A microalgae concentration and pigment detection method and system based on multispectral attenuation inversion

Abstract

This invention discloses a method and system for detecting microalgae concentration and pigments based on multispectral attenuation inversion. The method includes: establishing a nonlinear light attenuation model, whose inputs are the chlorophyll a content, chlorophyll b content, carotenoid content, and microalgae concentration, and whose output is the corresponding spectral transmittance; presetting bio-realism constraints based on the physiological characteristics of microalgae and physical constraints based on the physical laws of spectral detection; acquiring measured spectral transmittance data of the microalgae sample under different spectra; combining the bio-realism constraints and physical constraints, using a nonlinear optimization algorithm to invert and solve the nonlinear light attenuation model, and calculating the chlorophyll a content, chlorophyll b content, carotenoid content, and microalgae concentration that meet the constraints using the measured spectral transmittance data as the objective. This invention improves the accuracy and bio-reasonableness of microalgae detection through multispectral coupling and constraint optimization.

Description

Technical Field

[0001] This invention belongs to the field of microalgae carbon fixation technology, and particularly relates to a method and system for detecting microalgae concentration and pigments based on multispectral attenuation inversion. Background Technology

[0002] Microalgae photosynthetic growth depends on light energy input. Pigments, as the core light-harvesting molecules in microalgae, directly determine the efficiency of light capture. Microalgae concentration, on the other hand, affects the penetration depth of light in the culture system through light scattering; excessively high concentrations can lead to insufficient light for photosynthesis in deeper layers of the culture. Therefore, precise data on pigment content and concentration are necessary during microalgae cultivation to achieve quantitative supplementation and dynamic control of light.

[0003] However, existing methods for detecting microalgal pigment content and concentration have significant time lags, making it difficult to support real-time light control requirements. In traditional offline detection methods, pigment content needs to be measured by spectrophotometer after extraction, while microalgal concentration relies on the dry weight determination method using filtration and drying. These methods are cumbersome and time-consuming, and cannot obtain core parameters in real time. Mainstream spectral detection technologies also have application limitations: Although ultraviolet-visible spectroscopy can estimate pigment content and concentration based on the absorbance of the 680nm characteristic absorption peak of microalgae, this method only has a clear linear relationship in the low concentration range. At high concentrations, due to intercellular interactions and enhanced light scattering, the detection deviation is easily increased significantly. Infrared spectroscopy calculates concentration by the transmittance of light scattered by microalgae, which is also only applicable to low concentration scenarios. At high concentrations, the multiple scattering effect of microalgae and light will seriously interfere with the detection signal, resulting in a large deviation in the concentration calculation results.

[0004] In summary, existing detection technologies cannot effectively solve the problem of real-time, accurate, and low-cost online monitoring of the two core biological parameters of pigment content and concentration in microalgae cultivation scenarios. There is an urgent need to develop a microalgae concentration and pigment detection technology adapted to cultivation scenarios to overcome existing technical bottlenecks, support real-time and accurate control of light conditions during microalgae cultivation, and ensure the photosynthetic growth efficiency of microalgae. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for detecting microalgae concentration and pigments based on multispectral attenuation inversion, comprising the following steps: A nonlinear light attenuation model integrating microalgal cell scattering effect and pigment absorption characteristics was established. The input of the nonlinear light attenuation model is the chlorophyll a content, chlorophyll b content, carotenoid content and microalgal concentration of the microalgae, and the output is the corresponding spectral transmittance. Preset biorealism constraints based on the physiological characteristics of microalgae and physical constraints based on the physical laws of spectral detection; Obtain the measured spectral transmittance data of the microalgae samples under different spectra; Combining the biological and physical constraints, a nonlinear optimization algorithm is used to invert and solve the nonlinear light attenuation model. Using the measured spectral transmittance data as the target, the microalgal chlorophyll a content, chlorophyll b content, carotenoid content, and microalgal concentration that meet the constraints are calculated.

