Raman spectrum detection method, Raman spectrum detection equipment and dynamic monitoring method for microalgae oil

Abstract

The invention discloses a Raman spectrum detection method and equipment for microalgae oil and a dynamic monitoring method, and belongs to the technical field of spectrum detection. The method comprises the following steps: firstly, collecting time domain signals on a first microalgae sample with different laser powers, determining a damage threshold according to characteristic peak intensity, constructing a power gradient sequence, and further extracting gating delay and gating width corresponding to each laser power from the time domain signals; and calculating the current gating delay and fitting to obtain a gating gradient function between the laser power and the gating width. And performing fluorescence quenching treatment on a second microalgae sample by adopting laser power in the power gradient sequence in sequence, returning to adjust the laser power if the background intensity of residual fluorescence is higher than a preset threshold value, otherwise, starting a gating switch under the corresponding gating delay, calling a gating gradient function to determine the gating width and collecting a second Raman spectrum. And finally, acquiring lipid distribution data of the second microalgae sample based on the second Raman spectrum.

Description

Technical Field

[0001] This invention relates to the field of spectroscopic detection technology, and in particular to a Raman spectroscopy detection method, equipment, and dynamic monitoring method for microalgal lipids. Background Technology

[0002] Microalgae are an important potential source for the production of biolipids and biodiesel. Their lipid synthesis is a dynamic biological process. While traditional chemical extraction methods yield accurate results, they are extremely time-consuming and completely destroy cell structure, leading to the loss of crucial spatial distribution information regarding lipids within cell populations (heterogeneity) and individual cells (such as lipid droplet distribution). Raman spectroscopy, sensitive to lipid chemical bonds and requiring no complex staining, is theoretically suitable for lipid analysis. Chinese patent application CN118688179A discloses a method for real-time in-situ analysis of microalgal biochemical components combining Raman spectroscopy detection and a neural network model. This method collects the raw Raman spectra of microalgal samples, combines microalgal cell dry weight and fucoxanthin production to construct and train a neural network model, ultimately achieving quantitative prediction of the target variable. However, microalgal cells are rich in chlorophyll, carotenoids, and other fluorescent substances, which, under laser excitation, produce a fluorescence background much stronger than the Raman signal, severely obscuring weak characteristic Raman peaks. This results in a sharp decrease in the signal-to-noise ratio, making feature information extraction difficult and limiting the accuracy of the quantitative model. Chinese patent application CN113252637A discloses a fluorescence background suppression system and method for Raman spectroscopy detection. This method enhances signal collection efficiency and suppresses fluorescence background by using a specially designed fiber optic probe (central excitation, surrounding collection) combined with coded aperture technology. However, this method primarily focuses on static optimization at the hardware level, and its suppression effect largely depends on a fixed optical configuration. The fluorescence suppression lacks a priori algorithms, and the optical configuration cannot adaptively and actively control the dynamic changes in the fluorescence characteristics of the sample itself. Therefore, this method requires further improvement. Summary of the Invention

[0003] To address the aforementioned issues, this invention provides a Raman spectroscopy detection method and device for microalgal lipids. This invention selects optimized laser power and gate width through power gradient and gate gradient, and then performs laser surface scanning and Raman spectroscopy detection. This method effectively reduces fluorescence interference and improves the signal-to-noise ratio and detection accuracy of Raman imaging while avoiding damage to the target monitored components.

[0004] Furthermore, this invention also provides a dynamic monitoring method for microalgae stress treatment, which involves Raman spectroscopy detection of microalgae samples subjected to ultrasonic stress treatment to generate total lipid levels at different stress durations. This invention uses a single set of first microalgae samples to avoid repeated adjustments to power and gating parameters.

[0005] The objective of this invention can be achieved through the following technical means: A Raman spectroscopy method for detecting microalgal lipids, based on the following steps: Step 1: Select multiple test areas in the first microalgae sample, irradiate the test areas with first lasers of different powers, disable the gating switch, and collect the corresponding multiple time-domain signals; Step 2: Generate the first Raman spectrum of the time-domain signal and calculate the intensity of the characteristic peak. Determine the damage threshold of the laser power based on the intensity of the characteristic peak, and determine the power gradient sequence based on the damage threshold. Step 3: Find the gate delay and gate width for each laser power based on the time domain signal, fit the gate gradient function with different laser powers and corresponding gate widths, and calculate the current gate delay based on different gate delays; Step 4: Select laser power sequentially from the power gradient sequence, and use the second laser of that power to perform fluorescence quenching treatment on the observation area of ​​the second microalgae sample; Step 5: After quenching, the second laser surface is used to scan the observation area of ​​the second microalgae sample. Under the current gate delay, the gate switch is activated, the gate width of the second laser is selected from the gate gradient function, and the second Raman spectrum is acquired. Step 6: Calculate the fluorescence background intensity in the second Raman spectrum. If the fluorescence background intensity is greater than the quenching threshold, return to step 4; otherwise, proceed to step 7. Step 7: Extract multiple lipid characteristic peaks based on the second Raman spectrum, and generate lipid distribution data of the second microalgae sample based on the signal intensity of the lipid characteristic peaks.

