基于CARS-SPA特征提取的黄水淀粉近红外光谱定量模型优化  

Optimization of Quantitative Modeling of Starch in Huangshui Based on Near-Infrared Spectral Feature Extraction Using Competitive Adaptive Reweighted Sampling Combined with Successive Projections Algorithm

作　　者：母雯竹 张贵宇 张维 姚瑞 付妮 MU Wenzhu;ZHANG Guiyu;ZHANG Wei;YAO Rui;FU Ni(Artificial Intelligence Key Laboratory of Sichuan Province,Sichuan University of Science&Engineering,Yibin 644005,China)

机构地区：[1]四川轻化工大学人工智能四川省重点实验室,四川宜宾644005

出　　处：《食品科学》2024年第19期8-14,共7页Food Science

基　　金：四川省科技计划项目(2022YFS0554);泸州老窖研究生创新基金项目(LJCX-2022-8);酿酒生物技术及应用四川省重点实验室开放课题(NJ2022-06);四川轻化工大学科技成果转化专项(HXJY01);五粮液产学研合作项目(CXY2022ZR007)。

摘　　要：为提高白酒固态发酵的副产物黄水中淀粉含量预测模型精度和建模效率。采用傅里叶变换近红外光谱仪采集黄水光谱信息,利用一阶导数对光谱进行预处理,并结合偏最小二乘回归(partial least squares regression,PLSR)建立黄水淀粉定量预测模型。使用决定系数(R^(2))和预测均方误差(root mean square error of prediction,RMSEP)评价模型性能。光谱中含有大量冗余信息,为有效提升黄水淀粉含量检测精度和优化模型效率,将不同特征提取方法的优点结合,发现使用竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)结合连续投影算法(successive projections algorithm,SPA)提取的光谱特征所建立的PLSR模型,相较于未使用特征提取或仅使用单一特征提取所建立的模型均有明显提升。在单一使用CARS时,模型的R^(2)为0.9654,RMSEP为0.2012%,而结合SPA后,R2为0.9738,RMSEP为0.1748%。此外,光谱维度从2203个减少到了126个,不仅提高了预测精度,也提升了建模效率。本研究提出的方法可作为黄水近红外定量模型优化的有效途径。In order to improve the accuracy and efficiency of predictive modeling of the starch content of Huangshui,a byproduct of Baijiu production by solid-state fermentation,spectral information of Huangshui was collected using a Fourier transform near-infrared(FTIR)spectrometer and preprocessed by first derivative.Based on the preprocessed spectra,a predictive model for the starch content of Huangshui was developed using partial least squares regression(PLSR),and its performance was evaluated by determination coefficient(R^(2))and root mean square error of prediction(RMSEP).As the original spectra contained a lot of redundant information,in order to effectively improve the detection accuracy and to optimize the modeling efficiency,the advantages of different feature extraction methods were combined.Finally,it was found that the PLSR model established by using the spectral features extracted by competitive adaptive reweighted sampling(CARS)combined with the successive projections algorithm(SPA)was significantly better than the model built without feature extraction or using single feature extraction.The results showed that the R^(2) and RMSEP of the model established using CARS were 0.9654 and 0.2012%,while those obtained using CARS-SPA were 0.9738 and 0.1748%,respectively.The spectral dimension reduced from 2203 to 126 after the combination of CARS with SPA,which improved both the prediction accuracy and the modeling efficiency.The method proposed in this study provides an effective means to optimize near-infrared spectral quantitative modeling of starch in Huangshui.

关 键 词：黄水 近红外光谱 竞争性自适应重加权算法 连续投影算法 偏最小二乘回归法 

分 类 号：TS207.3[轻工技术与工程—食品科学]