ORIGINAL PAPER
Optimization of culturing conditions of Porphyridium cruentum
using uniform design
Juan Wang Æ Bilian Chen Æ Xiaozhen Rao Æ
Jian Huang Æ Min Li
Received: 30 October 2006 / Accepted: 29 January 2007 / Published online: 10 March 2007
Ó Springer Science+Business Media B.V. 2007
Abstract The red microalga Porphyridium contains
many valuable compounds such as polysaccharides, polyunsaturated
fatty acids, and phycoerythrin (PE). In this
study, a uniform design method and regression analysis
were used to investigate the effects of initial pH, light
intensity, inoculation ratio, and liquid volume in ﬂask on
the optimal biomass, exopolysaccharides (EPS), and PE
production of Porphyridium cruentum in a batch culture at
laboratory scale. Using regression analysis, we obtained the
models to clarify the effects of individual factors and their
interactions on the biomass, EPS, and PE production of
P. cruentum. The optimal condition for the biomass was
the following: pH 5.0, light intensity 7098.0 lx, inoculation
ratio 1:17.2, and liquid volume 100.0 ml; for EPS was pH
5.0, light intensity 4501.0 lx, inoculation ratio 1:20, and
liquid volume 100.3 ml; while pH 8.0, light intensity
7100.0 lx, inoculation ratio 1:20, and liquid volume
100.3 ml was the best for PE production. The maximum
biomass 3.27 g/l, EPS production 543.1 mg/l, and PE
production 132.0 mg/l were demonstrated by conﬁrmatory
experiment to the optimum culture conditions in a
reciprocal shaker. The statistical methods used in the
present study are useful strategies for optimizing of culture
conditions for other microalgae.
Keywords Biomass Á Culture condition Á
Exopolysaccharide Á Optimization Á Phycoerythrin Á
Porphyridium cruentum Á Uniform design
Introduction
The red microalga Porphyridium is a source of biochemicals
that possess nutritional and therapeutical value. These
biochemicals include a high content of polysaccharides,
long-chain polyunsaturated fatty acids, carotenoids such as
zeaxanthin, and ﬂuorescent phycobiliproteins. The polysaccharides
of Porphyridium exhibited impressive antiviral
activity (Huleihel et al. 2001, 2002; Huang et al. 2005).
Porphyridium cruentum has been shown to contain four
biliproteins with the following amounts (percentage):
allophycocyanin (5%), R-phycoerythrin (11%), b-phycoerythrin
(%42%), and B-phycoerythrin (BPE) (%42%)
(Bermejo et al. 2001). Among the four biliproteins, BPE is
particularly useful due to its high molar absorptivity and
great ﬂuorescence properties (Ayyagari et al. 1995). It has
been also reported that BPE can be used as a pigment in the
food, cosmetic, and pharmaceutical industries. The price of
highly puriﬁed BPE in the market has been reported to be
as high as US $50 per mg (Benavides and Rito-Palomares
2006; Dufosse´ et al. 2005). The high value makes it
attractive. Therefore, it is worth developing an efﬁcient
method for the production of BPE. It is well known that
culture conditions affect the biomass of P. cruentum as
well as the quality of the exopolysaccharides (EPS) and
phycoerythrin (PE) produced. There have been only a few
reports about the effects of different culture conditions on
growth, EPS, and PE production. The main conditions
studied are light quality (You and Barnett 2004), high light
intensity and low gas ﬂow rates (Merchuk et al. 1998), pH,
stirring and mineral nutrients in the ﬂat plate glass reactors
(Singh et al. 2000), aeration and agitation (Iqbal and Zafar
1993) and renewal rate of the culture volume (Fa’bregas
et al. 1998). However, a systematic approach for the system
has not been performed.
J. Wang Á B. Chen (&) Á X. Rao Á J. Huang Á
M. Li
Department of Biotechnology, College of Life Sciences,
Fujian Normal University, Fuzhou 350108, China
e-mail: chenbil@fjnu.edu.cn
123
World J Microbiol Biotechnol (2007) 23:1345–1350
DOI 10.1007/s11274-007-9369-8
Uniform design (UD) is an experimental design strategy
for the statistical optimization of a system. This design
seeks experimental points to be uniformly scattered in the
experimental domain. It is widely accepted, especially
in situations where little is known of the theoretical function
to be modeled. The reason of its high success rate is
the economical and ﬂexible experimental runs that determine
many factors simultaneously. Therefore, UD has the
advantage that it needs far fewer trials compared with
the other statistical designs of experiments such as the
orthogonal test (Fang 1998; Fang and Qin 2003). UD has
been successfully applied in various ﬁelds such as chemistry,
chemical engineering (Liang et al. 2000; Chen et al.