[0006] In this invention, in step 2, a range of ±15cm is selected, centered on the Raman shift where any Raman characteristic peak is located. -1 The Raman shift interval is taken as the characteristic peak window of the Raman characteristic peak. Multiple baseline points are selected on both sides of the characteristic peak window. The baseline points are fitted by a polynomial to obtain the baseline spectrum. The signal intensity of the first Raman spectrum in the characteristic peak window is subtracted from the signal intensity of the corresponding baseline spectrum to obtain the net signal intensity of the characteristic peak window. The integral area of ​​the net signal intensity corresponding to each Raman characteristic peak is the characteristic peak intensity.

[0007] In this invention, in step 2, a power range is determined based on the sampling sensitivity and damage threshold of the device, a power center is determined according to a preset ratio of the damage threshold, and laser power is extracted sequentially from the power center in the direction of sampling sensitivity and damage threshold at equal intervals to form a power gradient sequence.

[0008] In this invention, in step 3, the peak time t1 when the time domain signal first reaches a maximum value and the valley time t2 when the time domain signal first reaches a minimum value are found. The gating delay T1 = t1 - t0, where t0 is the emission time of the first laser, and the gating width T2 = 2(t2 - t1).

[0009] In this invention, in step 3, the mean of the gating delay for different laser powers is the current gating delay. Different laser powers and their corresponding gating widths are extracted. Based on the nonlinear relationship between the laser power and the corresponding gating width, a function whose gating width monotonically decreases with increasing laser power and asymptotically approaches the laser pulse width is fitted as the gating gradient function.

[0010] In this invention, in step 5, the observation area of ​​the second microalgae sample is scanned, with a scanning step size of Δx in the horizontal direction and Δy in the vertical direction. Δx and Δy are determined according to the spot diameter d of the second laser, and a time-domain signal containing scanning position information is obtained. The second Raman spectrum of the time-domain signal is extracted based on the corresponding gate width.

[0011] In this invention, in step 6, a background band of the second Raman spectrum is selected, and the average signal intensity of all spectral data points in the background band is calculated. This average value is used as the fluorescence background intensity under the corresponding laser power. The background band is a continuous interval that does not contain a characteristic peak window.

[0012] In this invention, in step 7, the total lipid amount at each scanning position is calculated based on the signal intensity of one or more lipid characteristic peaks in the second Raman spectrum corresponding to each scanning position. A first content matrix is ​​constructed based on the total lipid amount at all scanning positions and the scanning positions. The spatial difference method is performed on the first content matrix to generate a second content matrix. The total lipid amount in the second content matrix is ​​converted into pixel values ​​to generate lipid distribution data.

[0013] A Raman spectroscopy detection device for realizing the Raman spectroscopy detection method of the microalgal lipids, comprising: A tunable laser, configured to emit a first laser or a second laser; A glass slide is configured to hold either the first microalgae sample or the second microalgae sample; The confocal optical path assembly is configured to focus either a first laser or a second laser onto a glass slide; The gated detection module is configured to collect Raman scattering signals and generate time-domain signals. The gated detection module has a gated switch. The signal analysis module is configured to reconstruct the first and second Raman spectra in the frequency domain based on the time-domain signal; The data analysis module is configured to determine the power gradient sequence based on the first Raman spectrum and fit a gated gradient function; The image generation module is configured to generate lipid distribution data based on a second Raman spectrum. The system control module is configured to control the tunable laser, the glass slide, and the gated detection module. The system control module controls the laser power of the tunable laser according to the power gradient sequence; the system control module controls the gate width of the gated detection module according to the gated gradient function; and the system control module moves the glass slide according to the focusing position of the first laser or the second laser.

[0014] A method for dynamic monitoring of microalgae stress treatment includes the following steps: Step 100: Divide the microalgae samples to be tested into a first group of microalgae samples and multiple groups of second group of microalgae samples. Use the ultrasonic stress method to treat the second group of microalgae samples at different time periods. The parameters of the ultrasonic stress method are: ultrasonic frequency range of 20kHz-80kHz, ultrasonic power density range of 5W / L-275W / L, and the stress treatment method is pulse mode, with a single pulse duration of 1 to 10 minutes and a pulse interval of 0.5 to 2 hours. Step 200: Detect the first and second microalgae samples according to the Raman spectroscopy detection method for microalgae lipids to generate multiple sets of lipid distribution data; Step 300: Combine multiple sets of lipid distribution data to construct a lipid distribution data spectrum sequence and generate the total lipid amount for different time periods.

[0015] The Raman spectroscopy detection method, equipment, and dynamic monitoring method for microalgal lipids of this invention have the following advantages: Firstly, the Raman spectroscopy detection method adaptively adjusts the laser power within a safe range. The damage threshold is determined by testing the characteristic peak intensities at different laser powers, and a safe excitation power gradient sequence is constructed based on this, effectively avoiding cell damage or lipid oxidation caused by high-power lasers. Secondly, the optimal gating width is automatically matched according to the fitted gating gradient function, eliminating the need for manual experience in setting the time gating parameters. Thirdly, the optimal quenching effect is obtained by real-time evaluation of the fluorescence background intensity. After quenching, a second Raman spectrum based on a gating switch is acquired, achieving effective control of the fluorescence background. Finally, by precisely adjusting the current gating delay and gating width, the second Raman spectrum of the second microalgal sample is acquired, minimizing delayed fluorescence interference, improving the signal-to-noise ratio of lipid characteristic peaks, and achieving high-resolution imaging of the lipid distribution data of the second microalgal sample. Attached Figure Description

