Method, device, equipment and medium for constructing phospholipid content measurement model of euphausia superba oil

Abstract

The invention provides a method, a device, equipment and a medium for constructing a phospholipid content measurement model of euphausia superba oil, and relates to the technical field of food detection, the content of each phospholipid is associated with Fourier infrared spectrum data of a sample through a partial least square method, a prediction model of the content of each phospholipid is obtained, and the prediction model is used for predicting the phospholipid content of the euphausia superba oil. The prediction model of each phospholipid content can predict the phospholipid content according to the Fourier infrared spectrum data, and the efficiency of predicting the phospholipid content of the euphausia superba oil is improved. The phospholipid content measuring model provided by the invention can be used for independently predicting the content of each phospholipid, so that the accuracy and the efficiency of predicting the phospholipid content are improved. According to the method, three main components, namely PC, LPC and PL, of the euphausia superba oil are selected for prediction, and accurate prediction of the phospholipid content of the euphausia superba oil is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of food detection, and in particular to a method, device, equipment and medium for constructing a phospholipid content measurement model of Antarctic krill oil. Background Art

[0002] Antarctic krill oil (krill oil) is a new food raw material. It is a dark red or reddish brown transparent oily liquid made from krill through washing, crushing, extraction, concentration, filtration and other steps. The main omega-3 polyunsaturated fatty acids in Antarctic krill oil are eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Unlike the triglyceride type contained in the fish oil we are familiar with, the phospholipid type omega-3 polyunsaturated fatty acids rich in Antarctic krill oil are linked to phosphatidylcholine. At the same time, studies have also found that compared with fish oil, Antarctic krill oil is easier to lower blood lipids at a lower intake. Therefore, due to the uniqueness of phosphatidylcholine and astaxanthin antioxidants in Antarctic krill oil, Antarctic krill oil is considered to be more beneficial than any fish oil in the market, and is currently in great demand in the global market. Among the existing methods for determining phospholipids and other components in Antarctic krill oil, traditional determination methods such as high performance liquid chromatography and nuclear magnetic resonance are relatively complex to operate and require high accuracy and operability of the measuring instruments. It takes a long time from sample preparation to determination, and the cost is high. At the same time, the measurement requires the destruction of the original components of Antarctic krill oil and requires a certain amount of sample.

[0003] In summary, the existing determination of the phospholipid content of krill oil is costly, time-consuming and inefficient. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for constructing a phospholipid content measurement model of Antarctic krill oil, so as to solve the defects of high cost, long time consumption and low efficiency in the prior art of measuring the phospholipid content of Antarctic krill oil, and realize efficient and accurate measurement of the phospholipid content of Antarctic krill oil.

[0005] The present invention provides a method for constructing a phospholipid content measurement model of Antarctic krill oil, comprising: obtaining sample Fourier transform infrared spectroscopy data and sample phosphorus element nuclear magnetic resonance data of the sample Antarctic krill oil, obtaining multiple phospholipid contents of the sample Antarctic krill oil based on the sample phosphorus element nuclear magnetic resonance data, wherein the multiple phospholipid contents include the content of phosphatidylcholine PC, the content of lysophosphatidylcholine LPC and the content of total phospholipids PL; using a partial least squares method, constructing a correlation between each phospholipid content and the sample Fourier transform infrared spectroscopy data, obtaining an initial prediction model for each phospholipid content, correcting and predicting the initial prediction model for each phospholipid content, and verifying the prediction model for each phospholipid content after obtaining it.

[0006] According to the method for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention, multiple phospholipid contents of the sample Antarctic krill oil are obtained based on the sample phosphorus element nuclear magnetic resonance data, including: performing spectral phase correction processing and axis calibration processing on the sample phosphorus element nuclear magnetic resonance data to obtain calibrated sample phosphorus element nuclear magnetic resonance data; performing phosphorus element peak selection and integration on the calibrated sample phosphorus element nuclear magnetic resonance data to obtain the PC content, LPC content and PL content.

[0007] According to the method for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention, after obtaining sample Fourier transform infrared spectrum data and sample phosphorus element nuclear magnetic resonance data of the sample Antarctic krill oil, the method further includes: performing baseline correction processing, convolution smoothing processing, normalization processing or first-order derivative processing on the sample Fourier transform infrared spectrum data to obtain pre-processed sample Fourier transform infrared spectrum data; constructing a correlation relationship between each phospholipid content and the pre-processed sample Fourier transform infrared spectrum data to obtain an initial prediction model for each phospholipid content.

[0008] According to the method for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention, sample phosphorus element nuclear magnetic resonance data is obtained based on the following steps: dissolving a fixed mass of sample Antarctic krill oil in an appropriate amount of solvent to obtain an Antarctic krill oil solution, the solvent comprising triphenyl phosphate, deuterated chloroform and deuterated methanol in a set ratio; adding an appropriate amount of buffer solution to the Antarctic krill oil solution to obtain a preparatory solution; sealing, shaking, dissolving and centrifuging the preparatory solution, transferring the organic phase of the preparatory solution to a standard nuclear magnetic resonance tube to obtain a nuclear magnetic resonance test solution, using a Bruker 500-MHz nuclear magnetic resonance spectrometer to determine the phospholipid content of the nuclear magnetic resonance test solution to obtain sample phosphorus element nuclear magnetic resonance data, the Bruker 500-MHz nuclear magnetic resonance spectrometer adopts a Bruker zgig30 pulse program, and the parameter settings of the Bruker 500-MHz nuclear magnetic resonance spectrometer include 128 scan times, 50 ppm spectral width, 9.5 s cycle delay acquisition time, and 128k time domain.

