基于近红外光谱技术的小麦专用粉品质特性快速检测研究
Rapid detection of wheat special flour quality characteristics based on near-infrared spectroscopy technology
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摘要: 基于近红外光谱技术,结合不同预处理和特征波长筛选方法,构建小麦专用粉的破损淀粉含量、降落数值、吸水率、稳定时间、拉伸面积、延伸度和最大拉伸阻力的偏最小二乘(Partial Least Squares,PLS)预测模型和总体预测模型,并对模型的预测能力进行评估。结果表明:去线性趋势(Detrend,DT)是破损淀粉含量和吸水率预测模型的最佳预处理方法,Savitzky-Gloay (SG)卷积平滑是降落数值和拉伸面积预测模型的最佳预处理方法,标准正态变量变换(Standard Normal Variable Transformation,SNV)是延伸度和最大拉伸阻力预测模型的最佳预处理方法。竞争性自适应重加权法(Competitive Adaptive Reweighted Sampling,CARS)可有效提高破损淀粉含量、降落数值、吸水率、拉伸面积和最大拉伸阻力预测模型的预测精度,预测决定系数分别为0.964 1、0.714 0、0.975 5、0.943 4和0.828 3;连续投影算法(Successive Projections Algorithm,SPA)可有效提高稳定时间和延伸度预测模型的效果,预测决定系数分别为0.713 5和0.953 0。总体预测模型对稳定时间、拉伸面积和最大拉伸阻力的预测效果均有所提升,剩余预测偏差(Residual Predictive Deviation,RPD)分别从1.86、4.27和2.51提升到2.43、5.26和3.11。综上可知,近红外光谱技术对小麦专用粉品质特性的无损快速检测是有效的、可行的。Abstract: Based on near-infrared spectroscopy technology, combined with different preprocessing and characteristic wavelength screening methods, partial least squares(PLS)prediction models and overall prediction model were established for indicators such as damaged starch content, falling number, water absorption rate, stability time, stretching area, extensibility and maximum resistance. The results showed that detrend (DT) was the best preprocessing method for the prediction model of damaged starch content and water absorption rate, savitzky-gloay(SG) convolutional smoothing was the best preprocessing method for the prediction model of falling number and stretching area, and standard normal variable transformation (SNV) was the best preprocessing method for the prediction model of extensibility and maximum resistance. Competitive adaptive reweighted sampling (CARS) could effectively improve the prediction accuracy of models for damaged starch content, falling number, water absorption rate, stretching area and maximum resistance, with prediction determination coefficients of 0.964 1, 0.714 0, 0.975 5, 0.943 4 and 0.828 3, respectively, successive projections algorithm (SPA) had improved the performance of stability time and extensibility prediction models, with prediction determination coefficients of 0.713 5 and 0.953 0, respectively. The overall prediction model had improved its predictive performance for stability time, stretching area and maximum resistance, their residual predictive deviation increased from 1.86, 4.27 and 2.51 to 2.43, 5.26 and 3.11, respectively. In summary, near-infrared spectroscopy technology was effective and feasible for a non-destructive and rapid detection of the quality characteristics of wheat flour.
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