Nuclear magnetic resonance (NMR) is an unbiased, reproducible, and non-destructive detection method widely used to analyze and characterize complex systems. However, it is challenging to perform mixture identification because of the offset and peak overlap of chemical shifts that often exist in mixtures. Convolutional neural networks have translation invariance and high tolerance to distortion, which are good at extracting local features to solve the above problems well. Here, we propose a deep learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors). This is the code repo for the paper Deep Learning-based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy.

Python and TensorFlow:

Python 3.8.13 and TensorFlow (version 2.5.0-GPU)

The main packages can be seen in requirements.txt

• Install Anaconda https://www.anaconda.com/

• Install main packages in requirements.txt with following commands

  conda create --name pSCNN python=3.8.13
  conda activate pSCNN
  python -m pip install -r requirements.txt
  pip install tqdm

git clone https://github.com/yfWang01/DeepMID.git

Training your model and predict mixture spectra data

Run the file 'DeepMID.py'. Download the model and these example data, DeepMID can be reload and predict easily.

Yufei Wang: 222311036@csu.edu.cn