Nitrate and nitrite as mixed source of nitrogen for Chlorella vulgaris: fast nitrogen quantification using spectrophotometer and machine learning

Victor Pozzobon (1, 2) , Wendie Levasseur (1, 2) , Cédric Guerin (1, 2) , Patrick Perré (1, 2)

1 LGPM - Laboratoire de Génie des Procédés et Matériaux
2 CEBB - Centre Européen de Biotechnologies et Bioéconomie

This article presents a machine learning workflow allowing to construct spectrophotometric equations predicting nitrate and nitrite concentrations within microalgae culture samples. First, numerous samples with various nitrate and nitrite concentrations (in mixture or separated) were drawn from cultures. Their UV absorbance spectra were recorded with a tabletop spectrophotometer before being analyzed using ion chromatography. Then, the data collected were used to construct a machine leaning model based on partial least square regression. From a practical perspective, the best model involves 3 wavelengths to quantify both nitrate and nitrite within the samples. The proposed equations can readily be used (LoQ of 0.5 mg.L −1 , uncertainty of ± 10 %). They greatly shorten the delay to obtain sample nitrate and nitrite concentrations compared to ion chromatography while retaining adequate accuracy. Furthermore, the workflow is presented step-wisely, with emphasis on relevant details so that other scholars may deploy in their own laboratory to best suit their own needs. Finally, the data and source files are made available in an online repository.

Mots clés: Nitrogen; Quantification; Spectrophotometry; Machine learning; Partial least square

Domaines: Machine Learning [stat.ML]

Victor Pozzobon, Wendie Levasseur, Cédric Guerin, Patrick Perré. Nitrate and nitrite as mixed source of nitrogen for Chlorella vulgaris: fast nitrogen quantification using spectrophotometer and machine learning. Journal of Applied Phycology, 2021, 33 (3), pp.1389-1397. ⟨10.1007/s10811-021-02422-2⟩. ⟨hal-03384999⟩