In Situ Multi-Excitation Chlorophyll Fluorometer for
Phytoplankton Measurements
Technologies and Applications Beyond Conventional Fluorometers

Mitsuo Yoshida, Tomohiro Horiuchi, Yasuhiro Nagasawa
JFE Advantech Co., Ltd.
7-2-3 Ibukidai-Higashi, Nishi-ku, Kobe, Japan
Abstract—In this paper, we present the technology and evaluate
the performance of a newly developed in situ fluorometer, MultiExciter, which can measure the excitation spectra of chlorophyll
fluorescence both automatically and continuously. The MultiExciter has nine wavelength excitation LEDs to measure theexcitation spectra. The fluorometer measures the discrete
excitation spectra of phytoplankton. Then, the phytoplankton
species composition can be estimated by solving the optimization
problem. The fluorometer has a good linearity that is less than
Pg-Chla/l, and reduces errors (<1.0%FS) in turbid water
0.1P
caused by reflectance induced by the excitation lights. These
performances show that the fluorometer enables accurate
measurements of phytoplankton excitation spectra with a wide
range suitable for use in the field. The classification performanceof the device is also evaluated in a laboratory setting.

I.

INTRODUCTION

Measuring biomass and compositions of phytoplankton isnecessary to monitor water quality and the health of theecosystem. Chlorophyll concentrations have been used as anindex of the biomass. In the 1960s, in vivo fluorometry for theestimation of chlorophyll-a concentrations was introduced tooceanographic communities [1]. This methodology has beenvaluable in advancing an understanding of the spatial andtemporal distribution of phytoplankton biomass. However, thepresent in vivo fluorometry has been insufficient to obtain
information about the compositions of phytoplankton. This isone reason why a manual methodology with microscopy hasbeen widely used. The analysis of phytoplankton compositions
has become increasingly important with the explosion ofharmful algal blooms (HAB) during the past several decades.
HAB is produced by specific phytoplankton species and thespatial and temporal magnitudes vary rapidly. Therefore, tomonitor
phytoplankton
compositions,oceanographic
communities require autonomous technologies to identify
phytoplankton species and types.
Phytoplankton groups have different pigment compositions.
Therefore, the group can be identified by its composition [2].
However, pigment compositions in water cannot be quantified
directly. Optical properties of phytoplankton change because of
the pigment composition. Therefore, the compositions can be
estimated from the optical properties. By measuring opticalproperties of phytoplankton, phytoplankton groups areindirectly identified.

0-933957-39-8 ©2011 MTS

Because the optical properties significantly depend on thepigment composition, absorption and excitation spectra ofphytoplankton exist. Measuring absorption spectra ofphytoplankton in the field is quite difficult because theabsorbance is very low and interfered by other properties (i.e.
absorption and scattering of CDOM: Colored Dissolved
Organic Matter and suspended particles). Compared withabsorption spectra, excitation spectra can be easily measured.
All phytoplankton, including Cyanobacteria, have a fluorescentemission around 680 nm. This emission characteristic inaquatic environments is unique. Excitation spectra ofphytoplankton can be obtained with high S/N ratios. Therefore,
excitation spectra measurements are suitable to extract opticalproperties of phytoplankton. Previous scientific papers suggestthat we can classify phytoplankton groups using the excitation
spectra [3, 4].
We developed an in situ multi-excitation fluorometer,
Multi-Exciter, which measures the excitation spectra ofphytoplankton. The Multi-Exciter measures nine wavelength
excitation spectra. The Multi-Exciter both quantifies the total
phytoplankton biomass (chlorophyll-a) and estimates thephytoplankton group compositions using the observed
excitation spectra. To enhance its utility, the fluorometer wasdeveloped to have high sensitivity and reduce the noise-effectof reflectance from suspended particles in water. Throughmathematical processing, phytoplankton groups are identified
and quantified.
II.

