Test dataset for data analysis and creation of a calibration curve using spectral data with the Galaxy/ChemFlow 20.05 platform

Hyperspectral imaging results from the integration of two well-established technologies: spectroscopy and imaging. This approach acquires images across the visible and infrared wavelength ranges, allowing the capture of the complete spectral profile of a sample at each point in the imaging plane (Ngadi et al., 2010). Hyperspectral images are composed of spectral pixels, each corresponding to a spectral signature of its respective spatial region. A spectral pixel records the entire measured spectrum of the spatial point being imaged. This spectrum characterizes the ability of a sample to absorb or scatter incident light (Ngadi et al., 2010). The main advantage of hyperspectral imaging lies in its capacity to characterize the inherent chemical properties of a sample. This is achieved by measuring its spectral response, i.e., the collected spectral pixels. Typically, a hyperspectral image contains thousands of spectral pixels (Ngadi et al., 2010). Once processed, hyperspectral images primarily yield spectral data. When these data are combined with reference measurements obtained through laboratory analyses and processed with computational tools, it becomes possible to develop predictive models of sample composition. Among the freely available platforms for this purpose is Galaxy/ChemFlow 20.05. Galaxy is an open-source platform designed to support data-intensive research. It is developed by the Galaxy Team with contributions from a broad community. The Galaxy Project is partially funded by the National Human Genome Research Institute (NHGRI), the National Science Foundation (NSF), the Huck Institutes of the Life Sciences, the Penn State Institute for CyberScience, and Johns Hopkins University. Methodology: This guide outlines the specific steps for operating the Galaxy/ChemFlow 20.05 platform using spectral datasets obtained from hyperspectral images captured with the Specim FX17 hyperspectral camera. Calibration curves are generated using laboratory-based primary data, and the predictive models are implemented through Partial Least Squares Regression (PLSR). (2025-08)