[0006] Optionally, the process of establishing a nonlinear light attenuation model that integrates the scattering effect of microalgal cells and the absorption characteristics of pigments includes: Based on the Lambert-Beer law, and coupled with the scattering effect of microalgal cells and the sum of the products of pigment mass absorption coefficient and corresponding pigment content, a nonlinear light attenuation model is established.

[0007] Optionally, the biological authenticity constraints include: the ratio range of chlorophyll a content to chlorophyll b content, the non-negativity and upper limit threshold of each pigment content, and the non-negativity of microalgae concentration; the physical constraints include the range of spectral transmittance values.

[0008] Optionally, the process of inverting and solving the nonlinear light attenuation model using a nonlinear optimization algorithm, combining the aforementioned biological realism constraints and physical constraints, includes: Based on the measured spectral transmittance data, a nonlinear optimization algorithm is used to perform iterative calculations under the constraints of biological authenticity and physical constraints to obtain preliminary microalgal pigment content and concentration. The preliminary microalgal pigment content and concentration are verified based on the aforementioned biological authenticity constraints to obtain the final microalgal pigment content and concentration that conforms to biological authenticity.

[0009] Optionally, the nonlinear optimization algorithm is any one of the Levenberg-Marquardt algorithm, Newton's method, or genetic algorithm.

[0010] Optionally, the biological authenticity constraints are embedded in the nonlinear optimization algorithm by setting upper and lower boundaries of parameters or a penalty function.

[0011] This invention also proposes a microalgae concentration and pigment detection system based on multispectral attenuation inversion, used to implement the method, including: The continuous flow measurement unit is used to allow the test liquid containing microalgae to flow through a detection area with a fixed optical path in a stable state; A multispectral light-emitting unit, connected to the optical path of the continuous flow measurement unit, is used to emit red light, blue light, green light, and broadband white light with specific center wavelengths and bandwidths into the detection area; An irradiation detection unit is located at the output of the transmission light path of the continuous flow measurement unit. It is used to simultaneously receive and measure the intensity of the transmitted light of four spectra after attenuation by the liquid under test, and output the measured irradiation intensity data and blank reference irradiation intensity data. The data processing unit is connected to the irradiation detection unit by signal. It is used to receive irradiation intensity data, calculate spectral transmittance, call the nonlinear light attenuation model, and perform inversion calculation through nonlinear optimization algorithm in combination with constraints, and finally output microalgal pigment content and concentration data.

[0012] Optionally, the continuous flow measurement unit includes a measurement cell with a fixed thickness, and an inlet pipe, an outlet pipe, and a flow control unit connected to the measurement cell. The flow control unit is used to adjust the flow rate of the liquid flowing through the measurement cell.

[0013] Optionally, the multispectral light-emitting unit emits red light with a center wavelength of 620 nm and a bandwidth of ±20 nm; blue light with a center wavelength of 460 nm and a bandwidth of ±20 nm; green light with a center wavelength of 530 nm and a bandwidth of ±20 nm; and white light with a wavelength range of 400 nm to 760 nm.

[0014] Optionally, the data processing unit is configured to perform the following operations: calculate the transmittance of four spectra based on the measured irradiance data and blank reference irradiance data; use the transmittance as input, and under the constraints of biological realism and physical constraints, use a nonlinear optimization algorithm to solve the nonlinear light attenuation model; and verify the solution results, and if they do not meet the constraints, re-optimize.

[0015] Compared with the prior art, the present invention has the following advantages and technical effects: This invention establishes a nonlinear light attenuation model that integrates the scattering effect of microalgal cells with the absorption characteristics of pigments. This model not only considers the absorption of light by pigments but also incorporates the scattering effect of light by the microalgal cells themselves, and models four specific spectra: red, blue, green, and white light. This approach enables the model to more realistically simulate the light transmission process in microalgal suspensions, especially in high-concentration samples, thus effectively overcoming the problem of significantly increased detection bias caused by enhanced light scattering and intercellular interactions at high concentrations in traditional single-spectrum or linear models, and significantly improving detection accuracy over a wide concentration range.

