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Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review.
Xu, Sai; Guo, Yinghua; Liang, Xin; Lu, Huazhong.
Afiliação
  • Xu S; Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
  • Guo Y; College of Engineering, South China Agricultural University, Guangzhou 510642, China.
  • Liang X; Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
  • Lu H; College of Engineering, South China Agricultural University, Guangzhou 510642, China.
Foods ; 13(7)2024 Apr 06.
Article em En | MEDLINE | ID: mdl-38611420
ABSTRACT
Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article