The Activity of Microalgae in Ballast Water based on Microfluidic

Chip Electrokinetic Technology

Runzhe Sun

, Zhen Li

, Zhen Liu

, Na Li

and Yongxin Song

China Classification Society Tianjin Office, Tianjin 300457, China

CRRC Changchun Railway Vehicles CO., LTD, Changchun 130000, China

Department of Marine Engineering, Dalian Maritime University, Dalian 116026, China

Keywords: Ballast Water, Detection of Microalgae Activity, Contour Detection, Optical Flow.

Abstract: To control the marine bio-invasions in ballast water, the regulatory discharge standards specify the number

of viable organisms in ballast water treatment. It is an important task to determine the activity of microalgae

after ballast water treatment. In the current study, five kinds of microalgae were detected on microfluidic

chip. The electrokinetic velocity (EV) and diameter of microalgae were measured manually using an optical

microscope. Finally, the contour detection and Lucas-Kanada (L-K) Optical Flow technique were used to

calculate the diameter and velocity of microalgae, respectively. The result found that the EV of different

species of living microalgae decreases with increasing diameter. The EV of dead Pyramimonas sp.,

Platymonas and Prorocentrum donghaiense decreased to 0 μm/s. In addition, the L-K optical flow

technique can obtain the movement velocity of microalgae at any time, which can improve the detection

accuracy. Those study demonstrate that the development of a new field ballast water analysis instrument

based on contour detection and L-K optical flow technique is of great significance.

1 INTRODUCTION

During the daily operation, the ships have a large

number of ballast water in addition to transporting a

variety of cargo. However, the ballast water contains

large amounts of biological communities and

pathogens, which are the main sources of invasive

species in freshwater and marine ecosystems

(Sieracki et al., 2014). According to statistics, 3,000

species migrate with ballast water every day. The

foreign biological invasion will not only destroy the

biodiversity and ecological environment of the

original waters, but also have a serious negative

impact on the utilization of Marine resources and

Marine economy worldwide (Lymperopoulou and

Dobbs, 2017). In addition, the biological

characteristics of many microorganisms can promote

intrusion in ballast water. Because they have a high

ability to reproduce asexually and form dormancy

https://orcid.org/0000-0002-7696-1962

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https://orcid.org/0000-0001-9877-4335

stages, which will increase the chance of successful

invasion(Ruiz et al., 2000). The microalgae were the

main phytoplankton to be detected in ballast water.

Therefore, detection of microalgae activity after

ballast water treatment is an important part of the

inspection process. Currently, the main technology

to detect algae activity include flow cytometry,

chlorophyll fluorescence and cell staining (Song et

al., 2021).

Flow cytometry is an instrument for analysing

cell parameters based on optical principles. The

main principle is to disperse the samples to be

detected into suspension and dye with fluorescent

reagent. Under the irradiation of excitation light

source, the living cells will emit fluorescence, and

the photodetector can judge the activity of

microalgae by detecting the fluorescence intensity of

the cells (Joachimsthal et al., 2004). Compared with

epifluorescence direct counting, flow cytometry has

a higher degree of automation (Joachimsthal et al.,

2003). However, flow cytometry has limited

accuracy in detecting low concentrations of cells.

The technicians required to have certain technical

capacity, which led to some limitations of flow

cytometry in the detection of ship ballast water.

Sun, R., Li, Z., Liu, Z., Li, N. and Song, Y.

The Activity of Microalgae in Ballast Water based on Microﬂuidic Chip Electrokinetic Technology.

DOI: 10.5220/0011201100003443

In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2022), pages 283-287

ISBN: 978-989-758-595-1

283

Microalgae contain chlorophyll for

photosynthesis (Li et al., 2021). When the

chlorophyll is irradiated by an external laser, its

internal energy is in an unstable state. During the

transition from the ground state to excited state,

chlorophyll absorbs the energy brought by the

external laser. Eventually, the electrons return to

their ground state and the excess energy is released

outward as fluorescence. Therefore, the activity of

microalgae can be characterized by measuring

the intensity of fluorescence. However, some

microalgae do not

have chlorophyll, such as

microalgae living in the deep sea, cyanobacteria and

heterotrophs, which cannot be detected by

chlorophyll fluorescence technology (Steinberg et

al., 2011).

The cell staining can be divided into non-

fluorescent staining and fluorescent staining. Neutral

red and Trypan blue are commonly used in non-

fluorescent staining. Neutral red or Trypan blue dyes

can only stain living or dead algae, respectively

(Bradie et al., 2017; Stehouwer et al., 2013).