[0016] This invention establishes biorealism constraints based on the physiological characteristics of microalgae and physical constraints based on physical laws, and strictly applies these constraints during the inversion solution process. This approach ensures that the pigment content and concentration parameters obtained from the inversion solution not only mathematically fit the spectral data but also conform to known microalgal biological laws and basic physical principles. This greatly enhances the biological rationality and reliability of the detection results and avoids meaningless or non-common sense numerical solutions.

[0017] This invention employs a nonlinear optimization algorithm to directly solve the model using measured multispectral transmittance data of the sample. This approach combines complex model calculations with optimization algorithms, enabling rapid and automated calculation of target biological parameters from spectral data. This process eliminates the cumbersome steps of sample extraction, separation, and drying required in traditional offline detection, making real-time, online monitoring of microalgae concentration and core pigment content possible, and providing timely support for light regulation decisions during the cultivation process. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the system structure according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the prediction results in an embodiment of the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0021] Example 1 This embodiment provides a method for detecting microalgae concentration and pigments based on multispectral attenuation inversion, including the following steps: A nonlinear light attenuation model integrating microalgal cell scattering effect and pigment absorption characteristics was established. The input of the nonlinear light attenuation model is the chlorophyll a content, chlorophyll b content, carotenoid content and microalgal concentration of the microalgae, and the output is the corresponding spectral transmittance. Preset biorealism constraints based on the physiological characteristics of microalgae and physical constraints based on the physical laws of spectral detection; Obtain the measured spectral transmittance data of the microalgae samples under different spectra; Combining the biological and physical constraints, a nonlinear optimization algorithm is used to invert and solve the nonlinear light attenuation model. Using the measured spectral transmittance data as the target, the microalgal chlorophyll a content, chlorophyll b content, carotenoid content, and microalgal concentration that meet the constraints are calculated.

[0022] It is feasible that the microalgae include one or more of Chlorella, Chlamydomonas, and marine Chlorella.

[0023] The feasible process of establishing a nonlinear light attenuation model that integrates the scattering effect of microalgal cells and the absorption characteristics of pigments includes: based on the Lambert-Beer law, and coupled with the scattering effect of microalgal cells and the sum of the products of the pigment mass absorption coefficient and the corresponding pigment content, a nonlinear light attenuation model is established to accurately characterize the attenuation effect of different concentrations of microalgae on the multispectral spectrum.

[0024] The feasible biological authenticity constraints include: the ratio range of chlorophyll a content to chlorophyll b content, the non-negativity and upper limit threshold of each pigment content, and the non-negativity of microalgae concentration; the physical constraints include the range of spectral transmittance values.

[0025] The feasible process of inverting and solving the nonlinear light attenuation model using a nonlinear optimization algorithm, combining the aforementioned biological and physical constraints, includes: Based on the measured spectral transmittance data, a nonlinear optimization algorithm is used to iteratively calculate under the constraints of biofidelity and physical constraints to obtain preliminary microalgal pigment content and concentration. Based on the constraints of biofidelity, the preliminary microalgal pigment content and concentration are verified to obtain the final microalgal pigment content and concentration that conforms to biofidelity.

[0026] Furthermore, the biological authenticity constraints are embedded in the nonlinear optimization algorithm by setting upper and lower boundaries of parameters or penalty functions.

[0027] Furthermore, the nonlinear optimization algorithm is any one of the Levenberg-Marquardt algorithm, Newton's method, or genetic algorithm.

[0028] Furthermore, the solution process also includes verifying the biological authenticity of the inverse solution results; if the results exceed the constraints, the optimization is iterated again.