[0009] According to the method for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention, the sample Fourier infrared spectrum data of the sample Antarctic krill oil is obtained, including: using a single attenuated total reflection Fourier infrared spectrometer to sample the sample Antarctic krill oil to obtain initial sample Fourier infrared spectrum data, wherein the scanning range of the single attenuated total reflection Fourier infrared spectrometer is 4000-650cm -1 The scanning resolution of the single attenuated total reflection Fourier infrared spectrometer is 4-8cm -1 The scanning number of the single attenuated total reflection Fourier transform infrared spectrometer is 32-64 times.

[0010] According to the method for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention, the initial prediction model of each phospholipid content is calibrated, tested and verified to obtain a prediction model for each phospholipid content, including: obtaining a determination coefficient of the correlation between each phospholipid content and the sample Fourier transform infrared spectral data, a determination coefficient characterizing the degree of fit between the sample Fourier transform infrared spectral data and a phospholipid content, when each determination coefficient is greater than a set coefficient, obtaining an initial prediction model for each phospholipid content; based on the sample Fourier transform infrared spectral data and each phospholipid content, obtaining a sample set of each phospholipid content, dividing the sample set of phospholipid content into a training set of phospholipid content and a test set of phospholipid content according to a preset ratio; inputting the training set of each phospholipid content into the corresponding initial prediction model of phospholipid content, obtaining an initial prediction model for each phospholipid content; The prediction model is corrected with a root mean square error. When each corrected root mean square error is less than the set correction error, it is determined that the calibration of the initial prediction model for each phospholipid content is completed; the test set of each phospholipid content is input into the corresponding corrected initial prediction model of the phospholipid content to obtain the prediction root mean square error of the initial prediction model for each phospholipid content. When each prediction root mean square error is less than the set prediction error, it is determined that the test of the initial prediction model for each phospholipid content is completed; based on the sample set of each phospholipid content, the initial prediction model of each phospholipid content after the test is cross-validated to obtain the verification root mean square error of the initial prediction model of each phospholipid content after cross-validation. When each verification root mean square error is less than the set verification error, it is determined that the verification of the initial prediction model for each phospholipid content is completed, and the prediction model for each phospholipid content is obtained.

[0011] According to the method for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention, after obtaining the prediction model for each phospholipid content, the method further includes: obtaining Fourier transform infrared spectrum data of the Antarctic krill oil to be tested; inputting the Fourier transform infrared spectrum data to be tested into the prediction model for each phospholipid content, and obtaining the predicted value of each phospholipid content output by the prediction model for each phospholipid content.

[0012] The present invention also provides a device for constructing a phospholipid content measurement model of Antarctic krill oil, comprising: a sample acquisition module, used to obtain sample Fourier transform infrared spectroscopy data and sample phosphorus element nuclear magnetic resonance data of the sample Antarctic krill oil, and obtain multiple phospholipid contents of the sample Antarctic krill oil based on the sample phosphorus element nuclear magnetic resonance data, wherein the multiple phospholipid contents include the content of phosphatidylcholine PC, the content of lysophosphatidylcholine LPC and the content of total phospholipids PL; a model construction module, used to use the partial least squares method to construct a correlation between each phospholipid content and the sample Fourier transform infrared spectroscopy data, obtain an initial prediction model for each phospholipid content, calibrate, test and verify the initial prediction model for each phospholipid content, and obtain a prediction model for each phospholipid content.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the above-mentioned methods for constructing a phospholipid content measurement model for Antarctic krill oil is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for constructing a phospholipid content measurement model for Antarctic krill oil as described above is implemented.

[0015] The method, device, equipment and medium for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention associate each phospholipid content with the sample Fourier infrared spectroscopy data through the partial least squares method, and obtain a prediction model for each phospholipid content, so that the prediction model for each phospholipid content can predict the phospholipid content according to the Fourier infrared spectroscopy data, thereby improving the efficiency of predicting the phospholipid content of Antarctic krill oil. The phospholipid content measurement model of the present invention can predict each phospholipid content separately, thereby improving the accuracy and efficiency of phospholipid content prediction. The present invention selects the three main components of PC, LPC and PL of Antarctic krill oil for prediction, thereby realizing efficient and accurate determination of the phospholipid content of Antarctic krill oil. The present invention is simple, fast and accurate to operate, and the prediction model has strong practicality for krill oil and its products obtained in different production stages such as extraction and purification. It is simple and easy to operate without destroying the original chemical composition and component content of the krill oil sample, and the required sample amount is very small, thereby avoiding the problem of increased detection cost and environmental pollution. In addition, at different production process control points, the concentration changes of key components such as total phospholipids and phosphatidylcholine can be monitored, so that the composition of krill oil can be measured at a relatively low cost in the laboratory or production plant, which has excellent industrial prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic flow chart of the method for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention.

[0018] Figure 2 It is a schematic diagram of the sample Fourier infrared spectrum data of the sample Antarctic krill oil provided by the present invention.

[0019] Figure 3It is a calibration diagram of the initial prediction model of the PC content, the LPC content and the PL content provided by the present invention.

[0020] Figure 4 It is a cross-validation graph of the initial prediction model of the PC content, LPC content and PL content provided by the present invention.

[0021] Figure 5 It is a structural schematic diagram of a device for constructing a model for measuring the phospholipid content of Antarctic krill oil provided by the present invention.

[0022] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The efficiency of real-time detection of krill oil sample concentration at different process control points in the production of health products such as Antarctic krill oil is low. As a rapid qualitative and quantitative analysis technology, Fourier transform infrared spectroscopy is simple and easy to operate, and the required sample volume is very small, which avoids the problem of increased detection costs and environmental pollution. It is currently used in various chemical component structure analysis and content detection.

[0025] At present, it is urgent to develop a simple, fast and accurate infrared spectroscopy detection method for the determination of phospholipids in krill oil, so as to further broaden the application of infrared spectroscopy in the determination of krill oil components.

