A method for nondestructive detection of EPA and DHA content in the adductor muscle of scallops based on Raman spectroscopy

Abstract

The application provides a method for nondestructive detection of the contents of EPA and DHA in scallop adductor muscle based on Raman spectrum, which comprises the following steps: firstly, a proper amount of gill filament tissue is taken from a living scallop by a nondestructive method, and then a frozen section is prepared; secondly, the section is subjected to Raman laser scanning to obtain original data; and finally, after pretreatment such as normalization, noise reduction and baseline correction, a characteristic wavelength is selected, and a model for predicting the content of unsaturated fatty acid in the adductor muscle by using the gill filament Raman data across tissues is established in combination with the lipid group data of the adductor muscle. Compared with the existing method for detecting the content of unsaturated fatty acid in scallops, the determination method of the application can realize the rapid, accurate and simple detection of the content of unsaturated fatty acid in the main edible part, i.e., the striated muscle of scallops, under the condition of ensuring the survival of scallops, and greatly reduces the detection cost, thereby providing an efficient and simple technical means for the breeding of shellfish quality traits, and being beneficial to the popularization and promotion in the comprehensive evaluation and genetic improvement of aquatic germplasm.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of genetic breeding of shellfish, and particularly relates to a method for nondestructive detection of contents of EPA and DHA in adductor muscle of scallop based on Raman spectrum. BACKGROUND

[0002] The comprehensive quality of aquatic products is determined by nutritional value and sensory quality, and the nutritional value depends on the content and composition of biochemical components. In shellfish, high content of unsaturated fatty acids, especially EPA and DHA, is the key value. EPA and DHA help maintain the healthy function of brain and retina, regulate cell activity and health, and reduce the risk of cardiovascular disease, which are of great significance to human health. Therefore, the content of EPA and DHA can be used as an important indicator for evaluating the quality of shellfish, and is also an important target for breeding high-value varieties of shellfish. However, in order to carry out large-scale breeding, it is necessary to ensure the determination of DHA and EPA contents under the survival state of parent individuals, so non-lethal determination of breeding individuals is an important technical means for breeding shellfish.

[0003] In the existing method, the detection of DHA and EPA is mainly carried out by chemical analysis methods such as gas chromatography-mass spectrometry, but the determination of adductor muscle tissue after scallop shell opening cannot guarantee the survival state of individuals. Therefore, it is necessary to establish a non-lethal detection method for unsaturated fatty acids of shellfish. SUMMARY

[0004] The purpose of the present application is to provide a method for nondestructive detection of contents of EPA and DHA in adductor muscle of scallop based on Raman spectrum, which can quickly and non-lethally detect the contents of EPA and DHA in adductor muscle, the main edible part of scallop, and has lower detection cost.

[0005] The method for nondestructive detection of contents of EPA and DHA in adductor muscle of scallop based on Raman spectrum provided by the present application comprises the following steps:

[0006] 1) Take part of gill filament tissue of live scallop, freeze and embed the taken gill filament tissue to prepare 5-10 μm thick frozen sections on an aluminum-coated glass slide;

[0007] 2) Obtain original Raman spectrum: use a micro-Raman spectrometer to scan the sample obtained in step 1), randomly select multiple sites for scanning of each sample, and obtain the original Raman spectrum of the sites;

[0008] The parameters of the micro Raman spectrometer were set as follows: a 532 nm laser was used as the excitation source, the laser power was 10 mW, the grating was 600 g / mm, the spectral range was set to 191-3945 cm⁻¹, the integration time was 7 s, the objective lens was 100x, and the spot size was 300 nm. Before acquisition, the instrument was calibrated with a silicon wafer to calibrate the silicon wafer signal to 520.7 cm⁻¹.

