Real-Time and In Situ Evaluation of Phycocyanin Concentration in Spirulina platensis Cultivation System by Using Portable Raman Spectroscopy

Abstract

Spirulina platensis can synthesize a large amount of phycocyanin, which had been developed as a health food. At the same time, Spirulina can absorb the nitrogen and phosphorus in wastewater and provide for its own growth. Here, we studied the optimal nitrogen and phosphorus supply for the Spirulina production process. For the first time, 405 nm portable Raman spectrometer was used to estimate phycocyanin content for real-time industrial applications. We obtained three Raman characteristic peaks of phycocyanin through density functional theory combined with home-built Raman spectrometer, which were 1272, 1337, and 1432${\text{cm}}^{-1}$. There was a good linear correlation between the sum of the three peak intensities and the PCL concentration (y = 18.887x + 833.530, $R^2=0.890$). The least squares support vector machine model based on the characteristic peaks was used to estimate the concentration of phycocyanin and obtained good results with a correlation coefficient of prediction of 0.907 and residual predictive deviation of 3.357. The results can provide decision-making for integration of Spirulina effluent treatment and phycocyanin production and provide references for real-time Spirulina-based biorefinery applications.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

With the development of industry and agriculture, the living standard has been continuously improved. However, it has also produced a lot of wastewater, which has caused many pollution problems. Due to the large amount of N and P in the wastewater, the problem of eutrophication appears, which needs to be effectively controlled and solved. Algae have strong heterotrophic and polytrophic capabilities, which can recycle nutrients in wastewater and produce high value-added products. It is therefore considered to be ideal biomaterials for the comprehensive utilization of wastewater. Since this century, the research focused on wastewater treatment accounted for 25% of the algae-related research [1]. Gao et al. [2] used a membrane photobioreactor to develop a highly efficient algae cultivation process for removing nutrients from wastewater. The treatment was generally to use the organic substances in the wastewater to support growth for algae and then achieve the purpose of purifying the wastewater. During the growth of algae, many valuable products will be produced, such as phycocyanin, lipids, and pigment [3]. Spirulina platensis can synthesize phycocyanin and can be used for photosynthesis and heterotrophic cultivation [4]. As a rare water-soluble blue pigment-protein complex, phycocyanin had wide application prospects in beverage coloring, health food, biotechnology, and other industries [5]. Wang et al. [6] used livestock and poultry farming wastewater as a medium to cultivate S. platensis. The results showed it not only had rich biomass but also can remove the nitrogen (N) and phosphorus (P) in wastewater effectively. However, it will cause stress on the growth of S. platensis when the concentration of N and P was not suitable [7, 8]. Therefore, it is particularly important to find a suitable concentration of N and P that is conducive to the accumulation of the phycocyanin. In this production process, dynamic and real-time monitoring of phycocyanin content is very necessary. As commonly detection methods, spectrophotometry and fluorophotometer always have good accuracy but are of high cost [9]. Moreover, these methods require complicated and destructive preprocessing, which are tedious and time-consuming. Besides, the temperature and storage time will affect the stability of phycocyanin. Therefore, the development of rapid nondestructive methods for the detection of phycocyanin is very significant for the development of dynamic cultivation strategies. The Raman technology was widely used in the detection of biomacromolecules in microalgae, such as pigments [10, 11], proteins [12], lipids [13, 14], and the mechanism of intracellular biochemical reactions [15, 16]. The advantages of very weak influence from aqueous solution and no pretreatment make it possible for rapid in situ determination of phycocyanin during the cultivation process. Jehlicka and Oren [17] used a handheld Raman spectrometer to detect phycocyanin in cyanobacteria. Ando et al. [18] used micro-Raman spectroscopy to obtain high signal-to-noise ratio spectra of phycocyanin in a single living cyanobacteria cell. At present, the detection of phycocyanin by Raman spectroscopy was mainly focused on qualitative aspects, but there were few studies on the quantitative detection of phycocyanin. With the gradual expansion market of phycocyanin, convenient and rapid quantitative monitoring methods are needed to guide industrial production. The main analytical procedure of this study is displayed in Figure 1, which include the following: (1) to find the most suitable concentration of N and P that can support the growth of Spirulina to achieve the dual effects of wastewater purification and cost saving, (2) to find the Raman characteristic peak of phycocyanin, and (3) to achieve in situ rapid detection of phycocyanin by Raman spectroscopy.

