食品科学 ›› 2025, Vol. 46 ›› Issue (3): 267-273.doi: 10.7506/spkx1002-6630-20240714-137

• 专题论述 • 上一篇    下一篇

近红外光谱技术在谷物检测中的应用研究进展

王子熙,沈群,赵卿宇   

  1. (中国农业大学食品科学与营养工程学院,国家果蔬加工工程技术研究中心,国家粮食产业(青稞深加工)技术创新中心,国家粮油标准研究验证测试中心,北京 100083)
  • 出版日期:2025-02-15 发布日期:2024-12-30
  • 基金资助:
    “十四五”国家重点研发计划重点专项(2022YFF1100505);国家自然科学基金青年科学基金项目(32301983); 中国博士后科学基金资助项目(2022M723417;2023T160693)

Research Progress on the Application of Near Infrared Spectroscopy in Cereal Detection

WANG Zixi, SHEN Qun, ZHAO Qingyu   

  1. (National Engineering Research Centre for Fruits and Vegetables Processing, National Center of Technology Innovation (Deep Processing of Highland Barley) in Food Industry, National Center of Verifying and Testing in Grains and Oils Standard, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China)
  • Online:2025-02-15 Published:2024-12-30

摘要: 谷物作为人类的主要食物,富含多种关键营养成分,如水分、蛋白质、脂肪以及碳水化合物等。但由于谷物长期储存、霉菌污染和含异种粮粒等情况会严重影响谷物品质,危害粮食安全。因此,准确快速地检测谷物的营养成分、理化性质、霉菌污染以及其他方面对于食品质量控制和营养评估至关重要。近红外光谱技术作为一种快速、非破坏性的检测方法,近年来在粮食分析领域得到广泛应用。本文旨在综述近红外光谱技术在不同谷物营养成分(淀粉、脂肪、蛋白质、生物活性物质等)、理化特性(淀粉黏度特性值、硬度、粉质特性等)、霉菌污染以及其他(新陈度、品种鉴别、重金属和农药残留等)方面的应用现状,总结和比较常见模型算法的优点与不足,并提出目前近红外光谱技术在谷物检测中存在的问题以及未来发展方向,以期为进一步完善近红外光谱技术在谷物检测方面提供一定的参考依据。

关键词: 谷物;近红外光谱;营养成分;理化性质;霉菌污染

Abstract: Cereals, as the staple food for humans, are rich in a variety of nutrients such as moisture, proteins, fats and carbohydrates. However, the quality of cereals can be seriously affected by long-term storage, mold contamination and heterogeneous cereals, which jeopardize food security. Therefore, accurate and rapid detection of nutrients, physicochemical properties, and mold contamination in cereals is essential for food quality control and nutritional assessment. Near infrared spectroscopy has been widely used as a rapid and non-destructive detection method for cereal analysis in recent years. This paper aims to review the current status of the application of near infrared spectroscopy in the detection of cereals nutrients (starch, fat, protein and bioactive compounds), physicochemical properties (starch viscosity, hardness and farinograph parameters), mold contamination and other aspects (freshness, variety identification, heavy metals and pesticide residues), summarize and compare the advantages and disadvantages of common model algorithms, and propose some problems and future trends in the application of near infrared spectroscopy in cereal detection, in order to provide a reference for further improvement of near infrared spectroscopy in the detection of cereals.

Key words: cereal; near infrared spectroscopy; nutrients; physicochemical properties; mold contamination

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