[0026] Combine the following Figure 1-Figure 6 The invention describes the method, device and equipment for constructing a phospholipid content measurement model of Antarctic krill oil.

[0027] Figure 1 : is a flow chart of the method for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention, such as Figure 1 As shown, the method for constructing a phospholipid content measurement model for Antarctic krill oil includes S100 to S200, and each step is specifically as follows.

[0028] S100: Obtain sample Fourier transform infrared spectrum data and sample phosphorus nuclear magnetic resonance data of the sample Antarctic krill oil, and obtain various phospholipid contents of the sample Antarctic krill oil based on the sample phosphorus nuclear magnetic resonance data, wherein the various phospholipid contents include the content of phosphatidylcholine PC, the content of lysophosphatidylcholine LPC and the content of total phospholipids PL.

[0029] The sample Fourier infrared spectrum data of the sample Antarctic krill oil is obtained. Specifically, the sample Antarctic krill oil is sampled using a single attenuated total reflection Fourier infrared spectrometer to obtain the initial sample Fourier infrared spectrum data, wherein the scanning range of the single attenuated total reflection Fourier infrared spectrometer is 4000-650cm -1 The scanning resolution of the single attenuated total reflection Fourier infrared spectrometer is 4-8cm -1 The scanning number of the single attenuated total reflection Fourier transform infrared spectrometer is 32-64 times.

[0030] The Fourier transform infrared (FTIR) spectrometer was used to measure the sample Antarctic krill oil in the attenuated total reflectance (ATR) mode. The infrared spectrum range is 4000-650cm -1 The scanning resolution is 8cm -1 , scanned 32-64 times, and the data of each sample of Antarctic krill oil was repeatedly collected more than 3 times. For example, using different models of single attenuated total reflection Fourier transform infrared spectrometer (ATR-FTIR), more than 40 samples of Antarctic krill oil were collected three times, and used as calibration spectrum sets (training sets), and more than 20 samples of Antarctic krill oil measured on another ATR-FTIR instrument. Sample Fourier transform infrared spectrum data was used as a prediction set. The sample Fourier transform infrared spectrum data sampled this time was also used to evaluate the repeatability of spectral acquisition and infrared operation procedures when optimizing the initial prediction model, as well as to assess the transferability of calibration between instruments.

[0031] Figure 2 The final Fourier transform infrared spectrum of the sample is 2100-2299 cm -1 , 1260-1225 cm -1 and 1100-1040 cm -1 The peak at 1800-1600 cm is the absorption peak caused by the double frequency and combined frequency of the phospholipid-related component groups in the sample Antarctic krill oil. -1 Band and 970 cm -1 The absorption peak at 3 ) 3The Fourier infrared spectrum of the sample was preliminarily analyzed to remove unstable information peaks. The Antarctic krill oil was studied and modeled according to the full spectrum modeling method through characteristic wavelengths and bands.

[0032] The present invention obtains sample Fourier transform infrared spectrum data according to a single attenuated total reflection Fourier transform infrared spectrum analyzer, thereby improving the accuracy of the sample Fourier transform infrared spectrum data.

[0033] Based on the above embodiment, the sample phosphorus element nuclear magnetic resonance data is obtained based on the following steps: dissolving a fixed mass of sample Antarctic krill oil in an appropriate amount of solvent to obtain an Antarctic krill oil solution, the solvent comprising triphenyl phosphate, deuterated chloroform and deuterated methanol in a set ratio; adding an appropriate amount of buffer solution to the Antarctic krill oil solution to obtain a preparatory solution; sealing, shaking, dissolving and centrifuging the preparatory solution, transferring the organic phase of the preparatory solution to a standard nuclear magnetic resonance tube to obtain a nuclear magnetic resonance test solution, using a Bruker 500-MHz nuclear magnetic resonance spectrometer to determine the phospholipid content of the nuclear magnetic resonance test solution to obtain the sample phosphorus element nuclear magnetic resonance data, the Bruker 500-MHz nuclear magnetic resonance spectrometer adopts a Bruker zgig30 pulse program, and the parameter settings of the Bruker 500-MHz nuclear magnetic resonance spectrometer include 128 scan times, a spectral width of 50 ppm, a cycle delay acquisition time of 9.5 s, and a time domain of 128k.

[0034] The acquisition of the sample phosphorus nuclear magnetic resonance data of the present invention refers to the general principles of the United States Pharmacopoeia <761> Phosphorus element is determined by the nuclear magnetic resonance method specified in nuclear magnetic resonance.

[0035] For example, add about 300-350 mg of sample Antarctic krill oil and 25 mg of triphenyl phosphate (TPP) to a test tube, and add 1 ml of deuterated chloroform and deuterated methanol to dissolve the sample to obtain an Antarctic krill oil solution. After the dissolution is completed, add 1 mL of buffer to the Antarctic krill oil solution to obtain a preparative solution. The buffer includes 0.2 molar concentration of ethylenediaminetetraacetic acid (EDTA), and the buffer is adjusted to pH 7 with 1 M cesium carbonate solution. The preparative solution is sealed and shaken to dissolve for 20 minutes, centrifuged at 12000 rpm, and the organic phase of the preparative solution is transferred to a standard NMR tube to obtain a NMR test solution. The phospholipid content of the above-mentioned NMR test solution is determined using a Bruker 500-MHz NMR spectrum to obtain the sample phosphorus element NMR data. The Bruker zgig30 pulse program is used, the number of scans is 128, the spectrum width is 50 ppm, the cycle delay acquisition time is 9.5 s, and the time domain is 128k.

[0036] The invention obtains a nuclear magnetic resonance test solution by solvent dissolution, oscillation dissolution and centrifugation, which is conducive to accurate nuclear magnetic resonance testing of sample Antarctic krill oil. According to a Bruker 500-MHz nuclear magnetic resonance spectrometer and a Bruker zgig30 pulse program, the accuracy of obtaining sample phosphorus element nuclear magnetic resonance data is improved.

