A MATLAB tutorial on deep feature extraction combined with chemometrics for analytical applications

TL;DR

This paper provides a MATLAB tutorial guiding users on how to extract deep features from imaging data in analytical chemistry using existing open-source deep learning models, facilitating integration with spectral data.

Contribution

It offers a structured, step-by-step MATLAB tutorial for applying deep learning models to extract spatial features from imaging data in analytical chemistry.

Findings

Provides MATLAB code demonstrations for various imaging modalities.

Guides on integrating deep features with spectral data.

Facilitates adoption of deep learning in analytical chemistry.

Abstract

Background In analytical chemistry, spatial information about materials is commonly captured through imaging techniques, such as traditional color cameras or with advanced hyperspectral cameras and microscopes. However, efficiently extracting and analyzing this spatial information for exploratory and predictive purposes remains a challenge, especially when using traditional chemometric methods. Recent advances in deep learning and artificial intelligence have significantly enhanced image processing capabilities, enabling the extraction of multiscale deep features that are otherwise challenging to capture with conventional image processing techniques. Despite the wide availability of open-source deep learning models, adoption in analytical chemistry remains limited because of the absence of structured, step-by-step guidance for implementing these models. Results This tutorial aims to bridge this gap by providing a step-by-step guide for applying deep learning approaches to extract spatial information from imaging data and integrating it with other data sources, such as spectral information. Importantly, the focus of this work is not on training deep learning models for image processing but on using existing open source models to extract deep features from imaging data. Significance The tutorial provides MATLAB code tutorial demonstrations, showcasing the processing of imaging data from various imaging modalities commonly encountered in analytical chemistry. Readers must run the tutorial steps on their own datasets using the codes presented in this tutorial.