OPTICAL DESIGN AND TECHNOLOGY

The Multi-Exciter has nine wavelength LEDs to illuminate
a water sample. The center wavelengths of the nine LEDs are375, 400, 420, 435, 470, 505, 525, 570, and 590 nm,
respectively. Figure 1 shows the spectral characteristics of theLEDs. The excitation wavelengths of the LEDs are selected byconsidering the maximum absorption of the photosyntheticpigments, which appears at a shorter wavelength than 600 nm.
The Multi-Exciter detects fluorescent signals from 630 nm toabout 1000 nm, because phytoplankton commonly emits adistinguishable red fluorescence near 680 nm. The detector of
the Multi-Exciter is a Si-photodiode. The optical filters are used
for the excitation LED and the photodiode detector to intercept
the stray light generated through light scattering. Figure 2

shows a schematic of the optical filter characteristics used in
the Multi-Exciter.
The Multi-Exciter has temperature, depth, and turbidity
sensors. In addition, this instrument has a mechanical wiper toprevent bio-fouling on the optical window. Consequently, theinstrument provides stable and accurate optical data during thedeployment period. Figure 3 shows the exterior appearance ofthe Multi-Exciter.
Relative radiant intensity [a.u.]

1.0
Ch.1
Ch.2
Ch.3
Ch.4
Ch.5
Ch.6
Ch.7
Ch.8
Ch.9

0.5

(a) Multi-Exciter overview

Ex. spectrum

Depth

Temperature

Wiper

0.0350400

450 500 550
Wavelength [nm]

600

650

Figure 1. Spectral distributions of the nine LEDs.

(b) Sensors top viewFigure 3. Appearance of the Multi-Exciter.

Transmittance [%]100Excitation50

It is assumed that there are m groups of phytoplankton in
the water sample. Each group has a different spectral
characteristic. Here, we defined the specific spectrum as thespectrum normalized by the corresponding chlorophyll-aconcentration. The specific spectra of m groups are expressed
as follows:

Emission

si0350450550 650 750
Wavelength [nm]

850

950

[si1, si 2 ,, si9 ]t  R 9(i 1, 2, , m).

The excitation spectrum in the water sample, y , isreconstructed using the specific spectrum and thecorresponding chlorophyll-a concentrations:Figure 2. Characteristics of the optical filters.

yIII.

PROCESSING OF PHYTOPLANKTON GROUPS

A. Modeling of the excitation spectra
The Multi-Exciter measures the nine-wavelength excitation
fluorescence spectrum, y , in the water sample. The spectrum
is described as the following vector form:y[ y1 , y2 ,, y9 ]t  R 9

where t is the transpose of a vector, and R 9 is the set of ninedimensional real-valued vectors.

c1s1  c2s 2    cms mSc

where ci indicates the chlorophyll-a concentration of each
phytoplankton species in the water sample. The chlorophyll-aconcentrations, c , and the excitation spectra of thephytoplankton groups, S , are described as:c [c1, c2 ,, cm ]t  R m S [s1, s2 ,, sm ]  R9um,,

where R 9u m indicates the set of 9 u m matrices.

cˆ[cˆ1 , cˆ2 ,, cˆm ]t  R m.

ĉ is calculated using a constrained least-squares method,which minimizes the norm of the residual between theobserved spectrum (using the fluorometer y ) and thereconstructed spectrum composed of the specific spectrum andthe corresponding estimated chlorophyll-a, Scˆ . This problem
is formulated as the following optimization problem:

mincs.t.

y  Scˆ2cˆi t 0 (i 1, 2, , m)

where  is the Euclidean norm of the vector. The above
constraint is based on a priori information that the chlorophyll
concentrations are nonnegative.

B. Responses to suspended solids
In turbid water, the excitation light is reflected bysuspended matter (e.g. minerals and detritus). The instrument
overestimates the fluorescent intensities if the reflectance lights
come through the detector. To quantify the effect of thereflectance on measurement, we measured the responses
against a wide range of Kaoline solutions in a bucket. Afterputting Kaoline powder into 2 L of distilled water, thefluorescence sensor outputs were measured. At that time, theturbidity was observed using an IR-backscattering turbiditysensor. The reflectance effects were within 0.6% FS at 109
FTU [Figure 5].
1.0
Fluorescence output [%FS]

B. Algorithms
The estimated chlorophyll-a concentrations of each groupin the sample, ĉ , are described using the following vector:

Ch. 1
Ch. 2
Ch. 3
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9

0.8
0.6

0.4
0.2

0.0
0

IV.

A. Responses to Chl-aFigure 4 explains the relationship between the fluorometer
outputs and the chlorophyll-a concentrations in the Diatom alga
Cheatoceros sp. determined by a fluorometric method with theextracted samples. This result suggests the fluorometer outputsof the nine-channel device have good linearity to less than 0.1
Pg-Chla/l.
100.00

Ch.1

10.00

Ch.2

406080Turbidity [FTU]

100120Figure 5. Responses to suspended solids.

