Optimization of lipids’ ultrasonic extraction and production from Chlorella sp. using response-surface methodology
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Abstract
Background
Three steps are very important in order to produce microalgal lipids: (1) controlling microalgae cultivation via experimental and modeling investigations, (2) optimizing culture conditions to maximize lipids production and to determine the fatty acid profile the most appropriate for biodiesel synthesis, and (3) optimizing the extraction of the lipids accumulated in the microalgal cells.
Methods
Firstly, three kinetics models, namely logistic, logistic-with-lag and modified Gompertz, were tested to fit the experimental kinetics of the Chlorella sp. microalga culture established on standard conditions. Secondly, the response-surface methodology was used for two optimizations in this study. The first optimization was established for lipids production from Chlorella sp. culture under different culture conditions. In fact, different levels of nitrate concentrations, salinities and light intensities were applied to the culture medium in order to study their influences on lipids production and determine their fatty acid profile. The second optimization was concerned with the lipids extraction factors: ultrasonic’s time and temperature, and chloroform-methanol solvent ratio.
Results
All models (logistic, logistic-with-lag and modified Gompertz) applied for the experimental kinetics of Chlorella sp. show a very interesting fitting quality. The logistic model was chosen to describe the Chlorella sp. kinetics, since it yielded the most important statistical criteria: coefficient of determination of the order of 94.36%; adjusted coefficient of determination equal to 93.79% and root mean square error reaching 3.685 cells · ml^{− 1}.
Nitrate concentration and the two interactions involving the light intensity (Nitrate concentration × light intensity, and salinities × light intensity) showed a very significant influence on lipids production in the first optimization (p < 0.05). Yet, only the quadratic term of chloroform-methanol solvent ratio showed a significant influence on lipids extraction relative to the second step of optimization (p < 0.05).
The two most abundant fatty acid methyl esters (≈72%) derived from the Chlorella sp. microalga cultured in the determined optimal conditions are: palmitic acid (C16:0) and oleic acid (C18:1) with the corresponding yields of 51.69% and 20.55% of total fatty acids, respectively.
Conclusions
Only the nitrate deficiency and the high intensity of light can influence the microalgal lipids production. The corresponding fatty acid methyl esters composition is very suitable for biodiesel production. Lipids extraction is efficient only over long periods of time when using a solvent with a 2/1 chloroform/methanol ratio.
Keywords
Microalgae Lipids Extraction Biodiesel Production Response-surface methodologyAbbreviation
- Adj R^{2}
Adjusted determination coefficient (dimensionless)
- p
Value of probability (dimensionless)
- R^{2}
Determination coefficient (dimensionless)
- RMSE
Root Means Squared Error
- SD
Standard Deviation
- SSE
Sum of Squared Errors
- t
Student coefficient (dimensionless)
- X
cell number per mL
- μ_{max}
Maximum specific growth rate (day^{− 1})
Background
The biofuel derived from microalgae is considered one of the most promising and important renewable energy sources because of its many advantages. First, microalgae are photosynthetic microorganisms that adapt rapidly to new environments. In addition, compared to other bioenergy sources like soybean, corn, …, microalgae are characterized by high growth rates, high lipid production capacity, high CO_{2} fixation rates, and low cultivation space requirement [1]. The resultant biofuel is environment-friendly and does not exacerbate the carbon footprint.
Three important steps can be considered to produce biofuel from microalgae. First, controlling microalgae cultivation is a step of paramount importance. Actually, growth kinetic models are needed to approve this objective, and, thereafter, culture conditions can be optimized. In the corresponding literature, many models were established in order to study the growth kinetics of many microalgae and to determine the growth kinetics’ characterizations [1, 2, 3, 4].
Second, the optimization of culture conditions to maximize lipids productivity is another key step. Many authors are currently working towards the identification of the optimal culture conditions in order to maximize lipids accumulation in microalgal cells. The most studied factors in this vein are: light intensity, salinity, pH, nitrogen limitation, phosphorus limitation, … etc. [1, 3, 5, 6, 7].