[figure omitted; refer to PDF]

2. Materials and Methods

2.1. Algae Species and Culture Conditions

Spirulina platensis (FACHB-882) was purchased from the Wildlife Germplasm Bank of Chinese Academy of Sciences. Spirulina was cultivated in a triangular flask and expanded for cultivation using the Spirulina medium (SP medium) [19] under controlled conditions (25°C, 8000 μmol (photon), 12 h day). According to the previous analysis, wastewater containing N and P could be used to cultivate Spirulina to accumulate phycocyanin. The experimentation was first carried out to investigate the appropriate N and P supply for S. platensis cultivation. Chemical reagent NH₄Cl was supplied as the N source, while K₂HPO₄ was supplied as the P source. A blend of three N treatment was used along with three different P levels, and the formulation of these grades is presented in Table 1. Samples that were cultivated using the SP medium were regarded to be the control group, in which the concentration of NaNO₃ was 2.5 g/L and the K₂HPO₄ was 0.5 g/L. During the experiment, dry cell weight (DCW), phycocyanin content in liquid (PCL), and phycocyanin content in dried Spirulina (PCD) were measured 0, 4, 7, 11, 14, and 18 days after treatment.

Table 1

Concentration of culture medium components in the experimental group.

Group	NH4Cl (g/L)	${\text{K}}_2$HP${\text{O}}_4$ (g/L)
N1P1	0.1	0.02
N1P2	0.1	0.42
N1P3	0.1	0.82
N2P1	0.2	0.02
N2P2	0.2	0.42
N2P3	0.2	0.82
N3P1	0.3	0.02
N3P2	0.3	0.42
N3P3	0.3	0.82

2.2. Biochemical and Physiological Parameter Measurements

DCW, a proxy of Spirulina biomass, was determined by the gravimetric method. The algal muds of each sample were wrapped in tin foil tray and dried at 40°C to stable weights. The DCW was calculated as$$\begin{array}{}\text{(1)}& \text{DCW}=\frac{m_1-m_0}{V},\end{array}$$where DCW was the dry cell weight of Spirulina (g/L), m₁ (g) was the weight of the foil tray (g), m₀ (g) was the total weight of tin foil tray and dried algae mud, and V was the volume of algal fluid (L).

Transfer the dried Spirulina to a centrifuge tube, and add liquid nitrogen to freeze the cells in order to make the cell walls more fragile. After complete evaporation of the liquid nitrogen, the wall began to grind and crack. It was then shaken to dissolve completely the phycocyanin, the mixture was centrifuged at 8000 r/min, and take the supernatant as the detection solution. The phycocyanin was detected by a dual-beam ultraviolet spectrophotometer (TU-1901, Beijing General Analysis, China). There were two absorption peaks at 348 and 620 nm (Figure 2(a)), which was consistent with the result in some research studies [20]. The phycocyanin standard curve (Figure 2(b)) was obtained at 620 nm. The content of phycocyanin in liquid (PCL) was calculated as$$\begin{array}{}\text{(2)}& \text{PCL}=406.479A_{620}-0.213\frac{V_1}{V},\end{array}$$where PCL was the content of phycocyanin in liquid (mg/L), A₆₂₀ was the absorbance value at 620 nm, V₁ was the volume of phosphate buffer (L), and V was the volume of Spirulina fluid (L).

[figures omitted; refer to PDF]

The phycocyanin content in dried Spirulina (PCD) was calculated as$$\begin{array}{}\text{(3)}& \text{PCD}=\frac{\text{PCL}}{\text{DCW}}\text{,}\end{array}$$where PCD was the phycocyanin in dry algae (mg/g), PCL was the content of phycocyanin in liquid (mg/L), and DCW was the dry cell weight of Spirulina (g/L).

2.3. Raman Spectrum Acquisition

The portable Raman spectrometer in this study was designed and built by our own institute. The devices of this system (Figure 3) include a 405 nm Raman probe, a Raman spectrum detector, a liquid sample cell with blackout cover, a laptop with XRaman software, an optical fiber, and a shading box. Calibrate the instrument by a 405 nm excitation wavelength before spectrum acquisition. The parameters were set as follows: a scanning range of 151 to 3000 cm⁻¹, an integration time of 8000 ms, and an average spectral value of twice. When collecting the spectrum of samples, 1.5 ml of Spirulina liquid was added into a 2 mL quartz bottle and then placed at a liquid sample pool. Since the spectrum may be affected by noise and fluorescence, we measured 4 replicates for each sample. 96 spectral data were obtained, and it was divided into a calibration set of 64 individuals and a prediction set of 32 individuals, before constructing the prediction model.