2003), and biotechnology (Cao et al. 2004, 2006; Wen
et al. 2005; Chen et al. 2006). In this study, UD method
and regression analysis were applied to optimize the culture
conditions, including inoculation ratio, liquid volume,
initial pH, and light intensity, for biomass, EPS, and PE
production by P. cruentum.
Materials and methods
Organism and culture conditions
Porphyridium cruentum was obtained from the Institute
of Oceanology, Chinese Academy of Sciences (Qingdao,
China).
The algal cells were grown in 500-ml Erlenmeyer ﬂasks
for 12 days containing 100–300 ml culture. Cells were
cultivated in Koch medium, containing sterilized seawater
which was enriched with nutrients (per liter): KH2PO4,
25 mg; KNO3, 1,500 mg; MgSO4Á7H2O, 1,950 mg; Fect,
2.5 mg; NaCl, 12,500 mg; KCl, 400 mg; MgCl2Á6H2O,
1,250 mg; CaCl2Á2H2O, 600 mg; NaHCO3, 100 mg; Na2B4O7Á10H2O,
25 mg, at 25°C under constant ﬂuorescent
light at an exposure intensity of 1,000–5,000 lx in a
reciprocal shaker at 90 oscillations/min. Cultures were
inoculated at 5% (1/20) to 25% (1/4) (v/v) of the seed
culture.
Biomass determination
Biomass was measured on a dry weight basis. The cultures
were harvested and the cells were washed twice with distilled
water and dried at 60°C until constant weight was
obtained.
Phycoerythrin determination
Phycoerythrin was extracted in distilled water from the
pellets by freezing/thawing cycles. The extracts were
centrifuged and PE concentration determined spectrophotometrically
in the supernatant by measuring the absorbance
of extracts at 565, 620, and 650 nm (Tetzuya 1988).
Exopolysaccharide determination
The EPS in the medium (soluble fraction) was determined by
the phenol–sulfuric acid method (Zhang 1999) after centri-
fuging.
Uniform design
Based on the results of screening out with mono-factor
experiments (Wang et al. 2004), the experiment for optimization
of culture conditions was arranged four factors,
i.e., initial pH (X1), light intensity (X2) (lx), inoculation
ratio (X3) (v/v), and liquid volume in ﬂask (X4) (ml) by
design, each at nine levels. The UD table U9(94
) was
applied to arrange the experiments (Table 1), and each trial
was performed in triplicate. The evaluated response Y was
the content of PE or biomass or EPS.
Statistical analysis
All trials were carried out in triplicate and the average of
the biomass, EPS, and PE yield was taken as response
value. The statistical and stepwise regression analyses of
the data were done using UD DPS software (Version 7.55
by Hangzhou Reﬁne Information Tech. Co., Ltd, China).
Results and discussion
The design of the experiments and the results are shown in
Table 2. The data obtained were analyzed using the DPS
software. The equation relating the coefﬁcients obtained
for PE, biomass, and EPS production to the experimental
variable is as follows:
YPEproduction ¼ 0.21608 À 0.0011633X4 þ 0.76761X2
3
þ 0.0000024712X2
4 þ 0.00000078998X1X2
À 0.035676X1X3 À 0.000000038378X2X4ð1Þ
Ybiomass ¼ 7.82663 À 0.089748X1 À 0.050494X4
À 31.56174X2
3 þ 0.000085335X2
4
þ 0.036789X3X4 ð2Þ
YEPSproduction ¼ 719.15908 À 27.05941X1 À 1.13986X4
þ 100.58205X1X3 À 0.11557X2X3
þ 0.00017957X2X4 À 2.27495X3X4 ð3Þ
1346 World J Microbiol Biotechnol (2007) 23:1345–1350
123
In Eq. 1, Y is the response (PE), X1 is initial pH, X2 is
light intensity, X3 is inoculation ratio, and X4 is liquid
volume; with R = 0.99384, F-value = 26.8000, standard
error S = 0.00509, signiﬁcance p = 0.0364. In Eq. 2, Y is
the response (biomass), X1 the coded value of initial pH, X3
the code value of inoculation ratio and X4 the code value of
liquid volume; with R = 0.9886, F-value = 25.7717, standard
error S = 0.2011, signiﬁcance p = 0.0114. Equation 2
showed that the values of X2 (light intensity) do not appear
in the mathematical expression of the experimental design,
meaning that a different value of X2 in these ranges (1,000–
5,000 lx) had little inﬂuence on biomass. In Eq. 3, Y is the
response (EPS production), X1, X2, X3, and X4 are code
variables of initial pH, light intensity, inoculation ratio, and
liquid volume, respectively, with R = 0.99364,
F–value = 25.9409, standard error S = 17.3345, signiﬁcance
p = 0.0376.
