Abstract. With this work our main objective is to find the best general (baseline) model for analyzing unknown spectra using Fourier infrared transformed spectroscopy (FTIR) coupled with machine learning (ML) algorithms. This goal allows us to identify the best methodology applied for inline analysis of different experimental spectra for qualitative structural information obtained with types of structures that generate absorption or emission peaks. This methodology opens new perspectives for automated data processing using flexible algorithms and machine learning to encode experimental data for future applications. The results provide a good perspective on Machine Learning algorithms for applied sciences research. For our case study (FTIR experimental data) our model allows for peak feature extraction with a relative low, close to machine standard deviation, error budget. The best model identified is a specialized model but the standard, fully connected network models are evaluated.

Key words: Fourier transform-infrared spectroscopy, machine learning algorithms, autonomous data processing, data feature extraction.