Dataset for Accurate non-invasive quantification of astaxanthin content using hyperspectral images and machine learning

The dataset contains spectral data of cell suspensions of the microalgae Haematococcus pluvialis under no-stress and stress conditions. Spectral data was obtained with a hyperspectral imager (reflectance) and a spectrophotometer coupled with an integrating sphere (absorbance). Together with the raw data files, this dataset contains the Jupyter Notebook (PYTHON language) scripts to process the data and analysed it. Among the analysis, linear models and a convolutional neural network (CNN) are developed for the spectral data. The objective of this dataset was to develop a CNN able to accurately quantify astaxanthin content per dry weight from hyperspectral images (HSI). The CNN prediction accuracy was compared to linear models using the spectrophotometer couples with the integrating sphere. In addition to the scripts, this dataset contains all data files generated in those scripts.