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

Abstract

Commercial cultivation of the microalgae Haematococcus pluvialis to produce natural astaxanthin has gained significant traction due to the high antioxidant capacity of this pigment and its application in foods, feed, cosmetics and nutraceuticals. However, monitoring of astaxanthin content in cultures remains challenging and relies on invasive, time consuming and expensive approaches. In this study, we employed reflectance hyperspectral imaging (HSI) of H. pluvialis suspensions within the visible spectrum, combined with a 1-dimensional convolutional neural network (CNN) to predict the astaxanthin content (g mg-1) as quantified by high-performance liquid chromatography (HPLC). This approach had low average prediction error (5.9%) across a gradient of astaxanthin contents and was only unreliable at very low contents (<0.6 g mg-1). In addition, our machine learning model outperformed single or dual wavelength linear regression models even when the spectral data was obtained with a spectrophotometer coupled with an integrating sphere. Overall, this study proposes the use of HSI in combination with a CNN for precise non-invasive quantification of astaxanthin in cell suspensions.

Code and data availability

The supplied blocks describe hyperspectral imaging data, HPLC reference measurements, and a 1D CNN model, but contain no data availability statement, code deposit, or public repository URL for the spectra, phenotyping data, or trained model. No paper-specific public asset is identified.

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