋白质提取过程智能系统的发展提供了理论支持。