[0016] Figure 1 This is a flowchart of a Raman spectroscopy method for detecting microalgal lipids according to the present invention; Figure 2This is a comparison diagram of the main detection substances at different Raman shifts in this invention; Figure 3 This is a schematic diagram of the first laser and the time-domain signal of the present invention; Figure 4 This is a schematic diagram of the background band of the present invention; Figure 5 This is a schematic diagram of the gated gradient function of the present invention; Figure 6 This is a schematic diagram of the original second Raman spectra in different observation regions of this invention; Figure 7 This is a schematic diagram of the corrected second Raman spectrum according to the present invention; Figure 8 This is a block diagram of the Raman spectroscopy detection device for implementing the Raman spectroscopy detection method for microalgal lipids according to the present invention; Figure 9 This is an optical path diagram of the Raman spectroscopy detection method of the present invention. The solid arrows represent optical signals, and the dashed arrows represent electrical signals. Figure 10 This is a flowchart of a dynamic monitoring method for microalgae stress treatment according to the present invention; Figure 11 This is a schematic diagram of lipid distribution data for different stress periods according to the present invention; Figure 12 This is a schematic diagram of the total lipid content during different stress periods according to the present invention.

[0017] The attached figures are labeled as follows: 1. Tunable laser; 2. Bandpass filter; 3. Mirror; 4. First lens; 5. First beam splitter; 6. Broadband light source; 7. Filter; 8. Second beam splitter; 9. Second lens; 10. Glass slide; 11. Optical path adjustment module; 12. Gate switch; 13. Photodetector. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0019] Compared to traditional methods, Raman spectroscopy allows for direct spectral acquisition of intact cells. By analyzing the signal intensity of characteristic peaks within specific Raman shift ranges, it can qualitatively identify lipid compound categories and relatively quantitatively assess their content changes. However, the chlorophyll and pigment proteins abundant in microalgal cells generate a strong autofluorescence background. When excited by laser, the signal intensity of this fluorescence background is often several orders of magnitude higher than the Raman signal, causing the weak lipid characteristic peaks to be completely obscured.

[0020] Furthermore, when laser photons are absorbed by fluorescent molecules, they transition to a high-energy state. Under high-intensity irradiation, these fluorescent molecules cannot release energy back to their ground state through normal pathways. Irreversible breakage of chemical bonds or structural changes occur, resulting in a permanent loss of luminescence. This process requires a relatively low energy threshold. When laser energy is absorbed, it is converted into localized heat, causing increased vibration of lipid molecules. When the temperature rises to a certain level, it may lead to the breaking of chemical bonds or oxidation and isomerization of lipid molecules. However, this process requires a relatively high energy threshold. Therefore, this invention uses preliminary experiments to accurately determine a safe power gradient sequence that will not significantly damage lipid molecules, thus selecting a second laser. The second laser is then used to pre-treat the second microalgae sample with fluorescence quenching. A gated switch is then used to acquire the second Raman spectrum of the second microalgae sample, obtaining lipid distribution data. This method effectively eliminates fluorescence interference, significantly improving Raman signal quality while ensuring the structural integrity of the microalgae sample.

[0021] A preferred embodiment of the present invention is as follows. Example 1

[0022] Reference Figure 1 The Raman spectroscopy detection method for microalgal lipids described in this embodiment includes the following steps: Step 1: Select multiple test areas in the first microalgae sample, and irradiate each test area with a first laser of different powers. Disable the gating switch and collect multiple corresponding time-domain signals. Divide the first microalgae sample into multiple test areas of equal area. In this embodiment, the linear size of each test area is not less than 10 times the diameter of the first laser spot. In another embodiment, a single microalgae cell in the first microalgae sample can also be set as a test area. In this case, the size of the first laser spot is configured to be no larger than the size of a single cell to achieve single-cell resolution irradiation and detection.

[0023] Significant subcellular structural heterogeneity exists within microalgal cells—for example, chloroplasts are rich in chlorophyll, lipid droplets are rich in concentrated lipids, the nucleus contains nucleic acids, and the cytoplasmic matrix composition is also uneven. If laser irradiation is performed only at a single site, the acquired Raman signal may only reflect a certain local component (such as pure chlorophyll or pure lipid droplets). Therefore, in this embodiment, the first laser is used to irradiate multiple spatial sites in the corresponding test area and acquire time-domain signals. The time-domain signal of the test area is generated based on the mean of the time-domain signals of all spatial sites in the test area.

[0024] Step 2: Generate the first Raman spectrum of the time-domain signal and calculate the intensity of the characteristic peaks. Determine the damage threshold of the laser power based on the intensity of the characteristic peaks, and determine the power gradient sequence based on the damage threshold. Select ±15cm intervals centered on the Raman shift where any Raman characteristic peak is located. -1 The Raman shift range is used as the characteristic peak window of the Raman characteristic peak. Multiple baseline points are selected on both sides of the characteristic peak window, and a polynomial is used to fit the baseline points to obtain the baseline spectrum. The signal intensity of the first Raman spectrum within the characteristic peak window is subtracted from the signal intensity of the corresponding baseline spectrum to obtain the net signal intensity of the characteristic peak window. The integral area of ​​the net signal intensity corresponding to each Raman characteristic peak is the characteristic peak intensity. In this embodiment, the selected Raman characteristic peaks are selected from peaks related to lipids in microalgae. Specifically, referring to... Figure 2 The following peak position was selected as the Raman characteristic peak: 1007 cm⁻¹ -1 -1009cm -1 1066cm -1 1266cm -1 1445cm -1 and 1660cm -1 .