C. Specific spectra of phytoplankton
Figure 6 shows the specific spectra of three pigment-type
phytoplankton (Diatom: Cheatoceros sp., Green algae:Nanochloropsis sp., Cyanobacteria: Microcystis sp.) observed
using the fluorometer. All spectra are normalized using thefluorescent intensity at 430 nm or 570 nm. The fluorometer
observed different fluorescent properties depending on thepigment compositions among the three types. Microcystis sp.
indicates a quite different optical characteristic with a highexcitation ratio (570 nm:430 nm) because only Cyanobacteria
have phicobili-protein pigments with a maximum absorption
around 570 nm.

Ch.3
Cheatoceros sp.
Microcystis sp.

Ch.4

1.00

Ch.5
Ch.6
Ch.7

0.10

Ch.8
Ch.9

0.01
0.01

0.10
1.00
10.00
Chlorophyll-a [μg/l]
Figure 4. Responses to Chl-a concentration.

100.00Normalized Chl-a at 430 or 570 nm [a.u.]

Fluorescence [a.u.]

20PERFORMANCE

Nanochloropsis sp.

1.00.80.60.40.20.0350

400450500550
Wavelength [nm]600

Figure 6. Specific spectra of phytoplankton.

D. Evaluation of the classification
The classification performance described in the above
algorithm was evaluated in mixed culture samples (Diatom:Cheatoceros sp., Green algae: Nanochloropsis sp.,
Cyanobacteria: Microcystis sp.). To process the phytoplankton
groups, the specific spectra (see section III-B) were preprepared. By changing the mixing ratios, the test was carriedout in seven different samples. Figure 7 shows the relationships
between the estimated and the actual chlorophyll-aconcentrations. The Cyanobacteria and Green algae species
match both values with errors of less than 6%. In the Diatomspecies, the estimated values explain the variation but areoverestimated compared with the actual concentrations. The
maximum error was approximately 45%. The estimated errors
were caused by the mismatch of the fluorescent intensities perchlorophyll-a concentrations between the specific spectra of theactual samples and the prepared samples.
Cheatoceros sp.

9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.00.0CONCLUSIONACKNOWLEDGMENTS
The authors thank Prof. Yasuhiro Senga of the Departmentof Marine Science and Technology, Tokai University for hisvaluable comments.
REFERENCES
[1]

[2]

Sample 7

Sample 6

Sample 5

Sample 4

Sample 3

Sample 2[3]

Sample 1

Chl-a [μg/l]

Microcystis sp.
Nanochloropsis sp.

V.

The multi-excitation fluorometer, Multi-Exciter, provides
accurate excitation spectra of phytoplankton with highsensitivity for chlorophyll-a concentrations and low sensitivity
for the reflectance from suspended particles. Using amathematical process, phytoplankton groups can be estimated.
To achieve accurate classifications, the processing algorithm
should be slightly modified. New methods to identify
phytoplankton groups using specific fluorescent ratios should
be considered in the future. Distinguishing between Diatom
and some Dinoflagellate species may be possible because of
their different fluorescent properties around 470 nm [5]. The
Multi-Exciter has good potential for phytoplankton researchbeyond that of conventional fluorometers.

Figure 7. Estimates of the concentration. Colored dots indicate the estimatedchlorophyll-a concentrations, and dashed lines represent the actual
chlorophyll-a concentrations.

[4]

[5]

C. J. Lorenzen, “A method for the continuous measurement of in vivo
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S. W. Jeffrey, R. F. C. Mantoura, and S. W. Wright, Eds. Phytoplankton
Pigments in Oceanography: Guidelines to Modern Methods. Paris:UNESCO, 1997.
C. S. Yentsch and C. M. Yentsch, “Fluorescence spectral signatures: thecharacterization of phytoplankton populations by the use of excitationand emission spectra,” J. Mar. Res., vol. 37, pp. 471–483, 1979.
C. S. Yentsch and D. A. Phinney, “Spectral fluorescence: an ataxonomic
tool for studying the structure of phytoplankton populations,” J.
Plankton Res., vol. 7(5), pp. 617–632, 1985.
G. Johnsen, B. B. Prézelin, and R. V. M. Jovine, “Fluorescence
excitation spectra and light utilization in two red tide dinoflagellates,”
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