[0009] 3) Gill filament Raman data processing:

[0010] After area normalization of the raw Raman spectra obtained in step 2), the average spectrum was calculated. Then, the data was denoised and baseline corrected. The gill filament Raman data with characteristic wavelengths of 935, 1312, 1444, 1655, and 2928 cm⁻¹ were selected. The prediction models y(EPA) = 0.8427x + 0.0234 and y(DHA) = 1.0028x + 0.0004 were used to convert the EPA and DHA content of the scallop adductor muscle. In the formula, x is the selected gill filament Raman spectrum dataset.

[0011] The method for denoising and baseline correction of Raman data is SG smoothing and multivariate scattering correction (MSC).

[0012] This invention discloses a method for non-destructively detecting EPA and DHA content in the adductor muscle of scallops using Raman spectroscopy. Specifically, the method involves: firstly, taking an appropriate amount of gill filament tissue from a living scallop using a non-destructive method, preparing frozen sections 5-10 μm thick, and then performing Raman laser scanning on the sections to obtain raw data. After preprocessing such as normalization, noise reduction, and baseline correction, characteristic wavelengths are selected, and a model is established using gill filament Raman data to predict the content of unsaturated fatty acids in the adductor muscle across tissues, in conjunction with adductor muscle lipid profile data. Compared to existing methods for detecting the unsaturated fatty acid content of scallops, such as mass spectrometry, the method of this invention eliminates the need for pretreatment of tissues, such as grinding, extraction, vortexing, and centrifugation. Instead, after preparing frozen sections of the tissue, Raman spectroscopy is performed directly on the sample. Combined with a non-destructive method for obtaining gill filament tissue, this method enables rapid, accurate, and convenient detection of the unsaturated fatty acid content in the skeletal muscle, the main edible part of scallops, while ensuring the scallops remain alive. It also significantly reduces detection costs, providing an efficient and convenient technical means for the breeding of shellfish quality traits. This facilitates its widespread adoption and promotion in the comprehensive evaluation and genetic improvement of aquatic germplasm. Attached Figure Description

[0013] Figure 1 This is a technical roadmap of the present invention.

[0014] Figure 2 This is a Raman spectrum of the gill filament tissue of the Yesso scallop and a diagram showing the location of characteristic wavelengths.

[0015] Figure 3 These are images showing the effects before and after spectral preprocessing.

[0016] Figure 4 This is a regression diagram of the EPA content predicted by the established prediction model and the actual value.

[0017] Figure 5 This is a regression diagram of the DHA content predicted by the established prediction model and the actual value. Detailed Implementation

[0018] Raman spectroscopy is a rapidly developing method for analyzing molecular structure and chemical bonds based on the Raman scattering effect. It can provide evidence for distinguishing biological tissues at the molecular level by observing the changing Raman frequency shifts and intensities exhibited by different biomolecules. Given the synergistic relationship between the material components of different tissues, this invention establishes a cross-tissue prediction model for shellfish by training gill filament Raman spectral data and the DHA / EPA content of the adductor muscle, thereby achieving non-destructive determination of DHA content in the adductor muscle of shellfish.

[0019] Spectroscopic techniques for detecting the quality of aquatic products rely on the establishment of mathematical models. These models are widely used in freshness detection, quality grading, and adulteration analysis of aquatic products, but most are only for qualitative analysis, with few reports on quantitative models. Existing models are mainly based on the consistency of sample composition and properties. For heterogeneous tissues, existing simple models cannot be directly applied; denoising of tissue heterogeneity information and selection of cross-tissue characteristic spectra are necessary to improve the model's generalization ability. Furthermore, the amount of gill filament tissue collected in trace amounts is limited. Ensuring the tissue embedding morphology is crucial for acquiring effective spectral signals. Therefore, preprocessing steps such as tissue embedding and serial sectioning require precise pre-judgment of sample location to obtain high-quality sections of trace gill filament tissue.

