基于二维相关红外光谱对pH值影响大豆分离蛋白二级结构含量的快速分析  

Two-Dimensional Correlation Infrared Spectroscopy for Rapid Analysis of the Effect of pH on the Secondary Structure Content of Soybean Protein Isolate

作　　者：刘畅[1] 吴丹丹 王宁 王睿莹 王立琦 刘峰[3] 于殿宇 LIU Chang;WU Dandan;WANG Ning;WANG Ruiying;WANG Liqi;LIU Feng;YU Dianyu(College of Food Engineering,Harbin University of Commerce,Harbin 150028,China;School of Computer and Information Engineering,Harbin University of Commerce,Harbin 150028,China;Shandong Yuxin Bio-Tech Co.Ltd.,Binzhou 256500,China;School of Food Science,Northeast Agricultural University,Harbin 150030,China)

机构地区：[1]哈尔滨商业大学食品工程学院,黑龙江哈尔滨150028 [2]哈尔滨商业大学计算机与信息工程学院,黑龙江哈尔滨150028 [3]山东御馨生物科技股份有限公司,山东滨州256500 [4]东北农业大学食品学院,黑龙江哈尔滨150030

出　　处：《食品科学》2024年第17期26-34,共9页Food Science

基　　金：“十四五”国家重点研发计划重点专项(2021YFD2100401)。

摘　　要：为满足不同种类食品对大豆分离蛋白(soybean protein isolate,SPI)不同功能性的需求,本研究利用红外光谱快速采集70组不同pH值处理后SPI的数据,探讨pH值变化对SPI结构含量的影响。使用均值中心化、多元散射校正、标准正态变量变换和归一化算法对红外光谱数据进行预处理,基于二维相关红外光谱提取特征波段,再利用偏最小二乘(partial least square,PLS)法和算术优化算法-随机森林(arithmetic optimization algorithm-random forests,AOA-RF)建立不同pH值条件下SPI结构及含量的预测模型。结果表明,经均值中心化和多元散射校正结合处理后,α-螺旋、β-折叠、β-转角和无规卷曲模型的相对标准偏差分别为1.29%、1.60%、1.37%、7.28%,两者结合对光谱数据的预处理效果最佳。预测α-螺旋和β-折叠含量最优模型为AOA-RF(特征波段),校正集决定系数为0.9350和0.9266,预测集决定系数为0.8568和0.8701;预测β-转角和无规卷曲含量最优模型为PLS(特征波段),校正集决定系数为0.9154和0.8817,预测集决定系数为0.8913和0.7843。本研究结果可为工业生产过程中产品质量快速检测和工艺条件控制提供理论支撑。To meet the different functional needs for soybean protein isolate(SPI)in different food applications,this study utilized infrared spectroscopy to rapidly analyze 70 SPI samples subjected to different pH treatments,and explored the effect of pH changes on the secondary structure content of SPI.Mean centralization(MC),multivariate scattering correction(MSC),standard normal variate transformation,and normalization were used for infrared data preprocessing.Feature wavebands were identified based on two-dimensional correlation infrared spectra,and predictive modeling of the secondary structure content of SPI against pH was performed using partial least squares(PLS)and arithmetic optimization algorithm-random forests(AOA-RF).The results showed that the relative standard deviations of theα-helix,β-sheet,β-turn,and random coil prediction models developed by the combined use of MC and MSC were 1.29%,1.60%,1.37%,and 7.28%,respectively,indicating their combination to be the best spectral pre-processing method.The optimal model for predictingα-helix andβ-sheet contents was AOA-RF(characteristic wavebands),with calibration determination coefficients of 0.9350 and 0.9266 and prediction determination coefficients of 0.8568 and 0.8701,respectively.The optimal model for predictingβ-turn and random coil contents was PLS(characteristic wavebands),with calibration determination coefficients of 0.9154 and 0.8817 and prediction determination coefficients of 0.8913 and 0.7843,respectively.The results of this study provide a theoretical basis for product quality detection and processing condition control in industrial settings.

关 键 词：二维相关红外光谱 大豆分离蛋白 二级结构 PH值变化 预测模型 快速分析 

分 类 号：TS214.2[轻工技术与工程—粮食、油脂及植物蛋白工程]