The third crucial step is extraction optimization of the lipids accumulated in the microalgal cells. It consists in the working on lipids extraction that is in continuous development. Several methods have been used to perform a pretreatment leading to the effective extraction of lipids [8, 9]. Ultrasonic extraction is among the newest lipids extraction technologies. Ultrasonic exposition with the aim of lipids extraction showed the highest performance compared to other techniques [8]. The optimization of the relative conditions can be very useful in the biodiesel field. About the used solvents for lipids extraction, Folch et al. [10] and Park et al. [9] recommend chloroform/methanol as better solvent mixture for lipids extraction from microalgae. In fact, it is more suitable for microalgal lipids compared to other mixtures (e.g. Hexane; Hexane/Methanol).
This work aims to range over three principal subjects: the experimental and modeling studies of the growth kinetics of Chlorella sp. microalga, the optimization of culture conditions using three principal factors (nitrate limitation, salinity and light intensity), and the optimization of ultrasonic extraction in function of three factors (time, temperature, and chloroform/methanol-solvents ratio). The determination of the fatty acids profile produced from the obtained lipids can help us to quantify the biodiesel quality.
Methods
Conditions of Chlorella sp. cultivation
A Chlorella sp. microalga was preserved into a 1000 mL-Erlenmeyer flask containing 50 mL of inoculum and 250 mL of F/2-standard seawater medium consisting of (per liter): 1 mL of NaNO_{3} (75 g · L^{− 1}), 1 mL of NaH_{2}PO_{4} (5 g · L^{− 1}), 1 ml of metal solution, and 0.5 mL of vitamin solution.
Cultures of Chlorella sp. were maintained at 25 °C and continuously illuminated at a photosynthetic light intensity of 160 μmol photons · m^{− 2} · s^{− 1} (TL5 tungsten filament lamps; Philips Co., Taipei, Taiwan) in three replicates.
For the optimization of culture conditions, the microalga (a) was grown into a 250 mL-Erlenmeyer flask containing 150 mL of culture medium composed of inoculum (10%), a modified-F/2 medium, and (b) exposed to different photosynthetic light intensities.
Modeling of experimental kinetics
Used models for cells growth kinetics prediction
Model | Expression | Parameters | Equation n° |
---|---|---|---|
Logistic | \( X(t)=\frac{X_0\cdot {e}^{\left({\mu}_{\mathrm{max}}\cdot t\right)}}{1-\frac{X_0}{X_{\mathrm{max}}}\cdot \left(1-{e}^{\left({\mu}_{\mathrm{max}}\cdot t\right)}\right)} \) | μ_{max}; X_{max} | (1) |
Logistic- with-lag | \( X(t)={X}_0+\frac{X_{\mathrm{max}}-{X}_0}{1+{e}^{\left\{\left(\frac{4\cdot {\mu}_{\mathrm{max}}}{X_{\mathrm{max}}-{X}_0}\right)\cdot \left(\lambda -t\right)+2\right\}}} \) | μ_{max}; X_{max}; λ | (2) |
Modified- Gompertz | \( X(t)={X}_0+\left({X}_{\mathrm{max}}-{X}_0\right)\cdot {e}^{\left\{-{e}^{\left(\frac{\mu_{\mathrm{max}}\cdot {e}^1}{X_{\mathrm{max}}-{X}_0}\right)\cdot \left(\lambda -t\right)+1}\right\}} \) | μ_{max}; X_{max}; λ | (3) |
A Matlab algorithm was carried out and applied to identify the models’ parameters using the fitting procedure consisting in the comparison the experimental data to the calculated ones. In fact, this procedure of Chlorella sp. growth data was established using non-linear least squares regression method. The determination coefficient (R^{2}), the adjusted determination coefficient (Adj R^{2}), the sum of squared errors (SSE), and the root means squared error (RMSE) were chosen in this work to quantify the models fitting quality. All models’ coefficients were determined with a 95% confidence interval (corresponding to p < 0.05).