The mathematical model was expressed in terms of the
values of all the independent variables and by neglecting
the statistically insigniﬁcant terms. The closer the value of
R is to 1, the better the correlation between the obtained
and the predicted values. The values of R were 0.99384 for
PE production, 0.9886 for biomass, and 0.99364 for EPS
production, which suggested that the experimental data be
in good agreement with predicted values. The F-value is a
ratio of the mean square from regression to the mean
square from the real error. Generally, the calculated
F-value should be several times greater than the tabulated
F-value, if the model is a good prediction of the experimental
results and the estimated factor effects are real.
Here, the computed F-values of 26.8000 for PE production,
25.7717 for biomass, and 25.9409 for EPS revealing that
this regression was statistically signiﬁcant (p < 0.05) at the
95% conﬁdence level.
The results of the regression analysis listed in
Tables 3–5. Table 3 indicated that X4 linear coefﬁcient,
which constitutes the most signiﬁcant term, i.e., liquid
volume, had a signiﬁcant effect on PE production
(p < 0.05). The interaction between initial pH and inoculation
ratio, pH and light intensity, light intensity and
liquid volume had inﬂuence on PE, but they did not
show a signiﬁcant effect. Table 4 shows that X4 linear
coefﬁcient, which constitutes the most signiﬁcant term,
i.e., liquid volume, had a signiﬁcant effect on biomass
(p < 0.01). However, X1 linear coefﬁcient, i.e., pH, did
not have signiﬁcant effect on biomass (p > 0.1). The
interaction between inoculation ratio and liquid volume
had inﬂuence on biomass, but did not show a signiﬁcant
effect. Table 5 illustrated that X4 linear coefﬁcient,
which constitutes the most signiﬁcant term, i.e., liquid
Table 2 Application of UD U9(94
) for optimization of biomass (g/l), EPS (mg/l), and PE production (mg/l)
Experiment no. Factors Response
Initial pH (X1) Light intensity (X2) Inoculation ratio (X3) Liquid volume (X4) Biomass PE EPS
1 1(5.0) 2(1,500) 4(1:8.0) 7(250.0) 0.63 58.8 343.5
2 2(5.5) 4(2,500) 8(1:4.4) 5(200.0) 0.83 71.2 380.6
3 3(6.0) 6(3,500) 3(1:10.0) 3(150.0) 1.99 82.4 458.2
4 4(6.5) 8(4,500) 7(1:5.0) 1(100.0) 2.40 112.0 500.4
5 5(7.0) 1(1,000) 2(1:13.3) 8(275.0) 0.47 66.0 254.7
6 6(7.5) 3(2,000) 6(1:5.7) 6(225.0) 0.46 48.0 352.2
7 7(8.0) 5(3,000) 1(1:20) 4(175.0) 1.02 72.0 409.1
8 8(8.5) 7(4,000) 5(1:6.7) 2(125.0) 2.19 91.5 442.7
9 9(9.0) 9(5,000) 9(1:4.0) 9(300.0) 0.34 35.2 314.2
Symbols X1, X2, X3, and X4 represent factors of pH, light intensity (lx), inoculation ratio (v/v), and liquid volume (ml), respectively. Symbols 1–9
represent the levels of each factor
Table 1 Factors and levels of the uniform design experiment
Experiment
no.