[figure omitted; refer to PDF]

2.4. Analytical Methods

2.4.1. Density Functional Theory

Density functional theory (DFT) was a quantum mechanical method used to study the electronic structure of multielectron systems with varieties of calculation functions and basis sets [21]. In order to explain mechanism behind the formation of these absorption peaks, quantum chemistry calculations of the phycocyanin chromophore molecule were carried out using Gaussian.v09 (Gaussian, Inc., Wallingford, CT, USA). The hybrid functional model of B3LYP with the 6-311G basis set was used to optimize the single molecular structure of phycocyanin and simulate its DFT spectrum. The molecular vibration modes of each peak were determined by comparing the experimental and theoretical spectra.

2.4.2. Predictive Model

In this step, least squares support vector machine (LSSVM) was used to establish a quantitative detection model of phycocyanin. LSSVM is a regression method based on support vector machine (SVM) algorithm. It has certain applications in qualitative and quantitative analyses of spectra [22]. The LSSVM model transforms inequality constraints of SVM into equality constraints, which help to reduce the problem to solve linear equations. The LSSVM model was operated with the redial basis function as a kernel function. The sig² (${\sigma }^2$) and gam (γ) were determined based on the grid search technique. The ${\sigma }^2$ represents the bandwidth of redial basis function kernel. γ represented the tradeoff between minimizing model complexity and minimizing the training error.

2.5. Evaluation of Model Performance

The performance of LSSVM model was evaluated by calculating the coefficients of determination in calibration sets ($R_{\text{cal}}^2$) and prediction sets ($R_{\text{pre}}^2$), as well as root mean square error in calibration sets (RMSEC) and prediction sets (RMSEP). The metric residual predictive deviation (RPD) expresses the ratio of the standard deviation of the data set and RMSEP, which can evaluate the predictive ability of the model. It is generally believed that when 1.75 < RPD < 2.25, the model is valid; when 2.25 < RPD < 3, the model is more successful; 3 < RPD < 4, the model is successful; when RPD > 4, the model is very successful. Therefore, the larger the RPD value is, the better the model is.

2.6. Software

Matlab®2018b (The Math-Works, Natick, MA, USA) was used for regression model establishment and calculation of model evaluation parameters. Gaussian.v09 (Gaussian, Inc., Wallingford, CT, USA) was used to do the DFT calculation. The SPSS ver.22 (SPSS Inc., Chicago, IL, USA) was used to analyze the biochemical and physiological parameters. Comparisons of response value of DCW, PCD, and PCL were made using ANOVA. The significance of variables was determined using the F test ($p$ < 0.01).

3. Results and Discussion

3.1. Environmental Impact of Spirulina Growth and Phycocyanin Accumulation

3.1.1. Effects of Different N and P Levels on Spirulina Growth and Phycocyanin Accumulation

DCW represented the biomass of Spirulina, and its trend over time is shown in Figure 4(a). In the control group and N1 group, the biomass of Spirulina increased significantly over time. However, with the increased N supply (N2 and N3), the DCW began to decrease. These results indicated that high N supply will cause stress and hinder the growth of Spirulina [23]. In the N1 group, the biomass in N1P2 and N1P3 was higher than N1P1. In the N2 group, the biomass in N2P3 had a certain increase, while the group with a lower concentration of P (N2P1 and N2P2) showed a downward trend. In the N3 group, the higher the concentration of the P, the more gradual the downward trend. These results may indicate that when the Spirulina was damaged by high concentration of N, the high concentration of P can protect the Spirulina and then increase the biomass [24]. The overall trend of PCL (Figure 4(b)) was similar to DCW. When the concentration of N was low, the biomass tended to increase. At higher N concentrations, the PCL was gradually decreasing. The trend of PCD (Figure 4(c)) was obviously different from DCW and PCL. In the N1 group, the N1P1 remained unchanged, and the NIP3 group continued to increase. In addition, the N1P2 continued to increase, while the curve was higher than the normal group. This indicated that when the concentration of N was appropriate, the medium concentration of P was more suitable for the accumulation of phycocyanin.