Lipids production optimization
Established experiments for lipid content production and experimental response
Runs | Type | Block | [NaNO_{3}] (mL · L^{− 1}) | [NaCl] (−) | Light Intensity (μmol · m^{− 2 }· s^{− 1}) | Lipid content (%) | |
---|---|---|---|---|---|---|---|
Essay 1 | Essay 2 | ||||||
1 | Factorial points | 1 | 0 | 16 | 153.2 | 11.2 | 6.1 |
2 | 1 | 2 | 16 | 153.2 | 8.2 | 6.5 | |
3 | 1 | 0 | 32 | 153.2 | 12.6 | 12.1 | |
4 | 1 | 2 | 32 | 153.2 | 8.6 | 14.7 | |
5 | 1 | 0 | 16 | 311.1 | 15.8 | 15.3 | |
6 | 1 | 2 | 16 | 311.1 | 7.1 | 7.2 | |
7 | 1 | 0 | 32 | 311.1 | 12.8 | 10.1 | |
8 | 1 | 2 | 32 | 311.1 | 6.8 | 4.2 | |
9 | Star points | 2 | 0 | 24 | 163.6 | 4.9 | 5.4 |
10 | 2 | 2 | 24 | 163.6 | 16.1 | 9.9 | |
11 | 2 | 1 | 16 | 163.6 | 18.0 | 18.1 | |
12 | 2 | 1 | 32 | 163.6 | 19.0 | 17.3 | |
13 | 2 | 1 | 24 | 153.2 | 17.3 | 18.6 | |
14 | 2 | 1 | 24 | 311.1 | 14.7 | 14.7 | |
15 | Center points | 2 | 1 | 24 | 163.6 | 15.5 | 19.7 |
Extraction optimization
where W_{L} (g): extracted lipids weight and W_{A} (g): dry algae biomass.
Established experiments for extraction process and experimental response
Runs | Type | Time (min) | Temperature (°C) | Chloroform/Methanol (v/v) | Lipid content (%) | |
---|---|---|---|---|---|---|
Essay 1 | Essay 2 | |||||
1 | Factorial points | 6 | 30 | 1/1 | 15.8 | 14.4 |
2 | 30 | 30 | 1/1 | 13.3 | 14.2 | |
3 | 6 | 60 | 1/1 | 8.4 | 9.3 | |
4 | 30 | 60 | 1/1 | 7.9 | 9.4 | |
5 | 6 | 30 | 3/1 | 13.4 | 10.3 | |
6 | 30 | 30 | 3/1 | 11.6 | 12.7 | |
7 | 6 | 60 | 3/1 | 8.5 | 7.4 | |
8 | 30 | 60 | 3/1 | 12.9 | 12.5 | |
9 | Star points | 6 | 45 | 2/1 | 13.8 | 13.6 |
10 | 30 | 45 | 2/1 | 19.1 | 20.3 | |
11 | 18 | 30 | 2/1 | 10.4 | 10 | |
12 | 18 | 60 | 2/1 | 21.4 | 22.6 | |
13 | 18 | 45 | 1/1 | 5.9 | 7.9 | |
14 | 18 | 45 | 3/1 | 13.6 | 13.2 | |
15 | Center points | 18 | 45 | 2/1 | 7.8 | 7.4 |
Analysis of fatty acid methyl esters
Fatty acid analysis was performed after lipid extraction. The esterification of total fatty acids was carried out by a catalyst dissolved in methanol.
In the present work, the obtained lipid quantity was poured in 200 μl of hexane and 100 μl of KOH (1 N KOH in 2 N methanol) for the methylation, i.e. the sum of the interactions which take place between fatty acids and methanol. The fatty acids were analyzed by gas chromatography (GC) with electron ionization (70 eV), capillary column (length of 30 m, inner diameter of 0.25 mm and film thickness of 1 μm) and helium (1 ml min^{-1}) as carrier gas. 7 μl of the sample were injected with a dilution of 1/5 at a temperature set at 200 °C. The oven temperature was initially maintained at 50 °C for 1.5 min, then increased sequentially to 150 °C with a 15 °C · min^{-1} ramp for 8 min and finally at 200 °C (15 °C · min^{-1}) for 23 min. The pressure is set at 165 kPa [11].