[0025] Within the range where no optical damage occurs, the intensity of Raman characteristic peaks typically increases with increasing laser power (approximately linearly). However, once the damage threshold is exceeded, the characteristic peak intensity suddenly decreases, becomes distorted, or disappears. When the characteristic peak intensity first decreases, it indicates that the photothermal or photochemical effect of the laser has begun to have a detectable and irreversible impact on the chemical structure or spatial conformation of the target lipid molecules. The laser power at this point is taken as the damage threshold. A power range is determined based on the device's sampling sensitivity and the damage threshold. A power center is determined according to a preset ratio of the damage threshold. Laser power is extracted sequentially at equal intervals from this power center towards the sampling sensitivity and the damage threshold, forming a power gradient sequence.

[0026] Step 3: Find the gating delay and gating width for each laser power based on the time-domain signal. Fit the gating gradient function to different laser powers and corresponding gating widths, and calculate the current gating delay based on different gating delays. For example... Figure 3The peak time t1 and the valley time t2 of the first occurrence of a maximum and minimum value of the time-domain signal are found. The gating delay T1 = t1 - t0, where t0 is the emission time of the first laser, and the gating width T2 = 2(t2 - t1). Raman scattering is an instantaneous process that occurs immediately after the laser pulse. Fluorescence emission involves the lifetime of the electronically excited state and typically has a nanosecond-level delay. Therefore, in the time-domain signal, the early stage is Raman signal + elastic scattering, followed by a gradual increase in the fluorescence signal. Before the minimum value, it is mainly Raman signal, and after the minimum value, it is mainly fluorescence signal. The mean of the gating delay for different laser powers is the current gating delay. Different laser powers and their corresponding gating widths are extracted. Based on the nonlinear relationship between the laser power and the corresponding gating width, a function whose gating width monotonically decreases with increasing laser power and asymptotically approaches the laser pulse width is fitted as the gating gradient function, as detailed in Example 3.

[0027] Step 4: Select laser power sequentially from the power gradient sequence, and use the second laser of that power to quench the fluorescence in the observation area of ​​the second microalgae sample. Select laser power sequentially from the power gradient sequence, and use the second laser corresponding to that power to irradiate the observation area for a fixed irradiation time of 1–5 seconds. In this embodiment, a fixed irradiation time of 3 seconds is preferred. At this time, only the fluorescence background preprocessing step is performed; no time-domain signal acquisition is performed, therefore, there is no need to activate the gating switch. The second laser has the same wavelength as the first laser.

[0028] Step 5: After quenching, the observation area of ​​the second microalgae sample is scanned using the second laser surface. Under the current gating delay, the gating switch is activated, and the gating width of the second laser is selected from the gating gradient function. The second Raman spectrum is then acquired. The scanning step size in the horizontal axis direction is Δx, and the scanning step size in the vertical axis direction is Δy. Δx and Δy are determined based on the spot diameter d of the second laser, and both Δx and Δy range from [2d, 3d]. The time-domain signal containing scanning position information is acquired, and the second Raman spectrum of this time-domain signal is extracted based on the corresponding gating width.

[0029] Step 6: Calculate the fluorescence background intensity in the second Raman spectrum. If the fluorescence background intensity is greater than the quenching threshold, return to step 4; otherwise, proceed to step 7. Referring to the method for obtaining the characteristic peak window of the first Raman spectrum in step 2, obtain the characteristic peak window of the second Raman spectrum. The continuous interval not containing the characteristic peak window is taken as the background band. Figure 4The background band of the second Raman spectrum is selected, and the average signal intensity of all spectral data points within this background band is calculated. This average value is used as the fluorescence background intensity at the corresponding laser power. The quenching threshold is set based on the analytical requirements of lipid characteristic peaks. In this embodiment, the average net signal intensity of lipid characteristic peaks is measured through preliminary experiments, and the maximum permissible fluorescence background intensity is calculated by inversion to ensure that the signal-to-noise ratio of lipid characteristic peaks is not less than 10. This maximum permissible fluorescence background intensity is set as the quenching threshold. It has been verified that the reasonable range of the quenching threshold is 100 to 500 a.u.

[0030] Step 7: Extract multiple lipid characteristic peaks based on the second Raman spectrum, and generate lipid distribution data for the second microalgae sample based on the signal intensity of the lipid characteristic peaks. Calculate the total lipid amount at each scan position based on the signal intensity of one or more lipid characteristic peaks in the second Raman spectrum corresponding to that scan position. Construct a first content matrix based on the total lipid amount at all scan positions and the scan position. Perform spatial interpolation on the first content matrix to generate a second content matrix. Convert the total lipid amount in the second content matrix into pixel values ​​to generate lipid distribution data. The lipid characteristic peaks are those with Raman shifts between 600 and 2000 cm⁻¹. -1 Within this range, Raman characteristic peaks related to carbon skeleton vibrations or carbon-carbon unsaturated bond vibrations in lipid molecules are observed. In this embodiment, the lipid characteristic peak is 1066 cm⁻¹. -1 CC stretch key, 1266cm -1 HC = Curved plane, 1445cm -1 C-H2 bent key and 1660cm -1 C=H stretch bond. Example 2

[0031] Reference Figure 5 This embodiment further discloses the fitting method of the gated gradient function in step 3, and the method of determining the gate width based on the laser power when the gated gradient function is actually used in step 5.

