{'fraction_non_alphanumeric': 0.014164305949008499, 'fraction_numerical': 0.0009442870632672333, 'mean_word_length': 5.708860759493671, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Stacked spectral data processing and artificial neural networks applied to FT-IR and FT-Raman spectra in biomedical applications

Biomedical applications of vibrational spectroscopy developed for routine analysis require methods for data evaluation. Artificial neural networks open a new perspective for the spectra differentiation and identification of biological samples with their small spectra variance. In the present study, the stacked spectral data processing and the following use of neural networks for spectral identification was investigated. 6 different neural network architectures were tested in their capability to built spectral libraries for different bacterial genera and for yeasts, using FTIR and FT-Raman spectra. After developing these libraries, they were connected to a large library, what we called 'multilayered neural networks'. This combines the advantages that the wavelength can be chosen more selective for a given differentiation problem and the network architecture and training function can be more adapted to a special task.