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Application of image analysis for algal biomass quantification: a low-cost and non-destructive method based on HSI color space

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Abstract

Algal biomass has been utilized as a potential feedstock for bioenergy and a variety of other bioprocesses. At the core of this topic, there is a general demand for rapid, cost-effective, and accurate methods for algal biomass quantification. Here, we present a simple, low-cost, and non-destructive method to estimate algal biomass concentrations by image analysis based on the hue, saturation, intensity (HSI) color space. The applicability of the HSI-based quantitative method was verified using experimental data from both the present study and the literature. In addition, the HSI-based quantitative method showed better goodness of fit and a significantly higher detection range than related methods used in the literature. The results indicate that the HSI-based quantitative method can be used as an effective method for algal biomass monitoring and quantification.

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Fig. 1

source and (ii) the specialized light-tight dark apparatus for algal biomass quantification

Fig. 2

source of H = 266°; (ii) captured using CCD camera with light source of white; (iii) captured using smartphone with light source of H = 266°; (iv) captured using smartphone with light source of white. (b–e) The correlation between C. vulgaris density and Ahsi calculated from the Beer’s law in HSI color space, under different capturing conditions described in (a), respectively. The zoom-out panels in (c) and (e) indicate regression curves without zero-intercept. (f) Comparison of the algal density of the unknown sample calculated using regression curves from (b–e)

Fig. 3
Fig. 4

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We are grateful to Dr. Yukiko Goda for her technical assistance in preparing the dark chamber.

Funding

This work was partly supported by KAKENHI, Grants-in-Aid for Scientific Research, grant number 19H03302 and 21J15473 from the Japan Society for the Promotion of Science.

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Authors and Affiliations

  1. Center for Ecological Research, Kyoto University, 520-2113, Shiga, Japan

    Mengqi Jiang & Shin-ichi Nakano

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  1. Mengqi Jiang
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  2. Shin-ichi Nakano
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Correspondence to Mengqi Jiang.

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Jiang, M., Nakano, Si. Application of image analysis for algal biomass quantification: a low-cost and non-destructive method based on HSI color space. J Appl Phycol 33, 3709–3717 (2021). https://doi.org/10.1007/s10811-021-02571-4

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  • DOI: https://doi.org/10.1007/s10811-021-02571-4

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