Factors
Initial pH
(X1)
Light
intensity (X2)
Inoculation
ratio (X3)
Liquid
volume (X4)
1 1(5.0) 2(1,500) 4(1:8.0) 7(250.0)
2 2(5.5) 4(2,500) 8(1:4.4) 5(200.0)
3 3(6.0) 6(3,500) 3(1:10.0) 3(150.0)
4 4(6.5) 8(4,500) 7(1:5.0) 1(100.0)
5 5(7.0) 1(1,000) 2(1:13.3) 8(275.0)
6 6(7.5) 3(2,000) 6(1:5.7) 6(225.0)
7 7(8.0) 5(3,000) 1(1:20) 4(175.0)
8 8(8.5) 7(4,000) 5(1:6.7) 2(125.0)
9 9(9.0) 9(5,000) 9(1:4.0) 9(300.0)
Symbols X1, X2, X3, and X4 represent factors of pH, light intensity
(lx), inoculation ratio (v/v), and liquid volume (ml), respectively.
Symbols 1–9 represent the levels of each factor
World J Microbiol Biotechnol (2007) 23:1345–1350 1347
123
volume, had a signiﬁcant effect on EPS production
(p < 0.05). The interaction between initial pH and inoculation
ratio, light intensity and inoculation ratio, light
intensity and liquid volume, inoculation ratio and liquid
volume had inﬂuence on EPS, but did not show a signiﬁcant
effect. On the basis of condition optimization
evaluated from the model, the optimal values of the test
variables obtained by DPS software are as follows: (1)
for high PE production, the optimum combination was
initial pH 8.0, light intensity 7100.0 lx, inoculation ratio
1:20, and liquid volume 100.3 ml. (2) for high biomass,
the combination was initial pH 5.0, light intensity
7098.0 lx, inoculation ratio 1:17.2, and liquid volume
100.0 ml. (3) for high EPS, the optimum culture condition
was initial pH 5.0, light intensity 4501.0 lx, inoculation
ratio 1:20, and liquid volume 100.3 ml. Under
these conditions, the predicted PE production, biomass,
and EPS were 123.0 mg/l, 3.29 g/l, and 538.4 mg/l,
respectively. After obtained the optimal culture conditions
by statistical designs, we tested its feasibility. The
arrangement and result of conﬁrmatory trials was as
follow, the maximal predicted value of PE yield, biomass,
and EPS were 123.0 mg/l, 3.29g/l, and 538.4 mg/l,
respectively, where the corresponding experimental response
were 132.0 mg/l for PE, 3.27 g/l for biomass, and
543.1 mg/l for EPS. The experimental values of PE
production, biomass, and EPS were in good agreement
with those of the predicted value. The optimal culture
conditions produced 133.6% increase in PE production,
140.4% increase in biomass and 98.9% increase in EPS
production when compared with that achieved in unoptimized
ﬂask cultures as light intensity 3,000 lx, inoculation
ratio 1:6.7, and liquid volume 200 ml and pH
normal.
On the effect of pH on the growth of Porphyridium,
few studies are available. Practically, the range of pH
used for the cultures is very wide since it goes from 5.0
to 8.3 (Jones et al. 1963; Nuutila et al. 1997). The pH
optimum of 8.0 estimated for PE production is close to
the one adopted by Singh et al. (1998), who obtained the
optimum growth conditions of pH 7.5, stirring and
mineral nutrients in the ﬂat plate glass reactors for EPS
production. The highest eicosapentaenoic acid production
per cell was achieved at pH 7.6 with 41.8 g/l NaCl and
12.1 mM nitrate in the production medium and using a
growth temperature of 8°C (Nuutila et al. 1997). The pH
optimum of 5.0 found by this study for biomass and EPS
was different from those reported by Singh et al. (1998)
and Nuutila et al. (1997).
Porphyridium cruentum grow over a wide range of light
intensity, but light level and spectrum greatly control the
cell growth. The growth rate in continuous light was faster
than in a dark–light cycle (Jones et al. 1963). Light quality
is a key factor for the growth and polysaccharide production.
Blue and red light could be used to improve the
efﬁciency of photosynthesis and increase the production of
extracellular polysaccharide (You and Barnett 2004). The
optimum light intensity found by this study (7100.0 lx for
PE production) is close to the one adopted by Merchuk
et al. (1998), who indicated that higher productivities of
biomass and polysaccharide were achieved in the air-lift
reactor under high light intensity and low gas ﬂow rates.
Muller-Feuga et al. (2003) obtained the light sources
delivered a continuous 206 lE/m2
/s average photon ﬂux
density to cultures reaching concentrations of 3 g/l in batch
and 0.7 g/l in chemostat.