Modeling and statistical study
The coefficients β_{0}, β_{ i }, β_{ ij }, and β_{ ii } were calculated by the mean square method using the experimental matrix shown in Tables 2 and 3.
All statistical tests were performed with STATISTICA 13.0 Software, StatSoft, Inc.. The chosen confidence interval is of the order of 95%, corresponding to p < 0.05.
Results
Experimental kinetics and modelling results
Kinetics modeling results
Model | Model’s parameters | Statistical parameters | |||||
---|---|---|---|---|---|---|---|
μ_{max} (day^{− 1}) | X_{max} (cells·mL^{− 1}) | λ (day) | R^{2} (%) | Adj R^{2} (%) | SSE (cells · mL^{−1})^{2} | RMSE (cells · mL^{− 1}) | |
Logistic | 0.5778 | 5.582 10^{4} | – | 94.36 | 93.79 | 135.8 | 3.685 |
Logistic- with-lag | 9966 | 5.453 10^{4} | 7.182 | 93.21 | 91.71 | 163.3 | 4.260 |
Modified- Gompertz | 5541 | 6.017 10^{4} | 4.250 | 93.34 | 91.86 | 160.3 | 4.220 |
The obtained value of μ_{max} corresponding to 0.0242 h^{− 1} is higher than those obtained in the case of the Tetraselmis sp. microalga in different photoautotrophic conditions [2, 4]. In addition, it can be considered like competitive to results obtained in the case of Chlorella vulgaris grown with a CO_{2} biofixation and considering the coupled effects of light intensity and dissolved inorganic carbon via photobioreactor [13].
Optimization of lipids production
Student test results for lipids production
Coefficient | SD of coefficient | t | p-value | |
---|---|---|---|---|
Constant | −32.738 | 93.242 | − 0.351 | 0.729 |
Block | −0.828 | 2.336 | −0.354 | 0.727 |
[NaNO_{3}] | 21.260 | 5.592 | 3.802 | 0.001^{b} |
[NaNO_{3}]^{2} | −8.525 | 2.413 | −3.533 | 0.002^{b} |
[NaCl] | 0.111 | 1.827 | 0.061 | 0.952 |
[NaCl]^{2} | 0.008 | 0.038 | 0.207 | 0.838 |
Light intensity | 0.371 | 0.751 | 0.494 | 0.627 |
Light intensity^{2} | −0.001 | 0.002 | −0.389 | 0.701 |
[NaNO_{3}] × [NaCl] | 0.048 | 0.087 | 0.547 | 0.590 |
[NaNO_{3}] × Light intensity | −0.028 | 0.008 | −3.453 | 0.003^{b} |
[NaCl] × Light intensity | −0.002 | 0.001 | −2.247 | 0.037^{a} |
It is clear that the nitrate concentration has a very important influence on lipids production, since both simple and quadratic terms of [NaNO_{3}] are highly significant (Table 5, p < 0.005). In fact, the corresponding coefficients (21.260 and − 8.525, respectively) are very high compared to the other ones. The simple-effect nitrate concentration has a positive influence on lipids production, while the quadratic effect decreases it. Both interactions involving the light intensity factor, i.e. [NaNO_{3}] × Light intensity (p < 0.005) and [NaCl] × Light intensity (p < 0.05) are very significant because of the importance of their coefficients compared to the corresponding standard error. Negative effects are noted for those interactions, decreasing the lipids production.
Having blocks not significant is an important result for our study, because this means that all results are reproducible (p > 0.05). Results are also repeatable because of the low values of standard deviation SD of experimental data (0 ≤ SD ≤ 4.38). In fact, it can be seen that all repeated experimental lipids contents are in the same order for each condition (Table 2). This explains the low values of SD and the repeatability of experimental results. The obtained model presents an acceptable quality of fitting for experimental data with an R^{2} of 78.46% and an Adj R^{2} of 67.13%.