[0029] like Figure 2As shown, this embodiment also proposes a microalgae concentration and pigment detection system based on multispectral attenuation inversion, used to implement the method, including: The continuous flow measurement unit is used to allow the test liquid containing microalgae to flow through a detection area with a fixed optical path in a stable state; A multispectral light-emitting unit, connected to the optical path of the continuous flow measurement unit, is used to emit red light, blue light, green light, and broadband white light with specific center wavelengths and bandwidths into the detection area; The irradiation detection unit, located at the transmission light path outlet of the continuous flow measurement unit, includes a radiometer for synchronously receiving and measuring the intensity of the transmitted light of four spectra after attenuation by the liquid to be tested, and outputting measured irradiation intensity data of the liquid to be tested containing microalgae and reference irradiation intensity data of the blank liquid without algae, providing raw signals for subsequent transmittance calculation. The data processing unit is connected to the irradiation detection unit by signal. It is used to receive irradiation intensity data, calculate spectral transmittance, call the nonlinear light attenuation model, and perform inversion calculation through nonlinear optimization algorithm in combination with constraints, and finally output microalgal pigment content and concentration data.

[0030] The continuous flow measurement unit includes a measurement cell with a fixed thickness, an inlet pipe, an outlet pipe, and a flow control unit connected to the measurement cell. The flow control unit is used to adjust the flow rate of the liquid flowing through the measurement cell.

[0031] Furthermore, the optical path thickness of the measuring cell is 1 cm, allowing the test liquid containing microalgae to flow continuously through it, ensuring a stable liquid state and a fixed optical path length during the measurement process. The light emission direction of the multispectral luminescent unit is coaxially arranged with the optical path of the measuring cell, ensuring that the detection light penetrates the test liquid perpendicularly and reducing light propagation deviation.

[0032] In practice, the multispectral light-emitting unit emits red light with a center wavelength of 620 nm and a bandwidth of ±20 nm; blue light with a center wavelength of 460 nm and a bandwidth of ±20 nm; green light with a center wavelength of 530 nm and a bandwidth of ±20 nm; and white light with a wavelength range of 400 nm to 760 nm.

[0033] Implementable, the data processing unit has a built-in calculation program configured to perform the following operations: calculate the transmittance of four spectra based on the measured irradiance data and blank reference irradiance data; using the transmittance as input, solve the nonlinear light attenuation model by using a nonlinear optimization algorithm under the constraints of biological authenticity and physical constraints; verify the solution results, and if they do not meet the constraints, re-optimize, and finally output valid detection results.

[0034] Compared with the prior art, this embodiment has the following beneficial effects: This embodiment constructs an extended nonlinear light attenuation model based on the Lambert-Beer law, while integrating microalgal cell scattering terms and pigment absorption-concentration coupling terms. Combined with multispectral detection of red, blue, green, and white, it effectively overcomes the bias problems caused by light scattering and cell interactions in high-concentration scenarios caused by traditional single-spectrum detection. At the same time, by setting physical-biological dual constraints and embedding optimization algorithms, along with biological authenticity verification of the inversion results, the authenticity and accuracy of the detection results are further guaranteed, making it suitable for accurate detection of low and high concentration microalgal samples.

[0035] The detection system designed in this embodiment adopts a continuous flow measurement mode, combined with real-time signal acquisition from a multispectral luminescence unit and an irradiation detection unit, and rapid inversion solution from a data processing unit. It eliminates the need for offline processing steps such as extraction and drying in traditional detection methods, enabling online real-time monitoring of microalgae concentration and pigment content. It has low detection latency and can accurately match the real-time data requirements for light regulation during microalgae cultivation.

[0036] Example 2 Unless otherwise specified, all raw materials in this embodiment were purchased through commercial channels; among them, the microalgae was Chlorella vulgaris (C. vulgaris) FACHB-31.

[0037] In studies using spectroscopic techniques to detect microalgal pigment content and cell concentration, under specific light field conditions, when visible light penetrates the microalgal culture medium, the ratio of transmitted light intensity to incident light intensity exhibits a regular correlation with changes in incident light intensity. Based on this key observation, this embodiment proposes a method for detecting microalgal concentration and pigments based on multispectral inversion, which can achieve accurate prediction of the content of various microalgal pigments and cell concentrations.