Liquid volume in ﬂask is closely related to light intensity,
carbon dioxide supply and some rheological properties
Table 3 Results of the stepwise regression analysis for optimization
of PE production
Parameters
Signiﬁcance p t-test Coefﬁcient
X4 0.04131 3.43746 –0.92479
X3
2
0.14566 1.95430 0.81013
X4
2
0.0382 3.53415 0.92843
X1X2 0.46597 0.83298 0.50751
X1X3 0.11389 2.21195 –0.84252
X2X4 0.28355 1.30305 –0.67761
Table 4 Results of the stepwise regression analysis for optimization
of biomass
Parameters
Signiﬁcance p t-test Coefﬁcient
X1 0.21900 1.45641 –0.64358
X4 0.00907 4.73562 0.93915
X3
2
0.12487 1.93657 –0.74537
X4
2
0.02082 3.70088 0.90572
X3X4 0.15613 1.74387 0.70951
Table 5 Results of the stepwise regression analysis for optimization
of EPS production
Parameters
Signiﬁcance p t-test Coefﬁcient
X1 0.26314 1.37386 –0.6980
X4 0.04484 3.32642 –0.92028
X1X3 0.50774 0.74995 0.46850
X2X3 0.39110 0.99977 –0.57726
X2X4 0.16150 1.84952 0.79438
X3X4 0.55935 0.65474 –0.42013
1348 World J Microbiol Biotechnol (2007) 23:1345–1350
123
during the microalga cultivation processes. When algal
cells are grown in photobioreactors, the light decreases
exponentially with distance from the irradiated side of the
photobioreactor. In the case of dense cultures, this attenuation
can be very sharp. The algae near the front are
exposed to high photon ﬂux density, which allows a high
growth rate. At the core of the reactor, the cells receive less
light as a result of mutual shading and thus grow slowly.
The greater the light penetrates into the culture, the
stronger the effect would be (Merchuk et al. 1998). Liquid
volume in ﬂask strongly affected the EPS and PE production,
decreased the liquid volume which increased with
the light penetration into the liquid, allowed P. cruentum
cells exposed to high light intensity, and thus led to higher
EPS and PE production. Singh et al. (2000) indicated that
the highest concentrations of cell mass and polysaccharides
were obtained with the narrowest (1.3 cm) light path in a
ﬂat plate photobioreactor.
Concerning the optimal inoculation ratio determined, a
relatively low value is required in the present study. Higher
inoculation rapidly consumed the medium nutrients and
overall resulted in less cell growth, EPS and PE production
compared to lower inoculation. Sarada et al. (2002)
showed that inoculum volume and light intensity, sodium
nitrate and inoculum volume had signiﬁcant effect on yield
of biomass of Haematococcus pluvialis. The former
showed a positive effect while the latter one possessed a
negative effect.
Conclusions
Culture conditions are traditionally optimized by the one-ata-time
strategy, i.e., varying one factor while keeping the
others constant. Although this strategy is simple and easy as
it does not need statistical analysis, it involves a relatively
large number of experiments and the interactions among the
factors are often neglected. The results obtained in this study
showed that UD combining regression analysis is a powerful
method for the optimization of the culture conditions for
biomass, EPS, and PE production, as it not only provide
information about the interactions among the factors, but
also help to calculate the optimal conditions by using the
smallest number of experiments. The models proved to be
efﬁcient in deﬁning optimal conditions for biomass, PE, and
EPS production of P. cruentum. The successful application
of this optimization technique in this study implies that this
method is worthy applying to other culture systems for the
production of bioactive and biomass of algae.
Acknowledgments This study was funded by Development and
Reform Commission of Fujian Province of China [Minji(2003)203]
and the Natural Science Foundation of Fujian Province of China
(No. B0410008).
References
Ayyagari MS, Pande R, Kanstekar S et al (1995) Molecular assembly
of protein and conjugated polymers: toward development of
biosensors. Biotechnol Bioeng 45:116–121
Benavides J, Rito-Palomares M (2006) Simpliﬁed two-stage method
to B-phycoerythrin recovery from Porphyridium cruentum.
J Chromatogr B, online
Bermejo R, Talavera EM, Alvarez-Pez JM (2001) Chromatographic
puriﬁcation and characterization of B-phycoerythrin from Porphyridium
cruentum semipreparative high-performance liquid
chromatographic separation and characterization of its subunits.
J Chromatogr A 917:135–145
Cao Y, Zheng Y, Fang B (2004) Optimization of polymerase chain
reaction-ampliﬁed conditions using the uniform design method.