[0037] The contents of various phospholipids in the sample Antarctic krill oil are obtained based on the sample phosphorus element nuclear magnetic resonance data. Specifically, the sample phosphorus element nuclear magnetic resonance data are subjected to spectral phase correction and axis calibration processing to obtain calibrated sample phosphorus element nuclear magnetic resonance data; the calibrated sample phosphorus element nuclear magnetic resonance data are subjected to phosphorus element peak selection and integration to obtain the PC content, LPC content and PL content.

[0038] Bruker TopSpin software was used to process the phosphorus NMR data of the samples. Bruker TopSpin software was used for spectral phase correction, axis calibration processing, peak processing and integration. The contents of various phospholipids included phosphatidylcholine (PC), lysophosphatidylcholine (LPC) and total phospholipids (PL).

[0039] pass 31 P NMR nuclear magnetic resonance analysis determines the nuclear magnetic resonance data (absorption peaks) of components such as PC, LPC, and PL in the sample Antarctic krill oil. The absorption peaks of PC, LPC, and PL are integrated to obtain the content of PC, the content of LPC, and the content of PL. The sample Antarctic krill oil includes unprocessed Antarctic krill oil raw materials, semi-finished Antarctic krill oil at different stages of production, processing, and purification, and the final Antarctic krill oil capsule product. The range, mean, and standard deviation of the measured content of PC, LPC, and PL in the sample Antarctic krill oil are shown in Table 1. There is no correlation between the content of PC and the content of LPC in the sample Antarctic krill oil, but the content of PL is significantly correlated with the content of PC (R 2 >0.93).

[0040] Table 1 Range, mean and standard deviation of phospholipid content of sampled Antarctic krill oil

[0041] The present invention achieves accurate acquisition of PC content, LPC content and PL content by performing spectral phase correction, axis calibration processing, phosphorus peak selection and integration on sample phosphorus element nuclear magnetic resonance data, which is conducive to the subsequent construction of calibration models for the PC content, LPC content and PL content respectively.

[0042] After obtaining the sample Fourier transform infrared spectral data and the sample phosphorus element nuclear magnetic resonance data of the sample Antarctic krill oil, the method also includes: performing baseline correction processing, convolution smoothing processing, normalization processing or first-order derivative processing on the sample Fourier transform infrared spectral data to obtain pre-processed sample Fourier transform infrared spectral data; constructing a correlation between each phospholipid content and the pre-processed sample Fourier transform infrared spectral data to obtain an initial prediction model for the content of each phospholipid.

[0043] All phospholipid content measurement models were developed using OMNIC 7.0, TQ Analyst and other software. OMNIC 7.0 and TQ Analyst were used to preprocess the sample Fourier transform infrared spectroscopy data. Preprocessing included baseline correction, convolution smoothing, normalization or first-order derivative processing.

[0044] According to the sample phosphorus nuclear magnetic resonance data, the contents of various phospholipids were obtained. The partial least squares method was used to construct the correlation between the content of each phospholipid and the Fourier transform infrared spectroscopy data after sample pretreatment, and the initial prediction model of each phospholipid content was obtained.

[0045] The present invention improves the accuracy of the pre-processed sample Fourier transform infrared spectroscopy data through baseline correction processing, convolution smoothing processing, normalization processing or first-order derivative processing, which is conducive to better building a prediction model for the content of each phospholipid.

[0046] S200: The partial least squares method is used to construct the correlation between the content of each phospholipid and the Fourier transform infrared spectroscopy data of the sample to obtain the initial prediction model of each phospholipid content. The initial prediction model of each phospholipid content is calibrated, tested and verified to obtain the prediction model of each phospholipid content.

[0047] Partial least squares (PLS) was used for multivariate correction to link the contents of various phospholipids (including PC content, LPC content and PL content) determined by the sample Fourier transform infrared spectroscopy data and the sample phosphorus element nuclear magnetic resonance data. According to the correlation between each phospholipid content and the sample Fourier transform infrared spectroscopy data, an initial prediction model for each phospholipid content was constructed.

[0048] According to the determination coefficient, corrected root mean square error, predicted root mean square error and verified root mean square error of the correlation between each phospholipid content and the sample Fourier transform infrared spectroscopy data, the initial prediction model of each phospholipid content was calibrated, tested and verified to obtain the prediction model of each phospholipid content.

[0049] The method for constructing a phospholipid content measurement model of Antarctic krill oil provided by the present invention relates each phospholipid content to the Fourier infrared spectroscopy data of the sample through the partial least squares method, and obtains a prediction model for each phospholipid content, so that the prediction model for each phospholipid content can predict the phospholipid content according to the Fourier infrared spectroscopy data, thereby improving the efficiency of predicting the phospholipid content of Antarctic krill oil. The phospholipid content measurement model of the present invention can predict each phospholipid content separately, thereby improving the accuracy and efficiency of phospholipid content prediction. The present invention selects three main components of Antarctic krill oil, namely PC, LPC and PL, for prediction, thereby realizing efficient and accurate determination of the phospholipid content of Antarctic krill oil. The present invention is simple, fast and accurate to operate, and the prediction model has strong practicality for krill oil and its products obtained in different production stages such as extraction and purification. It is simple and easy to operate without destroying the original chemical composition and component content of the krill oil sample, and the required sample amount is very small, thereby avoiding the problem of increased detection cost and environmental pollution. In addition, at different production process control points, the concentration changes of key components such as total phospholipids and phosphatidylcholine can be monitored, so that the composition of krill oil can be measured at a relatively low cost in the laboratory or production plant, which has excellent industrial prospects.