[0038] Specifically, the multispectral inversion-based method for detecting microalgae concentration and pigments proposed in this embodiment, such as... Figure 1 As shown, the specific steps include: Step S1 involves sample preparation, measurement of light attenuation data, and model parameter fitting analysis for the construction of the microalgae light attenuation model. First, microalgae with different pigment contents are cultured and prepared into microalgae suspensions of different concentrations. Each group of algal suspension samples is placed directly above a surface light source. Using a radiometer, the transmitted light intensity values ​​of four spectral lights at 13 light depth gradients (0.002, 0.004, 0.006, 0.008, 0.01, 0.014, 0.018, 0.024, 0.03, 0.04, 0.05, 0.07, and 0.09 m) are measured, and the light transmittance data is calculated. Subsequently, based on the inherent light absorption characteristics of pigments, combined with the absorption spectra of different pigments and the emission spectra of the incident light, the relative absorption weights of each pigment to a specific light source and the average mass absorption coefficient of the three pigments are determined. Simultaneously, k is calculated. a The relative values ​​of kᵦ and kc were used to clarify the relative contributions of different pigments to light absorption; then, using the actual light transmittance data of microalgae suspension as a benchmark, E was determined by fitting the model. a 'and E s The optimal value is obtained by using the least squares method in MATLAB to complete the nonlinear optical attenuation model, as shown in the following formula: ; ; ; Where, α a α b α c (%) represents the content of the three pigments in the microalgae suspension, and Ea' is a dimensionless correction factor related to light absorption. i (m 2 / g) is the average mass absorption coefficient of pigment i to a specific light source, Es(m 2 C(g / m) represents the mass scattering coefficient of light by the microalgae suspension. 3 () represents the biomass concentration of the microalgae suspension, and L (m) represents the distance light travels in the microalgae suspension. The relative mass absorption coefficients of the three pigments under white, green, red, and blue incident light conditions are shown in Table 1 below: Table 1 The fitting parameters and goodness of the nonlinear light attenuation model under white, green, red, and blue incident light conditions are shown in Table 2 below: Table 2 Step S2, based on the physiological characteristics of Chlorella, sets the following constraints: pigment ratio: chlorophyll a / chlorophyll b between 1.5 and 3.5; microalgae concentration greater than or equal to zero; pigment content thresholds: chlorophyll a less than or equal to 60 mg / g, chlorophyll b less than or equal to 30 mg / g, and carotenoids less than or equal to 20 mg / g. Physical constraints, based on the physical laws of spectral detection, are set: transmittance range between 0.001 and 1. These constraints are embedded into the subsequent nonlinear optimization algorithm through upper and lower bounds of the parameters.

[0039] Step S3: Collect the transmittance data to be reversed, start the peristaltic pump to make the Chlorella suspension flow continuously through the measurement cell, and collect the measured irradiance intensity of the algae-containing sample.

[0040] Step S4 employs the LM algorithm as a nonlinear optimization algorithm, inputting transmittance data and constraints to solve the model and obtain pigment content and concentration. The solution process is as follows: initial values ​​are set to [0.001, 0.001, 0.001, 0.1], corresponding to chlorophyll a, chlorophyll b, carotenoids, and microalgae concentrations, respectively; biophysical constraints are set as the upper and lower boundaries of the parameters; iterative optimization: the residual between the model-predicted transmittance and the measured transmittance is minimized using the LM algorithm, iterating until convergence (in this embodiment, the convergence accuracy is set to 10). -6 Biological authenticity verification: Check whether the inversion results meet the constraints. If not, iterate again. The prediction results are as follows: Figure 3 As shown, the predicted results deviate by no more than 10% compared to the actual values.

[0041] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting microalgal concentration and pigments based on multispectral attenuation inversion, characterized in that, Includes the following steps: A nonlinear light attenuation model integrating microalgal cell scattering effect and pigment absorption characteristics was established. The input of the nonlinear light attenuation model is the chlorophyll a content, chlorophyll b content, carotenoid content and microalgal concentration of the microalgae, and the output is the corresponding spectral transmittance. Preset biorealism constraints based on the physiological characteristics of microalgae and physical constraints based on the physical laws of spectral detection; Obtain the measured spectral transmittance data of the microalgae samples under different spectra; Combining the biological and physical constraints, a nonlinear optimization algorithm is used to invert and solve the nonlinear light attenuation model. Using the measured spectral transmittance data as the target, the microalgal chlorophyll a content, chlorophyll b content, carotenoid content, and microalgal concentration that meet the constraints are calculated.