[0020] This invention optimizes the method from two main aspects: gill filament tissue sectioning and cross-tissue prediction modeling. It provides a method for non-destructive detection of EPA and DHA content in scallop adductor muscle based on Raman spectroscopy. It offers a method for preparing cryopreserved sections from trace amounts of gill filaments that are easy to collect signals. By acquiring Raman spectral data of gill filament tissue and combining it with fatty acid content data of shellfish adductor muscle, a model for predicting unsaturated fatty acid content across tissue spectroscopy is established. This enables non-lethal detection and evaluation of scallop quality, providing an important method for the breeding of high-quality shellfish varieties.

[0021] The present invention will now be described in further detail with reference to specific embodiments.

[0022] Example 1: Establishment of a non-destructive method for detecting EPA and DHA content in the adductor muscle of scallops based on Raman spectroscopy

[0023] 1. Preparation of the sample to be tested:

[0024] Sixty active scallops were selected. Mizuhopecten yessoensis Using pointed forceps, while ensuring the scallop is alive, a suitable amount of gill filament tissue was collected. The collected gill filament tissue was cryo-embedded to prepare frozen sections with a thickness of 5-10 μm. These sections were placed on aluminized glass slides and stored at -80 ℃ for later analysis. After dissecting the scallop, at least 200 mg of adductor muscle tissue was collected, flash-frozen in liquid nitrogen, and then stored at -80 ℃ for later analysis.

[0025] 2. The measurement steps are as follows:

[0026] Raman raw data determination: First, the measurement parameters were set, using a 532nm laser as the excitation source, a laser power of 10 mW, a grating of 600 g / mm, and a spectral range of 191-3945 cm⁻¹. -1 The integration time was 7 s, the objective lens was 100x, and the spot size was approximately 300 nm. Before acquisition, the instrument was calibrated using a silicon wafer to adjust the signal to 520.7 cm⁻¹. -1 Using a micro Raman spectrometer with the parameters set, the sample was laser-scanned. Ten points were randomly selected for scanning each sample to obtain ten raw Raman spectra, resulting in a total of 600 raw spectral data.

[0027] Lipidome analysis: Adductor muscle tissue samples were thawed on ice after being removed from a -80°C freezer and ground into powder using a homogenizer (30 Hz, 1 min). 50 mg of the ground sample was accurately weighed into a new EP tube, and 150 μL of methanol solution, 200 μL of methyl tert-butyl ether solution, and 50 μL of 36% phosphoric acid solution were added for extraction. The mixture was vortexed for 3 min and centrifuged at 4°C and 12000 r / min for 5 min. 200 μL of the supernatant was transferred and dried under nitrogen, and 300 μL of 15% boron trifluoride methanol solution was added. The mixture was vortexed for 3 min and kept in a 60°C oven for 30 min. After cooling to room temperature, 500 μL of n-hexane solution and 200 μL of saturated sodium chloride solution were accurately added. The mixture was vortexed for 3 min and centrifuged at 4°C and 12000 r / min for 5 min. 100 μL of the n-hexane layer was then transferred for analysis. Lipidome data were acquired using gas chromatography-mass spectrometry (GC-MS).

[0028] 3. Data processing and model building:

[0029] The area-mean integral of the raw Raman signal data was adjusted to 1 using Origin 2019 to eliminate spectral amplitude differences between different samples. The peak mean of parallel Raman data after area normalization for the same sample was taken as the corresponding Raman data. Raman spectroscopy of gill filaments is affected by biofluorescence background interference, and due to inherent stability limitations (laser, spectrometer), background noise and baseline drift occur, significantly impacting Raman spectroscopy analysis results. Therefore, before further data processing and analysis, it is necessary to perform baseline correction and smoothing on the acquired Raman spectral data to eliminate the effects of baseline drift and noise. After comparing the effects of several commonly used preprocessing methods (SG smoothing, median smoothing, standard normal transformation, and multivariate scattering correction) individually and in combination, it was found that using TheUnscrambler X 10.4 software, first applying SG smoothing for curve smoothing and then applying multivariate scattering correction for baseline correction, yielded the best preprocessing effect for gill filament Raman spectroscopy. Therefore, this method was subsequently selected for data noise reduction and baseline correction.

