Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networksopen access
- Authors
- Hoon Yun, Byung; Yu, Hyo-Yeon; Kim, Hyeongmin; Myoung, Sangki; Yeo, Neulhwi; Choi, Jongwon; Chun, Hyang Sook; Kim, Hyeonjin; Ahn, Sangdoo
- Issue Date
- May-2024
- Publisher
- Elsevier Ltd
- Keywords
- 1H NMR; Artificial intelligence; Deep learning-based CNN; Geographical discrimination; Red pepper powder
- Citation
- Food Chemistry, v.439
- Journal Title
- Food Chemistry
- Volume
- 439
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/71355
- DOI
- 10.1016/j.foodchem.2023.138082
- ISSN
- 0308-8146
1873-7072
- Abstract
- This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional 1H NMR spectra through a deep learning-based convolution neural network (CNN). 1H NMR spectra were collected from 300 samples originating from China, Korea, and Vietnam and used as input data. Principal component analysis − linear discriminant analysis and support vector machine models were employed for comparison. Bayesian optimization was used for hyperparameter optimization, and cross-validation was performed to prevent overfitting. As a result, all three models discriminated the origins of the test samples with over 95 % accuracy. Specifically, the CNN models achieved a 100 % accuracy rate. Gradient-weighted class activation mapping analysis verified that the CNN models recognized the origins of the samples based on variations in metabolite distributions. This research demonstrated the potential of deep learning-based classification of 1H NMR spectra as an accurate and reliable approach for determining the geographical origins of various foods. © 2023 The Author(s)
- Files in This Item
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- Appears in
Collections - College of Natural Sciences > Department of Chemistry > 1. Journal Articles
- Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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