结合2D-COS和光谱融合技术的小麦淀粉回生特性定量表征

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1.203 4和2.069 0提高至3.980 9,回生度...

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

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 1.203 4 and 2.069 0 to 3.980 9 and from 2.594 0 and 2.109 9 to 4.576 3 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 8.095 9 and 14.183 6. 2D-COS can improve spectral resolution and obtain more chemical structure information than the model based on Competitive Adaptive Reweighted Sampling. Therefore, the spectral fusion technology combined with 2D-COS model has better results. The results show that it is feasible to use the 2D-COS to identify the characteristic wavelengths for starch retrogradation properties, which provides a new idea for the characteristic variables optimization of fusion spectra. Spectral fusion technology combined with 2D-COS can realize the rapid detection of starch retrograde, which provides a method for rapidly detecting starch food quality and shelf life.