Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform

Abstract

Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

Keywords: Raman spectroscopy; microalgae; monitoring; optical sensor; pilot scale.

Figure 1

Development of a Raman measurement approach: from laboratory to pilot-scale applications.

Figure 2

Monitoring of Parachlorella kessleri in Bold Basal Medium in a 1-L airlift photobioreactor. (A) Overview of 1793 spectra obtained over 36 days. (B) Median of 50 spectra recorded on day 0, day 4, day 8 and day 32. (C) Concentrations of chlorophyll a, chlorophyll b and carotenoids increase during cell growth until nitrogen starvation. Pigment concentration decreases when NaNO₃ = 0 mg/L.

Figure 3

(A) 2D correlation map of all spectra with a correlation table covering the 36 days of culture in a 1-L airlift photobioreactor. The colour of each map point represents the level of correlation between two spectra, from red (highest correlation) to blue (lowest correlation). (B) Repeatability of spectra measured by the autocorrelation level between 50 spectra recorded in the same time window. (C) Similarity of the spectra to those of the first day assessed by calculating the correlation between them.

Figure 4

Monitoring of Parachlorella kessleri in Bold Basal Medium in a 100-L tubular airlift photobioreactor from day 0 to day 13. (A) Overview of 2720 spectra obtained at 13 days. (B) Median of 50 spectra recorded on day 0, day 4, day 8 and day 13 (C) Concentrations of chlorophyll a, chlorophyll b and carotenoids increase during cell growth until nitrogen limitation. After significant nitrogen limitation, the concentration of pigments decreases. (D) Lipid analyses of samples collected between day 7 and day 13. Abbreviations indicate: NL, neutral lipids; GL, glycolipids; PL, phospholipids.

Figure 5

Raman band intensity over 14 days at pilot scale in a 100-L photobioreactor: v(C=C)_1660cm⁻¹/δ(CH₂)_1444cm⁻¹, v_as(C−H₂)_2940cm⁻¹, v_as (=C−H)_3008cm⁻¹, v(C=O)_1750cm⁻¹, v(C=C)_1524cm⁻¹ and v(C−C)_1157cm⁻¹/v(C=C) _1524cm⁻¹.

Figure 6

(A) Three-dimensional representation of the principal component analysis (PCA) of spectra from a 14-day culture in a 100-L tubular airlift bioreactor (PC1 26.3%; PC2 11.4%; PC4 7.4%) and their three respective loadings. (B) Kruskal–Wallis one-way ANOVA test, based on PCA loading 1, representing the variance of the spectra over the 13 days.