空间偏移拉曼光谱分析技术及其在食品次表层检测中的应用

doi:10.3964/j.issn.1000-0593(2024)05-1201-08

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摘要：空间偏移拉曼光谱（SORS）是一种应用于浑浊/分层介质深层无损检测的新型拉曼光谱分析技术。分层物质不同深度的拉曼信号随传输距离增加对探测器的贡献产生规律性变化，SORS技术通过采集偏移激光入射点不同距离的拉曼光谱，结合最佳偏移距离信号增强、次表层谱峰识别、次表层信号分离等方法降低表层的干扰，获取纯净的次表层物质拉曼信号，然后借助拉曼的分子特异性实现无损的次表层物质定性和定量检测。介绍了空间偏移拉曼光谱技术的基本概念及测量技术，详细阐述了空间偏移拉曼光谱数据的分析方法及该技术在食品次表层检测中的应用，讨论了空间偏移拉曼光谱技术的限制条件并展望了该领域的未来发展。

关键词：空间偏移拉曼光谱；次表层无损检测；食品品质检测；信号识别；信号分离

Abstract：Spatially Offset Raman Spectroscopy (SORS) is a novel Raman spectroscopy technique for deep nondestructive turbid/layered media testing. Raman signals at different depths of layered materials have regular changes that contribute to detectors with the increase of transmission distance. Therefore, the SORS collects Raman spectra of offset laser points at different distances, combines the methods of optimal offset signal enhancement, subsurface spectral peak identification, subsurface signal separation, etc., to reduce the interference of the surface layer, and obtains pure Raman signals of subsurface materials. Then, the non-destructive qualitative and quantitative detection of subsurface materials is realized by the molecular specificity of Raman. This paper introduces the basic concept and measurement technology of SORS, and the analysis method of SORS and application of the technique in food subsurface inspection are described in detail. Finally, the limitations of SORS are discussed, and the future development of this field is prospected.

Key words：Spatially offset Raman spectroscopy; Subsurface nondestructive detection; Food quality detection; Signal recognition; Signal separation

收稿日期: 2022-11-23 修订日期: 2023-09-04

中图分类号:

O657.37

基金资助: 国家自然科学基金项目（61775086，61772240）资助

通讯作者: 黄敏 E-mail: huangmzqb@163.com

作者简介: 刘振方，1992年生，江南大学物联网工程学院先进控制教育部重点实验室博士研究生 e-mail: lzf940321@163.com

引用本文:

刘振方，黄敏，朱启兵，赵鑫，闫胜琪. 空间偏移拉曼光谱分析技术及其在食品次表层检测中的应用[J]. 光谱学与光谱分析, 2024, 44(05): 1201-1208.
LIU Zhen-fang, HUANG Min, ZHU Qi-bing, ZHAO Xin, YAN Sheng-qi. Spatially Offset Raman Spectroscopy Analysis Technology and Application in Food Subsurface Detection. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1201-1208.

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