CN112285031A - Method for rapidly representing umami intensity of salmon based on hyperspectral imaging technology - Google Patents
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Abstract
The invention belongs to the technical field of meat quality detection, and relates to a method for rapidly representing the umami intensity of salmon based on a hyperspectral imaging technology; firstly, preparing a salmon sample, and collecting a hyperspectral image by using a hyperspectral imaging collection system; measuring the umami intensity index of the sample by using a national standard method after collection; extracting the spectrum value of the region of interest in the center of the sample by adopting ENVI 4.5 software, and preprocessing the spectrum value; then extracting characteristic variables in the preprocessed spectral values by using different variable screening methods, and taking a sample formed by combining the extracted characteristic variables as a new sample; carrying out sample division on the new sample by adopting a sample set division method to obtain a re-divided sample set; and establishing a prediction model of the umami intensity index of the salmon by using the divided sample set, thereby realizing the distribution visualization of the umami intensity index. The method has the advantages of short required time, simple and convenient equipment operation and capability of accurately predicting the umami intensity index of the salmon.
Description
Technical Field
The invention belongs to the technical field of meat quality detection, and particularly relates to a method for rapidly representing the umami intensity of salmon based on a hyperspectral imaging technology.
Background
The salmon is rich in nutrition, is rich in amino acids, mineral substances, trace elements, a large amount of high-quality protein and omega-3 series unsaturated fatty acid, is called as 'gold in water', and is popular with consumers worldwide. The quality of the salmon can be kept to the maximum extent by the ice fresh storage, but the shelf life of the ice fresh salmon is short, and the ice fresh salmon is easy to rot and deteriorate. The frozen salmon can inhibit the activity of the enzyme and the growth of microorganisms at the freezing temperature, thereby having a long shelf life. However, the freezing causes the tissue structure of the salmon to be damaged and the juice to be lost, and the juice contains a large amount of flavor development substances such as nucleotides and amino acids and color development substances such as astaxanthin and carotenoid, so the frozen and thawed salmon has the situations of reduced tenderness, dull color, lack of taste and the like. The salmon with high quality is not only bright and attractive in appearance and color, but also smooth, tender, tasty, firm and full in taste. In addition, the salmon contains a large amount of flavor development substances such as nucleotides, free amino acids and the like, so that the salmon tastes delicious and sweet and is widely popular with consumers. The flavor developing substances have a flavor synergistic effect and jointly form the unique flavor characteristics of the salmon.
The delicate flavor is one of the most important characteristic flavors of aquatic products. By umami, it is meant mainly the taste of sodium glutamate. Nucleotide and free amino acid and other substances in organisms can present delicate flavor, generally speaking, the formation of the delicate flavor intensity of meat is a complex system, the delicate flavor synergistic effect exists between the nucleotide and the free amino acid, and when the nucleotide and the free amino acid exist at the same time, the promotion effect on the respective delicate flavor is generated, and the stronger delicate flavor intensity is presented. Therefore, detection of a single nucleotide or amino acid does not fully characterize the umami intensity of meat. Yamaguch, Japan, first proposed the monosodium glutamate Equivalents (EUC) method to characterize the umami taste intensity of foods. The monosodium glutamate equivalent method is adopted by multiple scholars to analyze and research the delicate flavor intensity of Chinese mitten crabs, lucid ganoderma and mushrooms respectively. However, the content of nucleotide and free amino acid in a sample is respectively detected by liquid chromatography in the prior determination of monosodium glutamate equivalent, and the monosodium glutamate equivalent value of the sample is obtained by calculation.
In recent years, the hyperspectral imaging technology is gradually applied to the field of food detection due to the advantages of rapid and lossless detection and image and spectrum information. The hyperspectral imaging technology is popular in the satellite remote sensing field at the end of the last century, can be used for rapidly acquiring the spectral information and the image information of a sample without damage, and has higher spectral and spatial resolution. Compared with the spectral technology, the method has the advantages of providing sample images and spatial information as well as the computer vision technology; compared with computer vision technology, it has the advantage that spectroscopic technology can provide chemical information of the sample. The hyperspectral imaging technology integrates the two technologies, and the integration of maps is realized. Cheng, Elmasry G, He and the like adopt a hyperspectral imaging technology to quickly predict indexes of DHA, fat, tenderness and the like of fish meat respectively, and obtain better effect. However, no method for rapidly characterizing the umami intensity of salmon based on a hyperspectral imaging technology exists at present.
