Recent advances in assessing qualitative and quantitative aspects of cereals using nondestructive techniques: A review

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.

Automated summary

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.