[figures omitted; refer to PDF]

3.1.2. Research on Optimal Environment Conditions

On the 18th day, the average response value of DCW, PCL, and PCD of 9 experimental groups was analyzed by SPSS (Table 2). For the average response values of DCW, PCL, and PCD, the range of N (0.284, 58.858, and 96.093) was greater than the range of P (0.048, 14.004, and 43.496), which indicated that the N had a greater impact on the growth of Spirulina. In order to further analyze the extent of the effects of N and P, we conducted an analysis of variance (Table 3). The influence of N on DCW ($p$ < 0.001), PCL ($p$ < 0.001), and PCD ($p$ = 0.001) was very significant, and all were greater than P. Therefore, it can be considered that the N was the main factor and the P was the secondary factor in this experiment. According to the average of the response values in Table 3, N1P3 was the best cultivation conditions. However, the analysis of variance found that the influence of P ($p$ = 0.065) on PCL was not very significant, and multiple comparison analysis was conducted (Table S1). The results showed that P2 and P3 have little difference between the experimental results. In Section 3.1.1, we had found that the medium concentration of P was more suitable for the growth of Spirulina when the concentration of N was appropriate. Therefore, the optimal condition was finally determined as N1P2 (NH₄Cl 0.1 g/L and K₂HPO₄ 0.42 g/L).

Table 2

The response value of dry cell weight (DCW), phycocyanin content in liquid (PCL), and phycocyanin content in dried Spirulina (PCD) in different culture conditions.

	N1	N2	N3	RN	P1	P2	P3	RP
DCW	0.332	0.130	0.048	0.284	0.147	0.168	0.195	0.048
PCL	63.643	17.081	4.785	58.858	20.962	29.580	34.966	14.004
PCD	190.997	126.881	94.094	96.093	113.588	141.300	157.084	43.496

Each content was measured once. N1, N2, and N3 were different concentrations of NH₄Cl (0.1, 0.2, and 0.3 mg/L); P1, P2, and P3 were different concentrations of K₂HPO₄ (0.02, 0.42, and 0.82 mg/L); R_N and R_P were the range of response value caused by different concentrations of N and P.

Table 3

Variance analysis of dry cell weight (DCW), phycocyanin content in liquid (PCL), and phycocyanin content in dried Spirulina (PCD).

	Source	Type III sum of squares	Df	Mean square	F	P
DCW	N	0.128	2	0.064	957.125	0.000
	P	0.004	2	0.002	26.375	0.005
PCL	N	5783.498	2	2891.749	113.351	0.000
	P	299.361	2	149.680	5.867	0.065
PCD	N	14575.954	2	7287.977	63.565	0.001
	P	2908.944	2	1454.472	12.686	0.019

3.2. Research on Rapid Detection of Phycocyanin Based on Raman Spectroscopy

As the phycocyanin market gradually expanded, more convenient and efficient monitoring methods will be required during the production process. This study was the first in situ detection of phycocyanin using a portable Raman spectrometer. The Spirulina was cultivated under the optimal culture conditions obtained in Section 3.1. A portable Raman spectrometer was used to collect information on Spirulina fluid and combined with LSSVM to establish a PCL concentration prediction model. A total of 96 spectral data were obtained. There were fluctuations in 151–935 cm⁻¹ and 1720–3000 cm⁻¹(Figure S1), resulting in a low signal-to-noise ratio. Therefore, we select 935–1720 cm⁻¹ (Figure 5(a)) for subsequent analysis. The Raman spectra show that there are 7 characteristic absorption peaks for these Spirulina fluid samples. Table 4 summarizes the assignment of these characteristic peaks [25–29].

[figures omitted; refer to PDF]

Table 4

The assignment of characteristic Raman peaks of Spirulina.

Raman shift (${\text{cm}}^{-1}$)	Assignment	Analytes
997	δ (ring)	Phenylalanine
1152	δ (CH)	β-Carotene
1272	Amide III, ν (C-N)	Proteins
	δ (CH3), δ (CH)	β-Carotene
1337	Τ (CH3CH2), ω (CH3CH2)	Proteins
1432	δ (CH2)	Proteins, lipids
1518	C = C stretching	β-Carotene
1599	C = C stretching	β-Carotene

In order to explain mechanism behind the formation of these absorption peaks, quantum chemistry calculations of the phycocyanin molecules were carried out with theoretical value calculated by DFT. The molecular vibration modes of each peak were determined by comparing the experimental and theoretical spectra. As shown in Figure 5(b), the characteristic peaks that were measured at 1272, 1337, and 1432 cm⁻¹ and which attributed to protein could be matched with the simulated peaks at 1276, 1340, and 1431 cm⁻¹, respectively. It can be considered that these three peaks represented phycocyanin in the data measured by Raman spectrometer.