[0050] Based on the above embodiment, the initial prediction model of each phospholipid content is calibrated, tested and verified to obtain a prediction model of each phospholipid content, including S210 to S250, and each step is specifically as follows.

[0051] S210: Obtain the determination coefficient of the correlation between each phospholipid content and the sample Fourier transform infrared spectrum data, wherein a determination coefficient represents the degree of fit between the sample Fourier transform infrared spectrum data and a phospholipid content. When each determination coefficient is greater than a set coefficient, an initial prediction model for each phospholipid content is obtained.

[0052] S220: Based on the sample Fourier transform infrared spectrum data and the content of each phospholipid, a sample set of each phospholipid content is obtained, and the sample set of phospholipid content is divided into a training set of phospholipid content and a test set of phospholipid content according to a preset ratio.

[0053] S230: Input the training set of each phospholipid content into the corresponding initial prediction model of the phospholipid content, obtain the corrected root mean square error of the initial prediction model of each phospholipid content, and when each corrected root mean square error is less than the set correction error, it is determined that the correction of the initial prediction model of each phospholipid content is completed.

[0054] S240: Input the test set of each phospholipid content into the corresponding corrected initial prediction model of the phospholipid content, obtain the prediction root mean square error of the initial prediction model of each phospholipid content, and when each prediction root mean square error is less than the set prediction error, it is determined that the test of the initial prediction model of each phospholipid content is completed.

[0055] S250: Based on the sample set of each phospholipid content, the initial prediction model of each phospholipid content after the test is cross-validated to obtain the verification root mean square error of the initial prediction model of each phospholipid content after cross-validation. When each verification root mean square error is less than the set verification error, it is determined that the verification of the initial prediction model of each phospholipid content is completed, and the prediction model of each phospholipid content is obtained.

[0056] Obtain the determination coefficient of the correlation between each phospholipid content and the sample Fourier transform infrared spectroscopy data. When the determination coefficient corresponding to the phospholipid content is greater than the set coefficient, it means that the phospholipid content and the sample Fourier transform infrared spectroscopy data have a good fit, and the initial prediction model of the phospholipid content is obtained.

[0057] For each phospholipid content, an initial prediction model is constructed. Sample Fourier transform infrared spectroscopy data (e.g., ATR-FTIR spectrum) of sample Antarctic krill oil is collected, and each phospholipid content is used as a reference value to construct an initial prediction model for each phospholipid content. An initial prediction model for a phospholipid content is used to predict a phospholipid content.

[0058] Based on the sample Fourier transform infrared spectroscopy data and phospholipid content, a sample set of each phospholipid content is obtained, wherein the phospholipid content is a label. The sample set of phospholipid content is divided into a training set of phospholipid content and a test set of phospholipid content according to a preset ratio.

[0059] The training set of each phospholipid content (including sample Fourier infrared spectroscopy data and phospholipid content) is input into the corresponding initial prediction model of phospholipid content. According to the error between the label of phospholipid content in the training set and the output value of the initial prediction model, the corrected root mean square error of the initial prediction model of each phospholipid content is obtained. When each corrected root mean square error is less than the set correction error, it is determined that the initial prediction model of each phospholipid content has been corrected. The correction diagram corresponding to the correction process is as follows: Figure 3 As shown. Figure 3 As shown in Figures 3a and 3c, during the calibration process, the determination coefficients (>0.997) of the initial prediction models for PC content and PL content were very high, and the RMSEC values ​​were very low. Figure 3 b. Since the content of LPC in Antarctic krill oil is relatively low compared with the content of PC and other components, the determination coefficient of the initial prediction model of LPC content is also high (>0.99), but it is lower than the R of the initial prediction model of PC content and the initial prediction model of PL content. 2 .

[0060] The test set of each phospholipid content (including sample Fourier infrared spectroscopy data and phospholipid content) is input into the corresponding corrected initial prediction model of the phospholipid content. The prediction root mean square error of the initial prediction model for each phospholipid content is obtained according to the error between the label of the phospholipid content in the test set and the output value of the initial prediction model. When each prediction root mean square error is less than the set prediction error, it is determined that the test of the initial prediction model for each phospholipid content is completed.

[0061] The sample sets of each phospholipid content (including the training set of the above phospholipid content and the test set of phospholipid content) are cross-validated. According to the error between the label of the phospholipid content in the sample set of each phospholipid content and the output value of the initial prediction model, the validation root mean square error of the initial prediction model of each phospholipid content is obtained. When each validation root mean square error is less than the set validation error, it is determined that the validation of the initial prediction model of each phospholipid content is completed, and the prediction model of each phospholipid content is obtained. During the cross-validation process, the determination coefficient corresponding to the prediction model of each phospholipid content is obtained. The corresponding cross-validation graph during the cross-validation process is as follows: Figure 4 shown.

[0062] The coefficient of determination (R) of the prediction model for each phospholipid content 2 ), Root Mean Square Error of Calibration (RMSEC), Root Mean Square Error of Prediction (RMSEP) and Root Mean Square Error of Cross-Validation (RMSECV) are shown in Table 2.

[0063] Table 2 Determination coefficient, corrected RMSE, predicted RMSE and validated RMSE corresponding to the initial prediction model for each phospholipid content

[0064] As shown in Table 2, based on the results of cross-validation, the PC content is in the range of 250-730 mg / g, and the PC prediction accuracy is estimated to be 17 mg / g. The LPC content is in the range of 20-150 mg / g, and the LPC prediction accuracy is 8 mg / g. The PL content is in the range of 310-910 mg / g, and the PL prediction accuracy is 17 mg / g. Figure 4 The cross-validation graphs of the initial prediction model show the best fit line and data dispersion of the phospholipid content in Antarctic krill oil. The cross-validation RMSECV values ​​in Table 2 are slightly higher than the RMSEC and RMSEP values ​​in the calibration and testing process, so the cross-validation R 2The values ​​are slightly lower, especially the cross-validation plots of the initial prediction model of LPC content (such as Figure 4 b). However, the error rate and relative standard deviation of the output values ​​of the initial prediction model of the calibration and testing process and the label of the phospholipid content (obtained through the sample phosphorus element nuclear magnetic resonance data) are very small (the error rate and relative standard deviation are both less than 5%), which shows that the data of the three phospholipid components in Antarctic krill oil predicted by the initial prediction model of each phospholipid content are very accurate.

