J Spectrosc 2022:e4509523Īhmad N, Mounsef JR, Lteif R (2021) A simple and fast experimental protocol for the extraction of xanthophylls from microalga Chlorella luteoviridis. Graphical AbstractĪchir N, Servent A, Soto M, Dhuique-Mayer C (2022) Feasibility of individual carotenoid quantification in mixtures using UV-Vis spectrophotometry with multivariate curve resolution alternating least squares (MCR-ALS). Finally, the data and source files are available in an online repository. Furthermore, the workflow is presented step-wisely so that other scholars may adapt it to their needs (e.g., producing a simpler model focusing only on one pigment). They would significantly shorten the delay in obtaining samples’ carotenoids concentrations compared to liquid chromatography while retaining adequate accuracy (below 10%). Therefore, the equations can readily be used for quantifying lutein, violaxanthin, and zeaxanthin (if high enough). From a practical perspective, the proposed model is not only calibrated but also validated. The best quantification (trade-off between accuracy and over-fitting) was obtained with a seven-feature model (one absorbance and six absorbance derivatives). The data collected were used to calibrate a machine learning model based on partial least square regression. To do so, numerous samples with various pigments concentrations (obtained by HLPC) were gathered with their associated visible spectra. Microalgae samples were extracted in methanol following a classical procedure, and no chromatographic separation was applied. A machine learning workflow was used to develop spectrophotometric equations quantifying chlorophyll a, b, lutein, violaxanthin, and zeaxanthin simultaneously.

Machine learning least squares