Optimization of lipids extraction
Student test results for lipids extraction
Factors | Coefficient | SD of coefficient | t | p-value |
---|---|---|---|---|
Constant | 19.721 | 16.951 | 1.163 | 0.258 |
Time | − 0.689 | 0.573 | −1.203 | 0.243 |
Time^{2} | 0.013 | 0.013 | 0.972 | 0.343 |
Temperature | −0.712 | 0.773 | −0.920 | 0.369 |
Temperature^{2} | 0.005 | 0.008 | 0.652 | 0.522 |
Ch/M | 15.154 | 8.360 | 1.813 | 0.085 |
(Ch/M)^{2} | −4.728 | 1.875 | −2.521 | 0.020^{a} |
Time × Temperature | 0.004 | 0.006 | 0.658 | 0.518 |
Time × Ch/M | 0.069 | 0.089 | 0.776 | 0.447 |
Temperature × Ch/M | 0.067 | 0.071 | 0.941 | 0.358 |
Fatty acids profiles proprieties
The produced lipids in optimal conditions were analyzed. Fatty acids profiles showed the presence of seven fatty acid methyl esters: 0.43% of lauric acid (C12:0), 1.22% of myristic acid (C14:0), 51.69% of palmitic acid (C16:0), 20.55% of oleic acid (C18:1), 7.84% of linoleic acid (C18:2), 7.78% of linolenic acid (C18:3), and 10.5% of arachidic acid (C20:0).
In fact, it is clear that the fatty acid methyl esters derived from the Chlorella sp. microalga cultured at the determined optimal conditions are characterized by a high amount of saturated and monounsaturated chain fatty acids of ≈72% of total fatty acids (principally C16:0 and C18:1) and a low-level amount of polyunsaturated chain fatty acids of ≈15% of total fatty acids (C18:2 and C18:3). This is very suitable for high-quality biodiesel production [5, 15, 20, 21]. In fact, biodiesel oxidation stability is always affected by the high level of polyunsaturated fatty acids, that tend to oxidize rapidly. This impacts the storage stability negatively, which is critical for fuel applications [5, 20].
The obtained results are very similar to those of previous works showing different stress conditions applied during culture of different microalgae [5, 15, 20, 21].
Conclusion
Three principal axes were treated in this work. First of all, the experimental culture kinetics of Chlorella sp. were established in standard medium. The modelling of the experimental data was also carried out via three models: logistic, logistic-with-lag and modified Gompertz. The most important model generating the best fitting quality is the logistic model. The second part of the present work was interested in the optimization of lipids production from Chlorella sp. via response-surface methodology. The better condition given the maximum lipids production is 1 mL · L^{− 1} of [NaNO_{3}], 32 of salinity, and high light intensity of 271.63 μmol · m^{− 2} · s^{− 1}. The corresponding fatty acid profile makes Chlorella sp. microalga a viable alternative to traditional sources of fossil fuel and an important feedstock source of high-quality biofuel. The third part is about the optimization of ultrasonic extraction of lipids from Chlorella sp. microalgal cells. The studied factors were the ultrasonic extraction period, the temperature and the chloroform-methanol solvents ratio. The best extraction can be established with longer time (30 min), higher temperature (60 °C) and with a chloroform-methanol solvents ratio of 2/1.
Notes
Acknowledgements
The authors thank the Tunisian Ministry of higher education and scientific research for providing the funding for this research.
Funding
This work received financial support from « Ministère de l’enseignement supérieur et de la recherche scientifique ». The funding organisms had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials
The dataset supporting the conclusions of this article is included within the article.
Authors’ contribution
BH, IA, MD and MB carried out characterization of microalga and different molecules. BH carried out the statistical analysis. BH, IA, MD, MB, IF and SA participated in the design of the study. BH, IA, MD, MB, IF and SA conceived the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not Applicable.
Consent for publication
Not Applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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