食品科学 ›› 2017, Vol. 38 ›› Issue (24): 87-93.doi: 10.7506/spkx1002-6630-201724014

• 成分分析 • 上一篇    下一篇

红葡萄酒中白藜芦醇含量的高光谱快速检测算法优化

房盟盟,刘贵珊,何建国,冯愈钦,郭红艳,丁佳兴,杨晓玉   

  1. (宁夏大学农学院,农产品无损检测实验室,宁夏?银川 750021)
  • 出版日期:2017-12-25 发布日期:2017-12-07
  • 基金资助:
    国家自然科学基金青年科学基金项目(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)

Algorithm Optimization for Fast Detection of Resveratrol Content in Wine by Hyperspectral Imaging

FANG Mengmeng, LIU Guishan, HE Jianguo, FENG Yuqin, GUO Hongyan, DING Jiaxing, YANG Xiaoyu   

  1. (Non-Destructive Detection Laboratory of Agricultural Products, School of Agriculture, Ningxia University, Yinchuan 750021, China)
  • Online:2017-12-25 Published:2017-12-07

摘要: 将高光谱技术与流化床富集技术相结合,用大孔吸附树脂对干红葡萄酒中的微量白藜芦醇吸附后,采集光谱图像,通过比对多种预处理方法对建模效果的影响进而优选算法。结果表明,采用霍特林T2统计检测方法剔除异常样本,KS算法划分白藜芦醇含量样本集,标准正态变换法预处理光谱数据,建立的标准正态变换-偏最小二乘回归模型预测效果最优,其校正相关系数Rc2为0.813?8,校正均方根误差为0.047?7,预测相关系数Rp2为0.782?4,预测均方根误差为0.050?2,为白藜芦醇的高光谱痕量检测提供理论参考。

关键词: 高光谱技术, 白藜芦醇, 流化床, 富集, 算法优化

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 T2 test 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.

Key words: hyperspectral technology, resveratrol, fluidized bed, enrichment, algorithm optimization

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