[0032] Fitting the gated gradient function. With the gate switch disabled, a series of discrete laser powers P are selected, and the time-domain signal of the observation area of ​​the first microalgae sample corresponding to each laser power is acquired. The gate width T2 is extracted based on the time-domain signal. Using laser power P as the independent variable and gate width T2 as the dependent variable, the exponential decay function is fitted using the nonlinear least squares method to obtain the gated gradient function T2 = τ + (T0 - τ)e -kP Where τ is the laser pulse width, representing the minimum time resolution of the device, k is the attenuation coefficient, which is fitted based on experimental data, e is the natural constant, and T0 is the gate width at the minimum power of the device, which is usually determined based on the device sensitivity.

[0033] Determining the gate width. The laser power in step 5 has been determined. First, the theoretically optimal gate width T3 is calculated using the gate gradient function. However, due to limitations in actual hardware performance, the gate width is usually discretized with fixed time steps, making it impossible to achieve precise configuration of arbitrary continuous values. Therefore, the theoretically optimal gate width T3 is mapped to the most recently available hardware setting: T4 = round(T3 / Δt)Δt, where Δt is the minimum time adjustment step size of the gate detection module (e.g., Δt = 100ps), round() represents rounding to the nearest integer, and T4 is the gate width for that laser power. Example 3

[0034] Reference Figure 6 and Figure 7 This embodiment further discloses a preferred method for generating lipid distribution data based on a second Raman spectrum.

[0035] A lipid quantification model was constructed. In this embodiment, partial least squares regression was used as the basic modeling framework to eliminate multicollinearity among Raman spectral variables and extract the latent variables most relevant to lipids. A series of microalgal samples with gradient total lipid amounts were prepared, and the total lipid amount of the microalgal samples was accurately determined using chemical benchmark methods (such as Soxhlet extraction) as a reference value Y. Raman spectra of these microalgal samples were collected under the same conditions, and the net signal intensity of the lipid characteristic peaks in the Raman spectra (the peak area or peak height of the lipid characteristic peaks after baseline correction) was extracted as the independent variable X. A linear or nonlinear calibration curve between X and Y was established by fitting using the least squares method. This calibration curve is the lipid quantification model that converts signal intensity into total lipid amount.

[0036] Baseline correction of the second Raman spectrum. The least squares fitting method can automatically adapt to different fluorescence background morphologies and achieve accurate baseline drift subtraction. To eliminate the interference of abnormal peaks caused by cosmic rays in the second Raman spectrum on lipid characteristic peaks, this embodiment uses the least squares fitting method: centering on each spectral data point in the second Raman spectrum, a low-order polynomial fitting is performed within a window formed by n adjacent spectral data points. When the deviation between the measured value and the fitted value of any spectral data point exceeds K times the noise standard deviation threshold, the spectral data point is determined to be an outlier and replaced with the fitted value. For the fluorescence background caused by microalgae themselves, an asymmetric reweighted least squares smoothing algorithm is used for adaptive baseline estimation, i.e., solving for a baseline spectral vector b with the same length as the original second Raman spectrum y. The objective function for solving the baseline spectral vector b is... Where I is the total number of spectral data points in the second Raman spectrum, and y i Let b be the signal intensity of the second Raman spectrum at the i-th spectral data point. i Let w be the estimated intensity of the baseline spectral vector at the i-th spectral data point, and w be the weight.i According to y i With b i The difference is asymmetrically distributed, that is, when y i >b i When assigning a smaller weight w i When y i ≤b i When assigning a larger weight w i γ is the smoothing penalty coefficient, used to balance the fitting error and baseline smoothness. The value of γ is determined through leave-one-out cross-validation optimization. 2 b i ) is the second difference of the baseline spectral vector, i.e., Δ 2 b i =b i-1 -b i +b i+1 Δ 2 b i The curvature smoothness of the baseline is constrained. The objective function is minimized through iterative optimization, and the baseline spectral vector b that fits the second Raman spectrum is finally obtained. Subtracting b from the original second Raman spectrum y yields the corrected second Raman spectrum.

[0037] Construction of lipid distribution data. The corrected second Raman spectral data matrix is ​​imported into the established lipid quantification model. The total lipid amount q corresponding to each scanning position is obtained through projection operation. The total lipid amount is mapped to the corresponding scanning position to generate the first content matrix. After improving the spatial resolution of the first content matrix by bicubic spline interpolation, quantile normalization is used to eliminate systematic bias between samples to obtain the second content matrix. Finally, the total lipid amount in the second content matrix is ​​converted into hue and saturation parameters in the HSV color space using the linear color mapping function c=αq+β, where α controls the color dynamic range, β sets the base hue, and c is the pixel value. The generated spatially encoded pseudo-color image is the lipid distribution data. Example 4

[0038] Reference Figure 8A Raman spectroscopy detection device for detecting microalgal lipids includes: a tunable laser, a glass slide, a confocal optical path assembly, a gated detection module, a signal analysis module, a data analysis module, an image generation module, and a system control module. The tunable laser is configured to emit a first laser or a second laser. The glass slide is configured to hold the first or second microalgal sample. The confocal optical path assembly is configured to focus the first or second laser onto the glass slide. The gated detection module is configured to collect Raman scattering signals and generate time-domain signals, and the gated detection module has a gate switch. The signal analysis module is configured to reconstruct the first and second Raman spectra in the frequency domain based on the time-domain signals. The data analysis module is configured to determine the power gradient sequence based on the first Raman spectrum and fit a gated gradient function. The image generation module is configured to generate lipid distribution data based on the second Raman spectrum. The system control module is configured to control the tunable laser, the glass slide, and the gated detection module. Furthermore, the system control module controls the laser power of the tunable laser according to a power gradient sequence. The system control module controls the gate width of the gated detection module according to a gate gradient function. The system control module moves the glass slide according to the focusing position of the first or second laser.