[0065] The present invention obtains a prediction model for each phospholipid content by correcting, testing and verifying an initial prediction model for each phospholipid content, thereby improving the accuracy of the prediction model for each phospholipid content.

[0066] Based on the above embodiment, after the prediction model of each phospholipid content is obtained, S300 to S400 are further included, and each step is specifically as follows.

[0067] S300: Acquire Fourier transform infrared spectrum data of the Antarctic krill oil to be tested.

[0068] S400: Inputting the Fourier transform infrared spectrum data to be tested into the prediction model of each phospholipid content, and obtaining the predicted value of each phospholipid content output by the prediction model of each phospholipid content.

[0069] After obtaining the prediction model of each phospholipid content, if it is necessary to measure the various phospholipid contents of Antarctic krill oil, it is only necessary to obtain the Fourier transform infrared spectrum data of the Antarctic krill oil to be tested. Input the Fourier transform infrared spectrum data to be tested into the prediction model of each phospholipid content, and the predicted value of each phospholipid content can be obtained.

[0070] The present invention obtains the content of each phospholipid according to the prediction model of each phospholipid content, and the operation is simple, rapid and accurate. The prediction model of each phospholipid content is highly practical for Antarctic krill oil and its products obtained in different production stages such as extraction and purification. It is simple and easy to operate without destroying the original chemical composition and component content of the Antarctic krill oil. The required sample amount is very small, which avoids the increase of detection cost and the problem of environmental pollution, and has excellent industrialization prospects.

[0071] The following is a description of a device for constructing a phospholipid content measurement model for Antarctic krill oil provided by the present invention. The device for constructing a phospholipid content measurement model for Antarctic krill oil described below and the method for constructing a phospholipid content measurement model for Antarctic krill oil described above can be referenced to each other.

[0072] like Figure 5As shown, a device for constructing a phospholipid content measurement model of Antarctic krill oil includes: a sample acquisition module 501, which is used to obtain sample Fourier transform infrared spectrum data and sample phosphorus element nuclear magnetic resonance data of the sample Antarctic krill oil, and obtain multiple phospholipid contents of the sample Antarctic krill oil based on the sample phosphorus element nuclear magnetic resonance data, wherein the multiple phospholipid contents include the content of phosphatidylcholine PC, the content of lysophosphatidylcholine LPC and the content of total phospholipids PL.

[0073] The model building module 502 is used to use the partial least squares method to build the correlation between each phospholipid content and the sample Fourier transform infrared spectrum data, obtain the initial prediction model of each phospholipid content, calibrate, test and verify the initial prediction model of each phospholipid content, and obtain the prediction model of each phospholipid content.

[0074] The phospholipid content measurement model construction device of Antarctic krill oil provided by the present invention associates each phospholipid content with the sample Fourier infrared spectrum data through the partial least squares method, and obtains the prediction model of each phospholipid content, so that the prediction model of each phospholipid content can predict the phospholipid content according to the Fourier infrared spectrum data, thereby improving the efficiency of predicting the phospholipid content of Antarctic krill oil. The phospholipid content measurement model of the present invention can predict each phospholipid content separately, thereby improving the accuracy and efficiency of phospholipid content prediction. The present invention selects the three main components of PC, LPC and PL of Antarctic krill oil for prediction, thereby realizing the efficient and accurate determination of the phospholipid content of Antarctic krill oil. The present invention is simple, fast and accurate to operate, and the prediction model has strong practicality for krill oil and its products obtained in different production stages such as extraction and purification. Under the premise of not destroying the original chemical composition and component content of the krill oil sample, it is simple and easy to operate, and the required sample amount is very small, thereby avoiding the problem of increased detection cost and environmental pollution. In addition, at different production process control points, the concentration changes of key components such as total phospholipids and phosphatidylcholine can be monitored, so that the composition of krill oil can be measured at a relatively low cost in the laboratory or production plant, which has excellent industrial prospects.

[0075] In one embodiment, the sample acquisition module 501 is used to: perform spectral phase correction and axis calibration processing on the sample phosphorus element nuclear magnetic resonance data to obtain calibrated sample phosphorus element nuclear magnetic resonance data; perform phosphorus element peak selection and integration on the calibrated sample phosphorus element nuclear magnetic resonance data to obtain the PC content, the LPC content and the PL content.

[0076] In one embodiment, the model building module 502 is also used to: perform baseline correction, convolution smoothing, normalization or first-order derivative processing on the sample Fourier transform infrared spectral data to obtain pre-processed sample Fourier transform infrared spectral data; construct a correlation between each phospholipid content and the pre-processed sample Fourier transform infrared spectral data to obtain an initial prediction model for each phospholipid content.

[0077] In one embodiment, the sample acquisition module 501 is used to: dissolve a fixed mass of sample Antarctic krill oil in an appropriate amount of solvent to obtain an Antarctic krill oil solution, wherein the solvent includes triphenyl phosphate, deuterated chloroform and deuterated methanol in a set ratio; add an appropriate amount of buffer solution to the Antarctic krill oil solution to obtain a preparatory solution; seal, shake, dissolve and centrifuge the preparatory solution, transfer the organic phase of the preparatory solution to a standard nuclear magnetic resonance tube to obtain a nuclear magnetic resonance test solution, use a Bruker 500-MHz nuclear magnetic resonance spectrometer to measure the phospholipid content of the nuclear magnetic resonance test solution, and obtain sample phosphorus element nuclear magnetic resonance data, the Bruker 500-MHz nuclear magnetic resonance spectrometer uses a Bruker zgig30 pulse program, and the parameter settings of the Bruker 500-MHz nuclear magnetic resonance spectrometer include 128 scan times, 50 ppm spectrum width, 9.5 s cycle delay acquisition time, and 128k time domain.