[0030] The Continuous Projection (SPA) algorithm was selected using MATLAB R2014B to couple cross-tissue spectral data. By analyzing the correlation between gill filament Raman data and adductor muscle lipid content, characteristic wavelengths of gill filament spectral data with predictive indicators for adductor muscle were screened. The selected characteristic wavelengths were 935, 1312, 1444, 1655, and 2928 cm⁻¹, which exhibited strong characteristic features of gill filament Raman data and a strong correlation with adductor muscle lipidome data. The screened gill filament Raman spectral data was used as the independent variable dataset x for model construction. For the lipidome data, the total fatty acid content of each adductor muscle sample was first calculated, and then the proportions of EPA and DHA content relative to the total fatty acid content were calculated, which were used as the dependent variable dataset y proposed by the model.

[0031] Commonly used Raman prediction models include Support Vector Machine Regression (SVR), Least Squares Regression (PLSR), and Principal Component Regression (PCR). After comparing the accuracy and robustness of these three models, PLSR performed best. With a training set of 60 samples, the resulting prediction models were y(EPA) = 0.8427x + 0.0234 and y(DHA) = 1.0028x + 0.0004.

[0032] Example 2: Model Performance Testing

[0033] 1. Sample preparation:

[0034] Forty scallop individuals were randomly selected from the population, and appropriate amounts of gill filament tissue and adductor muscle tissue were collected. The gill filament tissue was cryo-embedded and prepared into frozen sections with a thickness of 5-10 μm, which were then placed on aluminized glass slides as samples for Raman spectroscopy. At least 200 mg of adductor muscle tissue was taken, flash-frozen in liquid nitrogen, and subjected to a series of pretreatment procedures. Lipidome data were then collected using gas chromatography-mass spectrometry (GC-MS).

[0035] 2. Data Measurement

[0036] Raman raw data determination: First, the measurement parameters were set, using a 532 nm laser as the excitation source, a laser power of 10 mW, a grating of 600 g / mm, a spectral range of 191-3945 cm⁻¹, an integration time of 7 s, a 100x objective lens, and a spot size of approximately 300 nm. Before acquisition, the instrument was calibrated using a silicon wafer to adjust the signal to 520.7 cm⁻¹. Using the micro Raman spectrometer with the parameters set, the sample was scanned with laser. Ten points were randomly selected from each sample for scanning, obtaining 10 raw Raman spectra, for a total of 400 raw spectral data.

[0037] Lipidome analysis: Adductor muscle tissue samples were thawed on ice after being removed from a -80°C freezer and ground into powder using a homogenizer (30 Hz, 1 min). 50 mg of the ground sample was accurately weighed into a new EP tube, and 150 μL of methanol solution, 200 μL of methyl tert-butyl ether solution, and 50 μL of 36% phosphoric acid solution were added for extraction. The mixture was vortexed for 3 min and centrifuged at 4°C and 12000 r / min for 5 min. 200 μL of the supernatant was transferred and dried under nitrogen, and 300 μL of 15% boron trifluoride methanol solution was added. The mixture was vortexed for 3 min and kept in a 60°C oven for 30 min. After cooling to room temperature, 500 μL of n-hexane solution and 200 μL of saturated sodium chloride solution were accurately added. The mixture was vortexed for 3 min and centrifuged at 4°C and 12000 r / min for 5 min. 100 μL of the n-hexane layer was then transferred for analysis. Lipidome data were acquired using gas chromatography-mass spectrometry (GC-MS).

[0038] 3. Model Performance Evaluation

[0039] The measured raw Raman data were normalized by adjusting the area-mean integral of the raw Raman signal data to 1, followed by curve smoothing using SG smoothing and baseline correction using multivariate scattering correction. The selected characteristic wavelength peaks were then used as the set of independent variables x. Using MATLAB R2014B software, the dataset x was used as the model input file and applied to the cross-tissue prediction model of EPA / DHA content listed in Table 1. Running the predict command yielded the predicted values ​​of EPA and DHA content in the adductor muscle.

