|本期目录/Table of Contents|

奶粉掺假拉曼光谱成像检测新方法(PDF)

《纳米技术与精密工程》[ISSN:1672-6030/CN:12-1351/O3]

期数:
2017年1期
页码:
26-30
栏目:
精密测量
出版日期:
2017-01-15

文章信息/Info

Title:
A Novel Raman Imaging Methodology for Detection of Adulterated Milk Powder
作者:
陈达 黄志轩 韩汐 李奇峰
天津大学精密仪器与光电子工程学院,天津300072
Author(s):
Chen Da Huang Zhixuan Han Xi Li Qifeng
School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
关键词:
拉曼成像 奶粉掺假 数据驱动模型 多尺度
Keywords:
Raman imaging adulterated milk powder data driven model multiscale
分类号:
R460.4035
DOI:
10.13494/j.npe.20150159
文献标识码:
A
摘要:
本文采用拉曼光谱成像技术(Raman imaging)对掺杂奶粉进行快速检测,该技术有机结合了小尺度微区扫描和大尺度面积筛选功能,可高效采集奶粉样品信息.为了从复杂、变动的拉曼成像信号中准确识别掺假物质,本文发展了一种数据驱动的多尺度建模方法,以局部逼近的方式针对不同奶粉样品体系构建最佳小波基,自适应地提取拉曼成像信号中的掺假物质信息并加以甄别,最终以数据融合的方式建立多尺度模型.由此构建的光谱鉴定模型对奶粉掺假具有良好的识别能力,并有效避免了信息丢失.计算结果表明,该模型对未知样品的判别正确率为989%,可以半定量估计掺杂物浓度,为奶粉掺假识别提供了一种新方法.
Abstract:
This paper aims to apply Raman imaging technique for rapid detection of adulterated milk powder, which integrates the capability of microdomain scanning and large scale area screening to collect the imaging information of milk powder efficiently. A novel strategy, named as datadriven multiscale modeling (DDMM), is then proposed to extract the essential information of dopants in Raman imaging signals adaptively through constructing the optimal wavelet basis for data sets at hands. DDMM is capable of simultaneous estimation of presence of adulteration and its concentration level. As a result, a spectral diagnosis model is finally constructed for identification of adulterated milk powder, avoiding the leakage of important information through data fusion. The results indicate that the performance of DDMM model is satisfactory with a high selectivity of 98.9%, producing a semiquantitative results for estimating the concentration level of the dopants in milk powder. This definitely provides a promising tool for discrimination of adulterated milk powder.

参考文献/References

备注/Memo

备注/Memo:
收稿日期: 2016-07-01. 基金项目: 国家自然科学基金青年基金资助项目(21305101); 国家自然科学基金资助项目(61378048); 天津市应用基础与前沿技术研究计划重点资助项目(14JCZDJC34700). 作者简介: 陈达(1979—),男,博士,研究员. 通讯作者: 陈达,dachen@tju.edu.cn.
更新日期/Last Update: 2017-02-28