J Chem Technol Biotechnol 79:910–913
Cao Y, Xia Q, Fang B (2006) Optimization of expression of dhaT
gene encoding 1,3-propanediol oxidoreductase from Klebsiella
pneumoniae in Escherichia coli using the methods of uniform
design and regression analysis. J Chem Technol Biotechnol
81:109–112
Chen XG, Li X, Kong L et al (2003) Application of uniform design
and genetic algorithm in optimization of reversed-phase chromatographic
separation. Chemometr Intell Lab Syst 67:157–166
Chen D, Han Y, Gu Z (2006) Application of statistical methodology
to the optimization of fermentative medium for carotenoids
production by Rhodobacter sphaieroides. Process Biochem
41:1773–1778
Dufosse´ L, Galaup P, Yaron A et al (2005) Microorganisms and
microalgae as sources of pigments for food use: a scientiﬁc
oddity or an industrial reality? Trends Food Sci Tech 16:389–
406
Fa’bregas J, Garcia D, Morales E et al (1998) Renewal rate of
semicontinuous culture of the microalga Porphyridium cruentum
modiﬁes phycoerythrin, exopolysaccharide and fatty acid productivity.
J Ferment Bioeng 86:477–481
Fang KT (1998) Uniform design and uniform design table. Science
Press, Beijing, pp 1–5
Fang KT, Qin H (2003) A note on construction of nearly uniform
designs with large number of runs. Stat Probab Lett 16:215–224
Huang J, Chen B, You W (2005) Studies on separation of
extracellular polysaccharide of Porphyridium cruentum and its
anti-HBV in vitro. Chin J Mar Drugs 24(5):18–21
Huleihel M, Ishanu V, Tal J et al (2001) Antiviral effect of red
microalgal polysaccharides on herpes simplex and varicella
zoster viruses. J Appl Phycol 13:127–134
Huleihel M, Ishanu V, Tal J et al (2002) Activity of Porphyridium
sp. polysaccharide against herpes simplex viruses in vitro and
in vivo. J Biochem Biophys Methods 50:189–200
Iqbal M, Zafar SI (1993) Strategies toward optimization of cultural
conditions of Porphyridium cruentum for higher polysaccharide
production. Acta Microbiol Pol 42:71–82
Jones RE, Speer HL, Kury W (1963) Studies on the growth of the red
alga Porphyridium cruentum. Physiol Plant 16:636–643
Liang YZ, Fang KT, Xu QS (2000) Uniform design and its
applications in chemistry and chemical engineering. Chemometr
Intell Lab Syst 53:21–35
Merchuk JC, Ronen M, Giris S et al (1998) Light/dark cycles in the
growth of the red microalga Porphyridium sp. Biotechnol
Bioeng 59:705–713
World J Microbiol Biotechnol (2007) 23:1345–1350 1349
123
Muller-Feuga A, Gue´des RL, Pruvost J (2003) Beneﬁts and limitations
of modeling for optimization of Porphyridium cruentum
cultures in an annular photobioreactor. J Biotechnol 103:153–
163
Nuutila MA, Aura AM, Kiesvaara M (1997) The effect of salinity,
nitrate concentration, pH and temperature on eicosapentaenoic
acid (EPA) production by the red unicellular alga Porphyridium
purpureum. J Biotechnol 55:55–63
Sarada R, Bhattacharya S, Ravishankar GA (2002) Optimization of
culture conditions for growth of the green alga Haematococcus
pluvialis. World J Microbiol Biotechnol 18:517–521
Singh S, Arad (Malis) S, Richmond A (2000) Extracellular polysaccharide
production in outdoor mass cultures of Porphyridium
sp. in ﬂat plate glass reactors. J Appl Phycol 12:269–275
Tetzuya K (1988) Phycobilisome stability. Meth Enzymol 167:313–
318
Wang J, Chen B, Wang M et al (2004) Effect of culture conditions on
the growth and metabolites production of Porphyridium cruentum.
J Fujian Norm Univ 20(3):63–66
Wen S, Zhang T, Tan T (2005) Optimization of the amino acid
composition in glutathione fermentation. Process Biochem
40:3474–3479
You T, Barnett ST (2004) Effect of light quality on production of
extracellular polysaccharides and growth rate of Porphyridium
cruentum. Biochem Eng J 19:251–258
Zhang W (1999) Biochemical technique of glycoconjugates. Zhejiang
University Press, Hangzhou, pp 11–12
1350 World J Microbiol Biotechnol (2007) 23:1345–1350
123