[0040] The coefficient of determination (R²) between the EPA / DHA content predicted by the computational model and the actual EPA / DHA content measured by GC-MS 2 ), R 2 This is used to measure the model's performance in explaining the variance of the target variable. Its value ranges from 0 to 1, where 1 indicates a perfect fit and 0 indicates that the model cannot explain the variance of the target variable, thus evaluating the model's predictive performance. Specific test results are shown in Table 1:

[0041] Table 1: Performance Test Results of the Prediction Model

[0042] Predicted object EPA DHA Regression equation y = 0.8427x + 0.0234 y = 1.0028x + 0.0004 R of the full spectrum graph prediction model 2 ]] 0.544 0.617 R 2 ]] 0.867 0.860

[0043] R predicted by comparing the full Raman spectrum of the gill filaments without characteristic wavelength screening 2 The correlation coefficients of the training models obtained by traditional training methods were only 0.544 and 0.617, respectively. The prediction accuracy of the models trained by using the selected cross-tissue coupled spectroscopy was significantly improved. The calculated R2 of the prediction models for EPA and DHA were both above 0.86, indicating that the cross-tissue prediction feature wavelengths selected by this invention have strong features and strong correlation between EPA and DHA content, and can perform efficient and accurate cross-tissue prediction through gill filament spectroscopy.

[0044] As shown in Table 1, the model established according to this method for predicting the unsaturated fatty acid content of adductor muscle tissue using Raman spectroscopy of gill filament tissue across tissues has a determination coefficient greater than 0.85 between the predicted and actual values, indicating that the model has good predictive performance. Therefore, the determination method of this embodiment can be applied to the detection of scallop quality traits. The determination method of this embodiment can quickly, accurately, and non-destructively detect the unsaturated fatty acid content of scallops, and greatly reduce the detection cost. It can be promoted and applied in aquatic breeding.

[0045] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0046] The technical solution provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for non-destructive detection of the content of EPA and DHA in scallop adductor muscle based on Raman spectroscopy, characterized in that, The method comprises the following steps: 1) Take part of the gill filament tissue of the living scallop, freeze and embed the taken gill filament tissue, make frozen sections, and place the frozen sections on an aluminum-coated glass slide; 2) Obtain the original Raman spectrum: use a micro-Raman spectrometer to scan the sample prepared in step 1), randomly select multiple sites for each sample for scanning, and obtain the original Raman spectrum of the sites; The micro-Raman spectrometer is set as follows: use a 532 nm laser as the excitation light source, the laser power is 10 mW, the grating is 600 g / mm, the spectral range is set to 191-3945 cm-1, the integration time is 7 s, the 100 times objective lens, the spot size is 300 nm; Before collection, first calibrate the instrument with a silicon wafer, and calibrate the silicon wafer signal to 520.7 cm-1; 3) Raman data processing of gill filament: After area normalization of the original Raman spectrum obtained in step 2), the average spectrum is calculated, then the data is processed by noise reduction and baseline correction, the gill filament Raman data of characteristic wavelength is screened, and the prediction model y(EPA)=0.8427x+0.0234 and y(DHA)=1.0028x+0.0004 are used to convert into scallop adductor muscle EPA and DHA content; In the formula, x is the screened gill filament Raman spectrum data set; In the 1), the frozen sections with a thickness of 5-10 μm are made; In the 2), the multiple sites are randomly selected, which are 10 sites; In the 3), the gill filament Raman data of characteristic wavelength are the gill filament Raman data with characteristic wavelengths of 935, 1312, 1444, 1655, and 2928 cm-1; In the 3), the Raman data is processed by noise reduction, which is processed by SG smoothing method; In the 3), the Raman data is processed by baseline correction, which is processed by multivariate scatter correction.

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