[0078] In one embodiment, the sample acquisition module 501 is used to: use a single attenuated total reflection Fourier transform infrared spectrometer to sample the sample Antarctic krill oil to obtain initial sample Fourier transform infrared spectrum data, wherein the scanning range of the single attenuated total reflection Fourier transform infrared spectrometer is 4000-650cm -1 The scanning resolution of the single attenuated total reflection Fourier infrared spectrometer is 4-8cm -1 The scanning number of the single attenuated total reflection Fourier transform infrared spectrometer is 32-64 times.

[0079] In one embodiment, the model building module 502 is used to: obtain the determination coefficient of the correlation between each phospholipid content and the sample Fourier transform infrared spectral data, where a determination coefficient represents the degree of fit between the sample Fourier transform infrared spectral data and a phospholipid content, and when each determination coefficient is greater than a set coefficient, obtain an initial prediction model for each phospholipid content; based on the sample Fourier transform infrared spectral data and each phospholipid content, obtain a sample set of each phospholipid content, and divide the sample set of phospholipid content into a training set of phospholipid content and a test set of phospholipid content according to a preset ratio; input the training set of each phospholipid content into the corresponding initial prediction model of the phospholipid content, obtain the corrected root mean square error of the initial prediction model for each phospholipid content, and when each corrected root mean square error is greater than a set value, obtain a sample set of each phospholipid content; and When the error is less than the set correction error, it is determined that the calibration of the initial prediction model for each phospholipid content is completed; the test set of each phospholipid content is input into the corresponding corrected initial prediction model of the phospholipid content to obtain the prediction root mean square error of the initial prediction model for each phospholipid content, and when each prediction root mean square error is less than the set prediction error, it is determined that the test of the initial prediction model for each phospholipid content is completed; based on the sample set of each phospholipid content, the initial prediction model of each phospholipid content after the test is cross-validated to obtain the verification root mean square error of the initial prediction model of each phospholipid content after cross-validation, and when each verification root mean square error is less than the set verification error, it is determined that the verification of the initial prediction model for each phospholipid content is completed, and the prediction model for each phospholipid content is obtained.

[0080] In one embodiment, the model building module 502 is further used to: obtain the Fourier transform infrared spectrum data of the Antarctic krill oil to be tested; input the Fourier transform infrared spectrum data to be tested into the prediction model of each phospholipid content, and obtain the predicted value of each phospholipid content output by the prediction model of each phospholipid content.

[0081] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the method for constructing a phospholipid content measurement model of Antarctic krill oil, the method comprising: obtaining sample Fourier infrared spectroscopy data and sample phosphorus nuclear magnetic resonance data of the sample Antarctic krill oil, obtaining multiple phospholipid contents of the sample Antarctic krill oil based on the sample phosphorus nuclear magnetic resonance data, the multiple phospholipid contents including the content of phosphatidylcholine PC, the content of lysophosphatidylcholine LPC and the content of total phospholipids PL; using the partial least squares method, constructing the correlation between each phospholipid content and the sample Fourier infrared spectroscopy data, obtaining the initial prediction model of each phospholipid content, calibrating, testing and verifying the initial prediction model of each phospholipid content, and obtaining the prediction model of each phospholipid content.

[0082] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0083] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the method for constructing a phospholipid content measurement model for Antarctic krill oil provided by the above methods, the method comprising: obtaining sample Fourier transform infrared spectroscopy data and sample phosphorus element nuclear magnetic resonance data of the sample Antarctic krill oil, obtaining multiple phospholipid contents of the sample Antarctic krill oil based on the sample phosphorus element nuclear magnetic resonance data, the multiple phospholipid contents including the content of phosphatidylcholine PC, the content of lysophosphatidylcholine LPC and the content of total phospholipids PL; using partial least squares method, constructing a correlation between each phospholipid content and the sample Fourier transform infrared spectroscopy data, obtaining an initial prediction model for each phospholipid content, and calibrating, testing and verifying the initial prediction model for each phospholipid content to obtain a prediction model for each phospholipid content.

[0084] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0085] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a phospholipid content measurement model for Antarctic krill oil, characterized in that: include: Obtaining sample Fourier transform infrared spectroscopy data and sample phosphorus nuclear magnetic resonance data of the sample Antarctic krill oil, and obtaining various phospholipid contents of the sample Antarctic krill oil based on the sample phosphorus nuclear magnetic resonance data, wherein the various phospholipid contents include the content of phosphatidylcholine PC, the content of lysophosphatidylcholine LPC and the content of total phospholipids PL; The partial least squares method is used to construct a correlation between the content of each phospholipid and the Fourier transform infrared spectrum data of the sample to obtain an initial prediction model for the content of each phospholipid. The initial prediction model for the content of each phospholipid is calibrated, tested and verified to obtain a prediction model for the content of each phospholipid.

2. The method for constructing a phospholipid content measurement model of Antarctic krill oil according to claim 1, characterized in that: The method of obtaining the content of multiple phospholipids in the Antarctic krill oil sample based on the phosphorus nuclear magnetic resonance data of the sample includes: Performing spectrum phase correction and axis calibration on the sample phosphorus nuclear magnetic resonance data to obtain calibrated sample phosphorus nuclear magnetic resonance data; The phosphorus element nuclear magnetic resonance data of the calibrated sample is subjected to phosphorus element peak selection and integration to obtain the content of the PC, the content of the LPC and the content of the PL.

