食品科学

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光谱学技术在肉类腐败检测中的研究进展

刘庆森1,罗欣1,董鹏程2,郝剑刚3,毛衍伟1,成海建4,张一敏1   

  1. 1. 山东农业大学食品科学与工程学院
    2. 山东农业大学
    3. 国家肉牛牦牛产业技术体系乌拉盖综合试验站
    4. 国家肉牛牦牛产业技术体系济南站
  • 收稿日期:2022-07-04 修回日期:2022-08-25 出版日期:2022-09-27 发布日期:2022-09-27
  • 通讯作者: 毛衍伟
  • 基金资助:
    山东省生猪产业技术体系建设

Advances in spectroscopy technology for the detection of meat spoilage

  • Received:2022-07-04 Revised:2022-08-25 Online:2022-09-27 Published:2022-09-27
  • Contact: MAO Yan-Wei
  • Supported by:
    Shandong Province Pig Industry Technology System

摘要: 微生物的生长代谢是导致肉类腐败的主要原因,肉中微生物的快速、无损检测技术已受到越来越多的关注。其中以拉曼光谱、红外光谱和光谱成像为主的光谱学技术,在快速、无损检测等方面表现出较大的优势,但是其在肉类腐败检测中的应用情况却未得到及时总结。因此,本文首先基于肉在不同贮藏条件下发生腐败的优势菌群及微生物代谢活动,简述了肉类腐败能够进行光谱学预测的物质基础;然后在了解光谱技术建立预测模型方法的基础上,依次总结了拉曼光谱、红外光谱和光谱成像技术在预测肉的货架期方面的应用研究进展,并重点阐述了以菌落总数或挥发性盐基氮为目标物建模进行预测的效果及存在问题,以期对快速无损检测技术为肉类腐败鉴定的研究提供思路和理论指导。

关键词: 快速检测, 腐败微生物, 拉曼光谱, 红外光谱, 光谱成像

Abstract: The growth and metabolism of microorganism is the main cause of meat spoilage. The rapid and nondestructive detection technology of microorganism in meat has attracted more and more attention. Among them, Raman spectroscopy, infrared spectroscopy and spectral imaging show great advantages in rapid and non-destructive aspects, but their application in meat spoilage detection has not been timely summarized. Therefore, based on the dominant flora and metabolic activities of microorganisms in different storage conditions of meat spoilage, the material basis for spectral prediction of meat spoilage was briefly described in this paper. Then, based on theunderstanding spectral technology to establish prediction model method, the application of Raman spectroscopy, infrared spectroscopy and spectral imaging technology in predicting the shelf life of meat was summarized. The effects and existing problems of modeling based on the total visible colonies or total volatile base nitrogen were emphasized. In a practical perspective, this will provide ideas and theoretical guidance for the development and application of rapid nondestructive testing technology in meat spoilage identification.

Key words: rapid detection, spoilage microorganisms, raman spectrum, infrared spectroscopy, spectral imaging

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