Issue |
BIO Web Conf.
Volume 80, 2023
4th International Conference on Smart and Innovative Agriculture (ICoSIA 2023)
|
|
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Article Number | 01001 | |
Number of page(s) | 8 | |
Section | Agricultural Big Data Analysis | |
DOI | https://doi.org/10.1051/bioconf/20238001001 | |
Published online | 14 December 2023 |
Application of Hyperspectral Imaging for Rapid and Nondestructive Detection of Paraffine-Contaminated Rice
1 Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, Korea
2 Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Korea
* Corresponding author: Byoung-Kwan Cho (chobk@cnu.ac.kr)
The emergence of paraffin-coated rice in China, aimed at enhancing its market appeal and achieving a translucent appearance, has given rise to a significant global food safety concern. This situation poses substantial health risks to consumers. Hyperspectral analysis, recognized as a powerful and nondestructive technique for assessing food quality and safety, offers a potential solution. This study conducted a comprehensive investigation using Visible-Near Infrared (VIS-NIR) hyperspectral imaging systems operating within the 400-1000 nm range to identify paraffin-contaminated rice. Various rice varieties from diverse regions were obtained and intentionally tainted with varying levels of paraffin. Imaged samples were further preprocessed for spectral data extraction from individual rice seeds’ regions of interest (ROI). The dataset encompassed 3000 spectral records obtained from both non-contaminated and contaminated samples. The obtained spectral data were employed to develop partial least squares discriminant analysis (PLS-DA) and principal component linear discriminant analysis. The primary goal was to discriminate between contaminated and non-contaminated rice samples effectively. Notably, the results indicated that PLS-DA consistently achieved an accuracy exceeding 94% across various preprocessing techniques. Overall, this study showcased the potential of combining hyperspectral imaging with chemometrics to detect paraffin-contaminated rice seeds, providing a valuable contribution to food safety assessment in the industry.
Key words: hyperspectral imaging / paraffine contaminated / PLS-DA / PCA-LDA / beta coefficient
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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