3. The method for constructing a phospholipid content measurement model of Antarctic krill oil according to claim 1, characterized in that: After obtaining the sample Fourier transform infrared spectrum data and the sample phosphorus nuclear magnetic resonance data of the sample Antarctic krill oil, the method further includes: Performing baseline correction processing, convolution smoothing processing, normalization processing or first-order derivative processing on the sample Fourier transform infrared spectrum data to obtain pre-processed sample Fourier transform infrared spectrum data; A correlation relationship between the content of each phospholipid and the Fourier transform infrared spectroscopy data of the pretreated sample is constructed to obtain an initial prediction model for the content of each phospholipid.

4. The method for constructing a phospholipid content measurement model of Antarctic krill oil according to claim 1, characterized in that: The sample phosphorus element nuclear magnetic resonance data is obtained based on the following steps: Dissolving a fixed mass of the sample Antarctic krill oil in an appropriate amount of solvent to obtain an Antarctic krill oil solution, wherein the solvent comprises triphenyl phosphate, deuterated chloroform and deuterated methanol in a set ratio; Adding an appropriate amount of buffer solution to the Antarctic krill oil solution to obtain a preparatory solution; The preparatory solution is sealed, shaken to dissolve and centrifuged, and the organic phase of the preparatory solution is transferred to a standard nuclear magnetic resonance tube to obtain a nuclear magnetic resonance test solution. The phospholipid content of the nuclear magnetic resonance test solution is determined using a Bruker 500-MHz nuclear magnetic resonance spectrometer to obtain phosphorus element nuclear magnetic resonance data of the sample. The Bruker 500-MHz nuclear magnetic resonance spectrometer adopts a Bruker zgig30 pulse program. The parameter settings of the Bruker 500-MHz nuclear magnetic resonance spectrometer include 128 scan times, a spectral width of 50 ppm, a cycle delay acquisition time of 9.5 s, and a time domain of 128k.

5. The method for constructing a phospholipid content measurement model of Antarctic krill oil according to claim 1, characterized in that: The method of obtaining sample Fourier infrared spectroscopy data of the sample Antarctic krill oil comprises: The sample Antarctic krill oil is sampled using a single attenuated total reflection Fourier transform infrared spectrometer to obtain initial sample Fourier transform infrared spectrum data, wherein the scanning range of the single attenuated total reflection Fourier transform infrared spectrometer is 4000-650cm -1 The scanning resolution of the single attenuated total reflection Fourier transform infrared spectrometer is 4-8cm -1 The scanning number of the single attenuated total reflection Fourier transform infrared spectrometer is 32-64 times.

6. The method for constructing a phospholipid content measurement model of Antarctic krill oil according to claim 1, characterized in that: The initial prediction model of each phospholipid content is corrected, tested and verified to obtain a prediction model of each phospholipid content, including: Obtaining a determination coefficient of the correlation between the content of each phospholipid and the Fourier transform infrared spectrum data of the sample, wherein one determination coefficient represents the degree of fit between the Fourier transform infrared spectrum data of the sample and one phospholipid content, and when each determination coefficient is greater than a set coefficient, obtaining an initial prediction model for the content of each phospholipid; Based on the Fourier transform infrared spectrum data of the sample and the content of each phospholipid, a sample set of each phospholipid content is obtained, and the sample set of phospholipid content is divided into a training set of phospholipid content and a test set of phospholipid content according to a preset ratio; Inputting the training set of each phospholipid content into the corresponding initial prediction model of the phospholipid content, obtaining the corrected root mean square error of the initial prediction model of each phospholipid content, and determining that the correction of the initial prediction model of each phospholipid content is completed when each corrected root mean square error is less than the set correction error; Inputting the test set of each phospholipid content into the corresponding corrected initial prediction model of the phospholipid content, obtaining the prediction root mean square error of the initial prediction model of each phospholipid content, and determining that the test of the initial prediction model of each phospholipid content is completed when each of the prediction root mean square errors is less than the set prediction error; Based on the sample set of each phospholipid content, the initial prediction model of each phospholipid content after the test is cross-validated to obtain the verification root mean square error of the initial prediction model of each phospholipid content after cross-validation. When each of the verification root mean square errors is less than the set verification error, it is determined that the verification of the initial prediction model of each phospholipid content is completed, and the prediction model of each phospholipid content is obtained.

7. The method for constructing a phospholipid content measurement model of Antarctic krill oil according to claim 1, characterized in that: After obtaining the prediction model of each phospholipid content, the method further includes: Obtaining Fourier infrared spectrum data of the Antarctic krill oil to be tested; The Fourier transform infrared spectrum data to be tested is input into the prediction model of each phospholipid content, and the predicted value of each phospholipid content output by the prediction model of each phospholipid content is obtained.

8. A device for constructing a model for measuring the phospholipid content of Antarctic krill oil, characterized in that: include: A sample acquisition module, used to acquire sample Fourier transform infrared spectrum data and sample phosphorus nuclear magnetic resonance data of the sample Antarctic krill oil, and acquire the contents of multiple phospholipids in the sample Antarctic krill oil based on the sample phosphorus nuclear magnetic resonance data, wherein the contents of multiple phospholipids include the content of phosphatidylcholine PC, the content of lysophosphatidylcholine LPC and the content of total phospholipids PL; The model building module is used to use the partial least squares method to build the correlation between each phospholipid content and the sample Fourier transform infrared spectrum data, obtain the initial prediction model of each phospholipid content, calibrate and predict the initial prediction model of each phospholipid content, and verify the prediction model of each phospholipid content after obtaining it.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for constructing a phospholipid content measurement model for Antarctic krill oil according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for constructing a phospholipid content measurement model for Antarctic krill oil according to any one of claims 1 to 7 is implemented.

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