外文摘要：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.
外文关键词：artificial intelligence;Red pepper powder;Geographical discrimination;1 H NMR;Deep learning -based CNN
作者：Yun, Byung Hoon;Yu, Hyo-Yeon;Kim, Hyeongmin;Myoung, Sangki;Yeo, Neulhwi;Choi, Jongwon;Chun, Hyang Sook;Kim, Hyeonjin;Ahn, Sangdoo
作者单位：Seoul Natl Univ;Chung Ang Univ;Seoul Natl Univ Hosp
期刊名称：FOOD CHEMISTRY
期刊影响因子：0.0
出版年份：2024
出版刊次：439
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台