期刊
TRENDS IN FOOD SCIENCE & TECHNOLOGY
卷 116, 期 -, 页码 815-828出版社
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tifs.2021.08.012
关键词
Artificial intelligence; Cereals; Chemometrics; Nondestructive techniques; Spectroscopic techniques
资金
- National Natural Science Foundation of China [31972154]
- Key R&D Program of Jiangsu Province [BE2021343]
- Natural Science Foundation of Jiangsu Province [BK20190100]
- Project of Faculty of Agricultural Equipment of Jiangsu University
Cereals are important staple food globally, and their quality attributes are attracting more attention from nutritionists and scientists. Nondestructive techniques, such as near-infrared (NIR), infrared (IR), Raman spectroscopy, and fluorescence spectroscopy, along with colorimetric sensor array (CSA), have been introduced as promising tools for quality, authenticity, and discrimination of cereals. Chemometrics based on artificial intelligence and machine learning are also highlighted in this review article, which also addresses challenges in cereal processing that need further investigation.
Background: Cereals around the globe are consumed as a staple food owing to the provision of essential nutrients. Their quality attributes are increasingly attracting the attention of nutritionists and scientists. Emerging nondestructive techniques offers great perspectives due to the special advantages of noninvasive and rapid detection of qualitative and quantitative properties. Furthermore, no review article has been found covering all the nondestructive techniques coupled chemometrics in cereal. Taking this into consideration, current effort was made to provide an in-depth and up-to-date review article. Scope and methods: Traditional methods and nondestructive techniques utilized for the quality monitoring of cereals play a significant role. Traditional techniques accompanying the limitations of time-consuming, laborious, offline and destructive nature considered not good as compared to nondestructive techniques. Key findings: In the current review article, near-infrared (NIR), infrared (IR), Raman spectroscopy, and fluorescence spectroscopy, along with colorimetric sensor array (CSA), imaging-based techniques and data fusion strategies have been introduced as promising techniques for the quality, authenticity and discrimination of cereals. The use of chemometrics based on artificial intelligence and machine learning are also documented. This review article also covers the challenges related to cereal processing which need to be resolved or investigated in future studies.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据