食品科学2017，Vol.38Issue(24)：87-93,7.DOI:10.7506/spkx1002-6630-201724014
红葡萄酒中白藜芦醇含量的高光谱快速检测算法优化
Algorithm Optimization for Fast Detection of Resveratrol Content in Wine by Hyperspectral Imaging
摘要
Abstract
In this experiment, fluidized-bed enrichment of trace resveratrol in red wine was carried out by macroporous resin adsorption and hyperspectral images of the resin samples were acquired. The prediction models established using various spectral pretreatments were compared for obtaining the optimal algorithm. The results showed that the partial least squares regression (PLSR) model established by removing abnormal samples using Hotelling T2test method, dividing the sample sets using the KS algorithm, and pretreating the spectral data using standard normal variate (SNV) method exhibited the best prediction performance with correlation coefficient of correction (Rc2), root mean square error of calibration (RMSEC), correlation coefficient of prediction (Rp2) and root mean square error of prediction (RMSEP) of 0.813 8, 0.047 7, 0.782 4, and 0.050 2, respectively. Hyperspectral imaging can provide a new method for detecting trace components.关键词
高光谱技术/白藜芦醇/流化床/富集/算法优化Key words
hyperspectral technology/resveratrol/fluidized bed/enrichment/algorithm optimization分类
农业科技引用本文复制引用
房盟盟,刘贵珊,何建国,冯愈钦,郭红艳,丁佳兴,杨晓玉..红葡萄酒中白藜芦醇含量的高光谱快速检测算法优化[J].食品科学,2017,38(24):87-93,7.基金项目
国家自然科学基金青年科学基金项目(31401480) （31401480）
中央财政支持地方高校改革发展资金——食品学科建设项目(2017) （2017）