Fluorescence staining with fluorescein diacetate

(FDA) is a popular method for phytoplankton

vitality assessment. However, FDA could not stain

all living microalgae, which underestimated the true

number of viable microalgae (Hyun et al., 2018). In

addition, this method can only estimate the number

of microalgae by the total fluorescence intensity,

rather than accurately calculate the number of

microalgae (Song et al., 2021).

As mentioned above, all three methods have

certain limitations. Therefore, it is still necessary to

develop new technologies for microalgae activity

detection. For most microalgae, the negative charge

on cell surface is due to the presence of carboxyl,

amino, hydroxyl and phosphate anionic groups

(Keller et al., 2015). The surface charge and Zeta

potential of microalgae changed with the species and

growth process (Ives, 1959). In this study, we first

inactivated the algae with sodium hypochlorite.

Then, the electrokinetic velocity (EV) of live and

dead microalgae in ballast water was measured.

Meanwhile, image processing methods such as edge

detection and Lucas-Kanada (L-K) Optical Flow

technique are used to optimize the measured

parameters in the process of electric motion. A

method of microalgae activity detection based on

flow velocity is proposed, and the core objective is

to provide a method basis for ballast water

compliance.

2 MANUSCRIPT PREPARATION

2.1 Preparation of Microalgae

Chlorella vulgaris (C. vulgaris), Dunaliella salina

(D. salina), Pyramimonas sp., Platymonas and

Prorocentrum donghaiense (P. donghaiense) were

used in the experiments. The experiments need

living and death of algae. So, we inactivated

microalgae by treating them with 10 mg/L sodium

hypochlorite for 5min. Then, the method of neutral

red was used to stain microalgae in vivo to verify the

cell activity (Olsen et al., 2015). According to the

staining results, microalgae have been inactivated

after sodium hypochlorite treatments (Figure. 1). To

enable microalgae to be added to the microfluidic

chip, the living or dead algae were centrifuged for 3

min at 4000 rpm with a centrifuge (Eppendorf 5424,

GER). After abandoning the supernatant, 10% PEG

was added into the 1.5 mL centrifuge tube and

centrifuged again. PEG-living microalgae mixture

and PEG-dead microalgae mixture were obtained

respectively.

Figure 1: Illustration of neutral red staining for

Pyramimonas sp. (a) living algae and (b) dead algae.

2.2 Microchannel System

The microchannel (1 cm×100 µm×25 µm, length ×

width × height) and the slide coated with PDMS

were immersed in 10% PEG solution for 10 min.

Afterwards, the excess solution on the microfluidic

chip was dried and at 80 °C for more than 10 h in the

drying oven. Finally, the modified microfluidic chip

was obtained (Song et al., 2021) (Figure. 2).

The positive and negative platinum electrodes

are placed at the exit and entrance of the

microchannel, respectively. Add 10 μL PEG-

microalgae mixture and 10 μL 10% PEG solution to

the inlet and outlet of the channel, respectively.

Meanwhile, adjust the liquid level at both ends of

the channel and apply an electric field of 50 V/cm

after keeping the microalgae stationary. The

movement distance of microalgae was recorded

under the inverted optical microscope imaging

system (TI-E, Nikon, Japan). The diameter and EV

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

284

of microalgae were calculated by manual and

algorithm respectively.

Figure 2: Schematics of microfluidic chip.

2.3 Second Section

The target microalgae were extracted by background

subtraction method to eliminate the influence of

background. Then, we used the OTSU method to

binarize the extracted target microalgae to reduce the

internal texture. Finally, Canny operator is used to

detect its edge. To remove the contour error caused

by texture, the erode function in OpenCV is used to

corrode the image. The L-K optical flow technique

based on image pyramid is realized by

calcopticFlowpyrLK function, and the optical flow

of corresponding corner points is predicted.

3 RESULT AND DISCUSSION

3.1 Manual Measurement of

Electrokinetic Velocity and

Diameter in Algae

The relationship between EV and diameter of living

microalgae in this experiment is shown in Figure. 3.

The EV of different species of microalgae decreases

with increasing diameter. For instance, the EV of C.

vulgaris is found from 21.13 μm/s to 18.83 μm/s

when the diameter ranges from 3.23 μm to 4.21 μm.

