LUT CARS Data Analysis

Effect of Different NRB types

Paper's link: Effect of non-resonant background on the extraction of Raman signals from CARS spectra using deep neural networks

By Rajendhar junjuri, Ali Saghi, Lasse Lensu, and Erik M. Vartiainen

About Synthetic test data

These are 300 synthetic test spectra evaluated in the manuscript.

1. First, 100 spectra correspond to ‘Product of two Sigmoid NRB’.

2. Spectra 101-200 account for ‘One sigmoid NRB’.

3. Spectra 201-300 correspond to ‘Polynomial NRB’.

"y_test_300_merge_spectra3.npy"---> referes to the true Raman signal

"x_test_300_merge_spectra3.npy"---> referes to the input CARS data

About the experimental CARS test data

The experimental CARS test data set used in this investigation can only be provided upon request and can contact Erik M. Vartiainen

About the CNN model code

The model architecture is directly adapted from the SpecNet paper (See https://github.com/Valensicv/SpecNet for the full code of the neural network model) Here three different NRBs are evaluated.

It can be accessed from the following program. RSS_Advances_CNN_to_train_with_different_NRBs.py

Testing can be done by using the following program. RSS_Advances_CNN_prediction_on_test_data.py

About the trained model weights

"One_sigmoid_NRB_model_weights.h5" --->referes weights of the model trained with One sigmoid NRB.

"Polynomial_NRB_model_weights.h5" --->referes weights of the model trained with Polynomial_NRB.

"Specnet_weights.h5" --->referes weights of the model trained with the product of two sigmoids NRB

Getting Started and Requirements

You can use Python (TensorFlow 2.7.0) to test the pre-trained network. We have tested it in Spyder.

Article can be found here

https://scholar.google.co.in/citations?view_op=view_citation&hl=en&user=BRu_wuAAAAAJ&sortby=pubdate&citation_for_view=BRu_wuAAAAAJ:ULOm3_A8WrAC