[0039] Reference Figure 9 The confocal optical path assembly includes a bandpass filter 2, a filter 7, a first beam splitter 5, a second beam splitter 8, a reflector 3, a first lens 4, and a second lens 9. The gated detection module includes a photodetector 13 and a gate switch 12. In this embodiment, a broadband light source 6 is also included. The broadband light source 6 is used to output broadband light covering the visible light band to assist in illumination. When step 1 is executed, the tunable laser 1 emits a first laser. The first laser first passes through a bandpass filter 2 to filter out a specific wavelength (e.g., 532 nm), then is redirected by the reflector 3, collimated by the first lens 4, and incident on the first beam splitter 5. At the same time, the broadband light source 6 emits broadband light, which is filtered by the filter 7 to remove clutter and then coupled to the first beam splitter 5. The first laser and the broadband light are combined at the first beam splitter 5 and transmitted together through the second beam splitter 8. Then, they are focused by the second lens 9 onto the surface of the first microalgae sample on the glass slide 10, thereby realizing the excitation of the first laser and the auxiliary illumination of the broadband light. The Raman scattered photons generated by the first microalgae sample after laser excitation return along the original optical path, are collected and reflected by the second beam splitter 8, and then enter the optical path adjustment module 11, and are transmitted to the photodetector 13 for time-domain signal acquisition. Finally, the time-domain signal is analyzed by the signal analysis module. The optical path adjustment module 11 is used to control the relative time of the Raman scattered photons arriving at the photodetector 13, thereby achieving precise synchronization with the gate switch 12. Example 5

[0040] Reference Figure 10 A dynamic monitoring method for microalgae stress treatment includes the following steps: Step 100: The microalgae samples to be tested are divided into one group of first microalgae samples and multiple groups of second microalgae samples. The second microalgae samples are treated with ultrasonic stress at different time periods. The parameters of the ultrasonic stress method are: ultrasonic frequency range of 20kHz-80kHz, ultrasonic power density range of 5W / L-275W / L, and the stress treatment method is pulsed, with a single pulse duration of 1 to 10 minutes and a pulse interval of 0.5 to 2 hours. In this embodiment, the second microalgae samples are divided into experimental groups and blank control groups. The blank control group receives no treatment, while the experimental groups are treated with ultrasonic waves at 20kHz, 28kHz, 40kHz, and 68kHz, respectively.

[0041] Step 200: Detect the first and second microalgal samples using the Raman spectroscopy method for microalgal lipids, generating multiple sets of lipid distribution data. Extract the lipid characteristic peak (1066 cm⁻¹) from the second Raman spectrum of the second microalgal sample. - 1 CC stretch, 1266cm -1 HC = In-plane curvature, 1445cm -1 C-H2 bending and 1660cm -1 By analyzing the signal intensity of C=H stretching, pseudo-color lipid distribution data is generated, enabling the visualization and characterization of subcellular-scale lipid density gradients.

[0042] Step 300: Construct a lipid distribution data spectral sequence by combining multiple sets of lipid distribution data to generate total lipid amounts for different time periods. By performing time-series analysis on the lipid distribution data spectral sequence, the dynamic process and spatial heterogeneity of microalgal lipid accumulation at different time periods under stress treatment are quantitatively revealed. Specifically, such as... Figure 11 The typical lipid distribution data for days 1, 2, 3, 4, 5, and 6 of stress treatment are presented. For example... Figure 12The total lipid content was displayed at different time points. During the two days of stress, the total lipid content in each experimental group remained stable within the range of 28.1%-28.5%. With increasing stress duration, the total lipid content increased in the experimental groups, gradually stabilizing on day 4. Specifically, the total lipid content in the experimental groups corresponding to 20kHz, 28kHz, 40kHz, and 68kHz increased to 33.27%, 33.45%, 33.31%, and 33.07% on day 5, respectively. From the lipid distribution data sequence and total lipid content, it can be observed that the lipid distribution and total amount in the blank control group did not change significantly during the 6-day culture period, while the total intracellular lipid content in all experimental groups (20kHz, 28kHz, 40kHz, and 68kHz) showed a gradient increase with prolonged culture time. Among these, the lipid metabolism response of microalgae cells was most significant and efficient under 28kHz ultrasound stress. Example 6

[0043] In this embodiment, *Scenedesmus obliquus* (FACHB-12) was selected as the microalgae sample to be tested, and the dynamic monitoring method for microalgae stress treatment was implemented. *Scenedesmus obliquus*, with its high proliferation rate and significant lipid-protein dual-component accumulation characteristics, has important application value in the field of bioenergy.