To further determine the relationship between the concentration of PCL and the intensity of characteristic peak, five different PCL concentrations were collected, and the corresponding average Raman spectroscopy is shown in Figure 6(a). The intensity decreased gradually with the decrease in PCL concentration at the characteristic peaks that attributed to phycocyanin (1272, 1337, and 1432 cm⁻¹). The linear regression models between these three characteristic peaks and the PCL concentration were established. As shown in Figures 6(c)–6(e), the intensity of 1272, 1337, and 1432 cm⁻¹ had correlation coefficients (R²) of 0.789, 0.811, and 0.800, respectively. We further establish linear regression model between the sum of the three peak intensities and the PCL concentration. As shown in Figure 6(b), the correlation coefficient (R²) was significantly increased to 0.89, eliminating the influence of the error of a single characteristic peak on the results.

[figures omitted; refer to PDF]

It was worth noting in Figure 6(a) that the intensity of 1152 and 1518 cm⁻¹ attributed to β-carotene also decreased gradually with the decrease in PCL concentration. This indicated that the intensity of all the seven characteristic peaks had relationship with the concentration of PCL. Then, we implemented a simple correlation analysis between each peak and their response to PCL concentration (Figure 7(a)). Firstly, we can find that Pearson’s correlation coefficients of the seven characteristic peaks with PCL concentration were higher than 0.8. Then, the peaks that attributed to β-carotene displayed strong correlations with the characteristic peaks of phycocyanin. The 1518 cm⁻¹ displayed the strongest correlation with the PCL, while it had no correspondence with phycocyanin. β-Carotene is part of the pigment-protein complex in microalgae and plays an important role in the energy transfer process of photosynthesis. It can absorb the strongest light in the blue region (400–500 nm) and transfer the excitation energy to chlorophyll, making photosynthesis effective in a wider wavelength range. Therefore, β-carotene can reflect the growth of Spirulina. The healthier the Spirulina is, the more beneficial it is to the synthesis of phycocyanin. Therefore, all seven characteristic peaks are helpful for the study of PCL concentration.

[figures omitted; refer to PDF]

LSSVM model was finally implemented to evaluate the actual roles of these seven Raman characteristic peaks. The optimized γ and ${\delta }^2$ of LSSVM were set to 11.294 and 42.39, respectively. The process and result are shown in Figure 7(b). The overall R² was higher than 0.9. The $R_{\text{pre}}^2$ was 0.908, RMSEP was 11.531, and RPD was higher than 3, which indicated the model was successful [30]. Therefore, the modeling results indicated that the rapid detection of phycocyanin by the 405 nm portable Raman spectrometer was feasible.

4. Conclusions

Overall, we demonstrated that appropriate concentration of N and P is very important for the accumulation of phycocyanin in the cultivation process of Spirulina. When the high concentration of N causes significant damage to Spirulina, the high concentration of P may have a certain protective effect on the growth of Spirulina. When the concentration of N is appropriate, the medium concentration of P has a better effect on the synthesis of phycocyanin. The study obtained the most suitable conditions: NH₄Cl, 0.1 g/L, and K₂HPO₄, 0.42 g/L. We also detected the Spirulina by portable Raman spectrometer and obtained the characteristic peaks of phycocyanin (1272, 1337, and 1432 cm⁻¹). There was a good linear correlation between the sum of the peak intensities and the PCL concentration (y = 18.887x + 833.530, $R^2=0.890$). The LSSVM model based on the characteristic peaks obtained good result, the $R_{\text{pre}}^2$ was 0.907, and the RPD was 3.357. This study provides a method for real-time monitoring of phycocyanin content in Spirulina, which is of great significance for formulating dynamic cultivation strategies.

Acknowledgments

This research was financially supported by the Key Projects of International Scientific and Technological Innovation Cooperation among Governments under National Key R & D Plan (Grant no. 2019YFE0103800) and National Natural Science Foundation of China (Grant no. 31402318).

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