基于高光谱联合流化床富集的红葡萄酒白藜芦醇检测

发布时间：2018-11-18 07:30

【摘要】：利用高光谱成像技术联合流化床富集技术对红葡萄酒中微量成分白藜芦醇的含量进行快速检测。通过高效液相色谱法检测红葡萄酒中白藜芦醇的含量,设计流化床富集装置,研究HPD826、DA-201、AB-8、H103、HPD600共5种树脂对红葡萄酒中白藜芦醇的富集效果,筛选出H103树脂富集效果最优。将H103大孔吸附树脂用于富集红葡萄酒中的白藜芦醇。获取吸附白藜芦醇后树脂的高光谱反射图像(900~1 700 nm),对比5种光谱预处理方法(MSC、SNV、SG-S、RN、QN)对白藜芦醇含量的建模效果,优选出RN预处理方法;建立PLSR、SVMR(LK-SVMR、PKSVMR、RBF-SVMR和S-SVMR)、PCR的6种回归模型,优选出LK-SVMR校正模型和PLSR校正模型,将其用于预测集样本进一步评价模型的精度和稳定性,最终确定PLSR模型为最佳模型。研究表明,基于RN-PLSR的红葡萄酒中白藜芦醇的定量预测模型相关系数R_P=0.852 8,预测集均方根误差为0.036 0,R_C=0.878 3,校正集均方根误差为0.033 0,预测效果最佳,为高光谱技术在微量、痕量成分检测方面的应用提供了参考。
[Abstract]:The content of resveratrol in red wine was determined by hyperspectral imaging and fluidized bed enrichment. The content of resveratrol in red wine was determined by high performance liquid chromatography (HPLC). A fluidized bed enrichment device was designed to study the enrichment effect of resveratrol in red wine by HPD826,DA-201,AB-8,H103,HPD600. The best enrichment effect of H 103 resin was screened out. H103 macroporous adsorption resin was used to enrich resveratrol in red wine. The hyperspectral reflectance images of resveratrol resin (900 ~ 1 700 nm),) were obtained and compared with five spectral pretreatment methods (MSC,SNV,SG-S,RN,QN) to model resveratrol content, and the RN pretreatment method was selected. Six regression models of PLSR,SVMR (LK-SVMR,PKSVMR,RBF-SVMR and S-SVMR), PCR) are established, and LK-SVMR correction model and PLSR correction model are selected, which are used to further evaluate the accuracy and stability of the model. Finally, the PLSR model is determined as the best model. The results show that the correlation coefficient of quantitative prediction model of resveratrol in red wine based on RN-PLSR is R_P=0.852 8, the root mean square error of prediction set is 0.036? The prediction results are the best, which provides a reference for the application of hyperspectral technology in the determination of trace and trace components.
【作者单位】：宁夏大学农学院;
【基金】：国家自然科学基金项目(31401480)
【分类号】：O657.3;TS262.6

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