[0044] Pre-stress treatment: *Tetracyclis obliqueis* was activated in BG11 standard medium (1.7 g per liter of culture medium) and then photoautotrophically expanded in a glass reactor to obtain a microalgal suspension. The culture system was strictly controlled under the following optimized parameters: light intensity 3500-4000 lux (incandescent light source, 12h:12h light-dark cycle), shaking frequency once every 12 hours, and temperature gradient maintained at 26±1°C. To accurately control the optimal induction mode of lipid synthesis, biomass was dynamically monitored using a multi-dimensional method: every 48 hours, 5 ml of homogeneous microalgal suspension was taken, centrifuged at 3000×g for 2 min, and the supernatant was used to determine the OD using a UV-1800 spectrophotometer in a 1 cm quartz cuvette. 680 Values ​​were calculated, with deionized water as a blank control. Simultaneously, 8 μL of microalgae suspension (pretreated with a hemocytometer) was analyzed under an inverted fluorescence microscope (IX73, Olympus, Japan), using the ViewProfessional Suite image analysis system (version 2.4) for microalgae cell identification and counting. Each microalgae suspension was analyzed in triplicate. When the culture system entered the late exponential growth phase (OD...), the counts were determined. 680 ≥1.5 and microalgal cell concentration >5×10 6The optimal induction window for lipid synthesis metabolism is determined by the number of cells / mL. At this time, the microalgae suspension is used as the microalgae sample to be tested. The microalgae sample to be tested is divided into a first microalgae sample and a second microalgae sample. The first microalgae sample is used to determine the power gradient sequence, and the second microalgae sample is used to initiate subsequent ultrasonic stress treatment.

[0045] Selection of the Ultrasonic Stress Treatment Device. The ultrasonic stress treatment device used employs an HGD3000 programmable ultrasonic generator (frequency range 20-80kHz, power control 0-100% digital control, drive power 200-2200W), equipped with four sets of modular titanium alloy vibrators (nominal frequencies 20, 28, 40, and 60kHz). The main body of the ultrasonic stress treatment device is seamlessly coupled to the bottom of a customized sonochemical reactor (cylindrical glass cavity, inner diameter 12cm, effective volume 8L) via silicon-based thermally conductive adhesive, achieving efficient acoustic energy conduction. In this embodiment, the operating parameters of the ultrasonic stress treatment device are set as follows: constant power 100W, pulse time 3 minutes, and basic operating voltage 220V (±10% fluctuation compensation).

[0046] Ultrasonic stress treatment. 5L of the second microalgae sample was centrifuged (4000 rpm, 5 min) and washed three times with phosphate buffer, then resuspended in BioBG11 medium and distributed to 5 parallel photobioreactors (6L / group, initial OD). 680 =0.8±0.05), light intensity 180 μmol / m² / s, continuous aeration (2% CO2, 0.2 vvm), temperature maintained at 25±0.5℃, maintaining the same light-dark cycle as the cultivation phase. Four groups of second microalgae samples were used as experimental groups, and one group of second microalgae samples was used as blank control group. The four experimental groups were subjected to ultrasonic treatment at 20 kHz, 28 kHz, 40 kHz and 60 kHz respectively using a numerical control program (fixed parameters: constant power 100W, 3 minutes of pulse per hour), while the blank control group was maintained only by basal culture.

[0047] Lipid distribution data were collected. For six consecutive days starting with ultrasonic stress treatment, 3 grams of agar powder were accurately weighed every 24 hours and dissolved in 100 mL of distilled water. The solution was magnetically stirred and boiled for 3 minutes until completely dissolved, then naturally cooled to 45°C to obtain a warm agar solution. 1 mL of the second microalgae sample from the experimental group was mixed thoroughly with 3 mL of the warm agar solution and quickly poured into a sterile petri dish. After it had fully solidified, the agar gel was cut into thin slices approximately 1 mm thick using a sterile blade. A 532 nm laser-excited confocal Raman spectrometer (spectral range covering 491–2185 cm⁻¹) was used for analysis. -1The second Raman spectrum of the second microalgae sample was acquired using the Raman spectroscopy detection method for microalgae lipids, with a laser power of 15mW and a 50x objective lens. The observation area of ​​the second microalgae sample was a single microalgae cell, and a surface scan (20×20μm² observation area) was performed in 0.7μm increments within this area to obtain lipid distribution data. The entire surface scan was performed through a 520.5cm silicon wafer. -1 Characteristic peaks were used to correct wavenumber drift, and matrix interference was eliminated by background (pure agar) subtraction to ensure the reliability of spectral data. A total of 30 sets of lipid distribution data were collected over 6 days, and lipid distribution data spectral sequences were obtained.

[0048] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting microalgal oil by Raman spectroscopy, characterized by, The method comprises the following steps: Step 1: selecting multiple test regions in the first microalgae sample, irradiating the test regions with a first laser with different laser powers, disabling the gate switch, and collecting multiple time-domain signals corresponding to the different laser powers; Step 2: generating a first Raman spectrum of the time-domain signals, calculating the intensity of the characteristic peaks, determining the damage threshold of the laser power according to the intensity of the characteristic peaks, and determining the power gradient sequence according to the damage threshold; Step 3: finding the gate delay and gate width of each laser power according to the time-domain signals, fitting a gate gradient function according to the different laser powers and corresponding gate widths, and calculating the current gate delay according to the different gate delays; Step 4: selecting laser powers from the power gradient sequence in turn, and performing fluorescence quenching treatment on the observation region of the second microalgae sample with a second laser with the selected laser power; Step 5: after quenching, scanning the observation region of the second microalgae sample with the second laser, enabling the gate switch at the current gate delay, selecting the gate width of the second laser from the gate gradient function, and collecting a second Raman spectrum; Step 6: calculating the fluorescence background intensity in the second Raman spectrum, if the fluorescence background intensity is greater than the quenching threshold, returning to step 4, otherwise entering step 7; Step 7: extracting multiple lipid characteristic peaks based on the second Raman spectrum, and generating lipid distribution data of the second microalgae sample according to the signal intensity of the lipid characteristic peaks.

