结合2D-COS和光谱融合技术的小麦淀粉回生特性定量表征  

Quantitative Characterization of Wheat Starch Retrogradation by Combining 2D-COS and Spectral Fusion

作　　者：安焕炯 翟晨 马倩云 张凡 王书雅 孙剑锋[1] 王文秀 AN Huan-jiong;ZHAI Chen;MA Qian-yun;ZHANG Fan;WANG Shu-ya;SUN Jian-feng;WANG Wen-xiu(College of Food Science and Technology,Hebei Agricultural University,Baoding 071000,China;Nutrition and Health Research Institute COFCO Corporation,Beijing Key Laboratory of Nutrition and Health and Food Safety,Beijing 102209,China)

机构地区：[1]河北农业大学食品科技学院,河北保定071000 [2]中粮营养健康研究院,营养健康与食品安全北京市重点实验室,北京102209

出　　处：《光谱学与光谱分析》2023年第1期162-168,共7页Spectroscopy and Spectral Analysis

基　　金：河北农业大学引进人才科研专项(YJ201950);国家“十三五”重点研发计划项目(2016YFD0401204)资助。

摘　　要：回生是淀粉加工、运输和储藏过程中的重要理化性质,快速检测淀粉回生程度对淀粉制品的品质和保质期有重要意义。为了探究二维相关光谱法(2D-COS)优选回生淀粉特征变量的可行性,研究结合2D-COS和光谱融合技术对小麦淀粉的回生特性进行定量表征。首先,将不同回生时间的小麦淀粉测定结晶度和回生度,从淀粉体系中晶体含量和对淀粉酶水解抗性的角度表征淀粉回生特性。然后,分别采集样品的近红外和中红外光谱数据,对采集的原始光谱进行Savitzky-Golay平滑和标准正态变量变换预处理后,结合偏最小二乘法分别基于近红外光谱、中红外光谱和融合光谱构建全光谱的预测模型。在此基础上,以回生天数为外部扰动,分别选取回生0,1,2,3,5,7,10,14,21和35 d的10条淀粉光谱进行2D-COS分析。通过分析同步谱和自相关谱,辨识了近红外13个和中红外11个与回生特性有关的特征波长。最后,基于这些特征波长进一步建立回生度和结晶度的预测模型。结果表明,全光谱模型结果中,光谱融合后的模型预测效果较好,结晶度模型的相对分析误差(RPD)值由1.2034和2.0690提高至3.9809,回生度模型的RPD值由2.5940和2.1099提高至4.5763,表明光谱融合能提高模型性能。利用2D-COS筛选特征波长后建立的模型预测效果有大幅度提高,结晶度模型的RPD值提高至8.0959,回生度模型的RPD值提高至14.1836。与利用竞争性自适应重加权算法筛选特征波长建立的模型结果相比,2D-COS更能提高光谱分辨率,获得更多的化学结构信息,因此光谱融合技术结合2D-COS的模型结果更佳。研究结果表明,将2D-COS用于筛选与淀粉回生特性有关的特征波长是可行的,为融合光谱的特征变量优选提供了新思路;同时也表明光谱融合技术结合2D-COS可以实现淀粉回生程度的快速检测,为淀粉食品的质量和保质期的快速检测提供了方法支持。Retrogradation is an important physicochemical property of starch during processing,transportation and storage.Rapid detection of retrogradation is of great significance to starch products’quality and shelf life.In order to investigate the feasibility of selecting the characteristic variables of retrograde starch by two-dimensional correlation spectroscopy(2D-COS),spectral fusion technology and 2D-COS was combined to quantitatively characterize the retrogradation characteristics of wheat starch in this study.First,wheat starch’s crystallinity and retrogradation degree at different retrograde times were measured.The retrograde properties of starch were characterized by crystal content in the starch system and resistance to amylase hydrolysis.Then,the samples’near-infrared and mid-infrared spectral data were collected respectively.After spectral pretreatment,prediction models based on near-infrared,mid-infrared,-and fusion spectra were established using partial least squares analysis.On this basis,the retrogradation day was used as the external disturbance.Starch spectra of 0,1,2,3,5,7,10,14,21 and 35 days were selected for 2D-COS analysis.By analyzing the synchronization and autocorrelation spectrum,13 and 11 feature variables related to starch retrogradation characteristics were identified from near-infrared and mid-infrared spectra,respectively.Finally,prediction models for retrogradation degree and crystallinity were established based on these variables.The results show that the models based on full-spectra yielded better prediction performance after spectral fusion,with relative percent deviation(RPD)increasing from 1.2034 and 2.0690 to 3.9809 and from 2.5940 and 2.1099 to 4.5763 for crystallinity and retrogradation degree.Using the feature spectra obtained by 2D-COS analysis,the RPD values for the crystallinity model and retrogradation degree model increased to 8.0959 and 14.1836.2D-COS can improve spectral resolution and obtain more chemical structure information than the model based on Competitive Ada

关 键 词：淀粉 回生特性 光谱融合技术 二维相关光谱 

分 类 号：O657.33[理学—分析化学]