In general, the EV is related to the number of

anionic groups on the cell surface, gravity effect and

friction effect on the channel wall. Therefore, the

different sizes of microalgae are affected to different

degrees, resulting in differences in EV. According to

the dead microalgae, the average EV of C. vulgaris,

D. salina, Pyramimonas sp., Platymonas and P.

donghaiense decreased to 2.79 μm/s, 2.13 μm/s, 0

μm/s, 0 μm/s, and 0 μm/s with the inactivation,

respectively (Figure. 3).

Figure 3: The relationship between algae diameter and EV

measured by manual measurements.

The main reason for the decrease of velocity may

be that the stop of algae metabolism and the

passivation of surface anionic groups lead to the

decrease of Zeta potential.

3.2 Contour Detection

The contour detection method was used to measure

the size of 50 randomly selected microalgae

samples. The results were compared with the manual

measurement, as shown in Figure. 4. The size of C.

vulgaris, D. salina, Pyramimonas sp., Platymonas

and P. donghaiense differed by 0.35 μm, 0.41 μm,

0.84 μm, 0.27 μm and 0.62 μm from that measured

manual measurement, respectively. It was found that

manual measurement had a better effect on large

microalgae (Pyramimonas sp., Platymonas and P.

donghaiense), while contour detection was more

advantageous for small microalgae (C. vulgaris and

D. salina). Therefore, the technology of contour

detection can not only improve the detection

accuracy of diameter parameters in algae, but also

reduce the work of experiments.

Figure 4: Detection results of algae by contour detection

and manual measurements.

The Activity of Microalgae in Ballast Water based on Microﬂuidic Chip Electrokinetic Technology

285

3.3 Lucas-Kanada Optical Flow

Technique

As shown in Table 1, the EV of living microalgae

was measured by L-K Optical Flow technique. The

same species of microalgae had similar EV at

different locations in the culture medium. The

average EV of C. vulgaris, D. salina, Pyramimonas

sp., Platymonas and Prorocentrum donghaiense was

21.81 μm/s, 15.36 μm/s, 10.24 μm/s, 8.45 μm/s and

4.77 μm/s, respectively. In addition, we also

measured the EV of dead microalgae, in which we

found that the average EV of Pyramimonas sp.,

Platymonas and P. donghaiense decreased to 0 μm/s

(Table 1). For small microalgae, the velocity of C.

vulgaris and D. salina decreased to 2.84 μm/s and

2.36 μm/s, respectively.

In order to verify the reliability of the L-K

Optical Flow technique, the results are compared

with the manual measurements. The EV deviation of

living C. vulgaris, D. salina, Pyramimonas sp.,

Platymonas and P. donghaiense measured by the

above two methods was 0.91 μm/s, 0.96 μm/s, 0.52

μm/s, 0.76 μm/s and 0.42 μm/s, respectively. In

addition, the velocity deviation of dead C. vulgaris

and D. salina was 0.24 μm/s and 0.22 μm/s,

respectively (Table 1). The main reason for the

above deviation is that the measurement result of L-

K Optical Flow technique depends on tracking the

movement displacement of the marked pixel level

corner point between two frames. Then, calculate the

instantaneous velocity from the displacement.

However, manual measurement may have artificial

errors in the process of distance marking and timing.

In contrast, the L-K Optical Flow technique can get

the speed at any time and more accurate velocity

results.

Table 1: EV of algae was measured by L-K optical flow technique and manual measurements.

C. vulgaris D. salina Pyramimonas sp. Platymonas

P. donghaiense

Live Dea

L-K Optical Flow 21.81 2.84 15.36 2.36 10.24 0 8.45 0 4.77 0

Manual 20.90 2.60 16.32 2.14 10.76 0 9.21 0 4.35 0

Deviation 0.91 0.24 0.96 0.22 0.52 0 0 0 0 0

4 CONCLUSIONS

In this study, we propose a method to judge

microalgae activity based on EV for evaluating the

viability of algae after ballast water treatment. The

EV of five different species of microalgae was

measured manually by using a microfluidic chip.

The result showed that the EV of different species of

living microalgae decreases with increasing

diameter. The EV of dead large microalgae

(Pyramimonas sp., Platymonas and P. donghaiense)

decreased to 0 μm/s, while the small algae of C.

vulgaris and D. salina decreased to 2.84 μm/s and

2.36 μm/s, respectively. In addition, to avoid time-

consuming and susceptible to human factors in the

process of manual measurement, the EV parameters

of microalgae are optimized by contour detection

and L-K Optical Flow technique. It reduces the

influence of human factors on EV measurement and

improves accuracy.

ACKNOWLEDGEMENTS

This work was supported by the financial support of

the National Natural Science Foundation of China

(51679023, 51979019) to Y. Song.

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