2. The Raman spectroscopy detection method for microalgal lipids according to claim 1, characterized in that, In step 2, a Raman shift interval of ±15 cm -1 around the Raman shift at which any Raman characteristic peak is located is selected as a characteristic peak window of the Raman characteristic peak, a plurality of baseline points are selected on both sides of the characteristic peak window, a baseline spectrum is obtained by polynomial fitting the baseline points, the signal intensity of the first Raman spectrum in the characteristic peak window is subtracted by the signal intensity of the corresponding baseline spectrum to obtain the net signal intensity of the characteristic peak window, and the integral area of the net signal intensity corresponding to each Raman characteristic peak is the characteristic peak intensity.

3. The method of claim 1, wherein the microalgal oil is a triacylglycerol.

3. The method of claim 1, wherein the microalgal oil is a triacylglycerol. In step 2, the power interval is determined according to the sampling sensitivity and damage threshold of the device, the power center is determined according to the preset proportion of the damage threshold, and the laser powers are extracted from the power center to the sampling sensitivity and damage threshold in turn at equal intervals to form the power gradient sequence.

4. The Raman spectroscopy detection method for microalgal lipids according to claim 1, characterized in that, In step 3, the peak time t1 at which the time-domain signal first appears a maximum value and the valley time t2 at which the time-domain signal first appears a minimum value are found, the gate delay T1=t1-t0, t0 is the emission time of the first laser, and the gate width T2=2(t2-t1).

5. The method of claim 1, wherein the microalgal oil is a triacylglycerol. In step 3, the mean value of the gate delays of different laser powers is the current gate delay, the gate widths corresponding to different laser powers are extracted, and a function in which the gate width monotonically decreases with the increase of the laser power and asymptotically tends to the laser pulse width is fitted as the gate gradient function based on the nonlinear relationship between the laser power and the corresponding gate width.

6. The method of claim 1, wherein the microalgal oil is a triacylglycerol. In step 5, the observation region of the second microalgae sample is scanned, the scanning step length in the horizontal axis direction is Δx, the scanning step length in the vertical axis direction is Δy, Δx and Δy are determined according to the spot diameter d of the second laser, the time-domain signal containing the scanning position information is obtained, and the second Raman spectrum of the time-domain signal is extracted based on the corresponding gate width.

7. The method of claim 2, wherein the microalgal oil is a triacylglycerol.

7. The method of claim 2, wherein the microalgal oil is a triacylglycerol. In step 6, a background waveband of the second Raman spectrum is selected, the average value of the signal intensity of all spectral data points in the background waveband is calculated, and the average value is taken as the fluorescence background intensity under the corresponding laser power. The background waveband is a continuous interval that does not contain a characteristic peak window.

8. The Raman spectroscopy detection method for microalgal lipids according to claim 7, characterized in that, In step 7, the total lipid content at each scanning position is calculated based on the signal intensity of one or more lipid characteristic peaks in the second Raman spectrum corresponding to each scanning position. A first content matrix is ​​constructed based on the total lipid content at all scanning positions and the scanning position. The spatial difference method is performed on the first content matrix to generate a second content matrix. The total lipid content in the second content matrix is ​​converted into pixel values ​​to generate lipid distribution data.

9. A Raman spectroscopic measuring apparatus for implementing the Raman spectroscopic measuring method of the microalgal oil of claim 1, characterized by, include: A tunable laser, configured to emit a first laser or a second laser; A glass slide is configured to hold either the first microalgae sample or the second microalgae sample; The confocal optical path assembly is configured to focus either a first laser or a second laser onto a glass slide; The gated detection module is configured to collect Raman scattering signals and generate time-domain signals. The gated detection module has a gated switch. The signal analysis module is configured to reconstruct the first and second Raman spectra in the frequency domain based on the time-domain signal; The data analysis module is configured to determine the power gradient sequence based on the first Raman spectrum and fit a gated gradient function; The image generation module is configured to generate lipid distribution data based on a second Raman spectrum. The system control module is configured to control the tunable laser, the glass slide, and the gated detection module. The system control module controls the laser power of the tunable laser according to the power gradient sequence; the system control module controls the gate width of the gated detection module according to the gated gradient function; and the system control module moves the glass slide according to the focusing position of the first laser or the second laser.

10. A method for dynamic monitoring of stress treatment of microalgae, characterized in that, Includes the following steps: Step 100: Divide the microalgae samples to be tested into a first group of microalgae samples and multiple groups of second group of microalgae samples. Use the ultrasonic stress method to treat the second group of microalgae samples at different time periods. The parameters of the ultrasonic stress method are: ultrasonic frequency range of 20kHz-80kHz, ultrasonic power density range of 5W / L-275W / L, and the stress treatment method is pulse mode, with a single pulse duration of 1 to 10 minutes and a pulse interval of 0.5 to 2 hours. Step 200: Detect the first microalgae sample and the second microalgae sample using the Raman spectroscopy detection method for microalgae lipids according to claim 1, and generate multiple sets of lipid distribution data; Step 300: Combine multiple sets of lipid distribution data to construct a lipid distribution data spectrum sequence and generate the total lipid amount for different time periods.

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