食品科学 ›› 2022, Vol. 43 ›› Issue (24): 296-302.doi: 10.7506/spkx1002-6630-20211130-378

• 安全检测 • 上一篇    

基于自编码收缩神经网络的奶粉掺杂快速拉曼成像检测

夏启,黄志轩,鲍蕾,卜汉萍,陈达   

  1. (1.天津大学精密仪器与光电子工程学院,天津 300072;2.雀巢研发(中国)有限公司雀巢食品安全研究院,北京 100016;3.中国民航大学 民航热灾害防控与应急重点实验室,天津 300300)
  • 发布日期:2022-12-28
  • 基金资助:
    国家自然科学基金面上项目(21973111;61378048);“十三五”国家重点研发计划重点专项(2018YFF01011700)

A High-Speed Raman Imaging Method for the Detection of Adulteration in Milk Powder Using Self-encode Shrinkage Neural Network

XIA Qi, HUANG Zhixuan, BAO Lei, BU Hanping, CHEN Da   

  1. (1. School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China;2. Nestlé Food Safety Institute, Nestlé R&D (China) Ltd., Beijing 100016, China; 3. Key Laboratory of Civil Aviation Thermal Management and Emergency Response, Civil Aviation University of China, Tianjin 300300, China)
  • Published:2022-12-28

摘要: 建立一种快速的奶粉掺杂拉曼成像识别方法,开发了自编码收缩神经网络重构算法,从低信噪比的短时拉曼成像信号中准确提取本征信号,并有机结合多元回归技术对奶粉中掺杂物进行定量分析,极大提升了拉曼成像扫描速度。在多种掺杂奶粉样本的定量检测中,该方法所建立的定量模型R2均达到了0.95以上,其检测速度较传统拉曼成像技术提升了30 倍,可在2 min之内完成50 mm×50 mm区域内的奶粉掺杂检测。结果表明,该方法可有效满足奶粉掺杂快速检测的实际需求,并为其他非均匀食品体系掺杂快速检测提供了一种新方法。

关键词: 奶粉掺杂;快速拉曼成像;自编码收缩神经网络;本征信号提取

Abstract: In this study, we proposed a high-speed Raman imaging method for the identification of adulterants in milk powder. In this method, a novel self-encode shrinkage neural network (SSNN) was developed to extract intrinsic information from the low signal-to-noise ratio Raman image with short integration time. Thereafter, multivariate regression models for quantitating the adulterant content in milk powder accurately were developed with the SSNN filtered Raman images. The coefficient of determination (R2) of these quantitative models for various adulterated samples was above 0.95. Through this method, a sample region size of 50 mm × 50 mm could be scanned with Raman imaging technique within two minutes, 30 times faster than traditional Raman imaging method. These satisfactory results demonstrate that this method can successfully meet the demand of milk powder adulteration detection in practice and can be used to detect adulteration in other non-homogeneous food systems.

Key words: milk powder adulteration; high-speed Raman imaging; self-encode shrinkage neural network; extraction of intrinsic signals

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