2. The method according to claim 1, characterized in that, The process of establishing a nonlinear light attenuation model that integrates the scattering effect of microalgal cells and the absorption characteristics of pigments includes: Based on the Lambert-Beer law, and coupled with the scattering effect of microalgal cells and the sum of the products of pigment mass absorption coefficient and corresponding pigment content, a nonlinear light attenuation model is established.

3. The method according to claim 1, characterized in that, The biological authenticity constraints include: the ratio range of chlorophyll a content to chlorophyll b content, the non-negativity and upper limit threshold of each pigment content, and the non-negativity of microalgae concentration; the physical constraints include the range of spectral transmittance values.

4. The method according to claim 1, characterized in that, Combining the aforementioned biological and physical constraints, the process of inverting and solving the nonlinear light attenuation model using a nonlinear optimization algorithm includes: Based on the measured spectral transmittance data, a nonlinear optimization algorithm is used to perform iterative calculations under the constraints of biological authenticity and physical constraints to obtain preliminary microalgal pigment content and concentration. The preliminary microalgal pigment content and concentration are verified based on the aforementioned biological authenticity constraints to obtain the final microalgal pigment content and concentration that conforms to biological authenticity.

5. The method according to claim 4, characterized in that, The nonlinear optimization algorithm is any one of the following: the Levenberg-Marquardt algorithm, Newton's method, or a genetic algorithm.

6. The method according to claim 4, characterized in that, The biological authenticity constraints are embedded in the nonlinear optimization algorithm by setting upper and lower boundaries of parameters or penalty functions.

7. A microalgae concentration and pigment detection system based on multispectral attenuation inversion, used to implement the method according to any one of claims 1-6, characterized in that, include: The continuous flow measurement unit is used to allow the test liquid containing microalgae to flow through a detection area with a fixed optical path in a stable state; A multispectral light-emitting unit, connected to the optical path of the continuous flow measurement unit, is used to emit red light, blue light, green light, and broadband white light with specific center wavelengths and bandwidths into the detection area; An irradiation detection unit is located at the output of the transmission light path of the continuous flow measurement unit. It is used to simultaneously receive and measure the intensity of the transmitted light of four spectra after attenuation by the liquid under test, and output the measured irradiation intensity data and blank reference irradiation intensity data. The data processing unit is connected to the irradiation detection unit by signal. It is used to receive irradiation intensity data, calculate spectral transmittance, call the nonlinear light attenuation model, and perform inversion calculation through nonlinear optimization algorithm in combination with constraints, and finally output microalgal pigment content and concentration data.

8. The system according to claim 7, characterized in that, The continuous flow measurement unit includes a measurement cell with a fixed thickness, and an inlet pipe, an outlet pipe, and a flow control unit connected to the measurement cell. The flow control unit is used to adjust the flow rate of the liquid flowing through the measurement cell.

9. The system according to claim 7, characterized in that, The multispectral light-emitting unit emits red light with a center wavelength of 620 nm and a bandwidth of ±20 nm; blue light with a center wavelength of 460 nm and a bandwidth of ±20 nm; green light with a center wavelength of 530 nm and a bandwidth of ±20 nm; and white light with a wavelength range of 400 nm to 760 nm.

10. The system according to claim 7, characterized in that, The data processing unit is configured to perform the following operations: calculate the transmittance of four spectra based on the measured irradiance data and blank reference irradiance data; use the transmittance as input, and under the constraints of biological realism and physical constraints, use a nonlinear optimization algorithm to solve the nonlinear light attenuation model; and verify the solution results, and if they do not meet the constraints, re-optimize.

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