Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networks

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.

Keywords: (1)H NMR; Artificial intelligence; Deep learning-based CNN; Geographical discrimination; Red pepper powder.