Disclosure of Invention
The method aims to overcome the defects in the prior detection technology, such as complexity and time consumption of a liquid chromatography detection method. The method takes salmon as a research object, and adopts a hyperspectral imaging technology to carry out rapid prediction research on the umami intensity of the salmon. Firstly, a hyperspectral imaging acquisition system is utilized to acquire hyperspectral image data of a salmon sample, then a conventional liquid chromatography is adopted to measure the monosodium glutamate equivalent value of the umami intensity index of the salmon sample, the rapid prediction of the umami intensity of the salmon is realized by establishing the corresponding relation between the spectrum and the umami intensity index, and then the distribution visualization of the umami intensity of the salmon is completed by the corresponding relation between the spectrum value of each pixel point on the spectrum image and the umami intensity index.
The invention is realized by the following technical scheme, and the specific steps are as follows:
(1) preparing and numbering salmon samples, and specifically dividing the salmon samples into iced fresh salmon and freeze-thaw salmon;
(2) performing hyperspectral image acquisition on the salmon sample in the step (1) by using a hyperspectral imaging acquisition system to obtain a hyperspectral image of the salmon sample;
(3) measuring the umami intensity index of the salmon sample which has collected hyper-spectral image data in the step (2) by using a national standard method;
(4) extracting the spectral value of the region of interest in the center of the hyperspectral image of the salmon sample collected in the step (2) by adopting ENVI 4.5 software;
(5) preprocessing the spectral values extracted in the step (4) by adopting different preprocessing methods to obtain preprocessed spectral data;
(6) extracting characteristic variables in the spectrum data after pretreatment in the step (5) by using different variable screening methods, and taking a sample formed by combining the extracted characteristic variables as a new sample;
(7) carrying out sample division on the new sample generated after the characteristic variable is extracted in the step (6) by adopting a sample set division method to obtain a sample set after the sample set is divided again;
(8) establishing a salmon umami intensity index prediction model by adopting the sample set divided in the step (7); the specific operation method comprises the following steps: collecting spectral image information of a salmon sample by adopting a hyperspectral imaging system, then calculating EUC (EUC) by using nucleotide and free amino acid values measured by high performance liquid chromatography as a true value, and establishing a Partial Least Squares (PLS) prediction model by using spectral information subjected to pretreatment and variable screening as a variable;
(9) and (4) realizing the distribution visualization of the umami intensity index by using the prediction model established in the step (8).
Preferably, the specific operation process for preparing the salmon sample in the step (1) is as follows: peeling salmon, cleaning, taking middle-section fish blocks with a sterilized blade, cutting into fish slices with length of 3cm, width of 3cm and height of 2cm, wherein the individual mass is about 20 +/-5 g, and vacuum packaging; dividing the divided salmon samples into two groups, wherein the first group is named as group C, namely a fresh group, and the salmon samples in the group are analyzed on the same day; the second group was named FT group, freeze-thaw group; referring to the circulating freeze-thaw method of Ali and the like, the FT component is divided into an FT-1 group, an FT-2 group and an FT-3 group, namely a primary freeze-thaw group, a secondary freeze-thaw group and a tertiary freeze-thaw group, and the freeze storage time is 30 days. Wherein the FT-1 group is frozen at-30 ℃ for 30 days and then taken out for thawing analysis; taking the FT-2 group out of the freezing layer on day 15, thawing for 12 hours at 4 ℃, then continuing freezing, taking out the group on day 30 for thawing analysis, and taking out the group as a twice freezing-thawing cycle group; FT-3 was taken out on days 10 and 20 for one freeze-thaw cycle, and finally taken out on day 30 for analysis as a three freeze-thaw cycle group.
Preferably, the specific method for collecting the hyperspectral image of the salmon sample in the step (2) is as follows: in the process of acquiring spectral image information, the translation speed of a sample stage is 90mm/s, the stroke is 180mm, the exposure time of a camera is set to be 50ms, and the image resolution is 1628 multiplied by 775 pixels; since the spectral scan range is 618 wavelengths in the 431-962nm band, the size of the three-dimensional data block obtained finally is 1628 × 775 × 618.
Preferably, the index of the umami taste intensity of the salmon in the step (3) is monosodium glutamate equivalent content (EUC); the national standard method is to measure the nucleotide and free amino acid in the salmon sample by using a high performance liquid chromatography, and then calculate the EUC value by a formula.
Preferably, the specific extraction method of the spectral values of the region of interest in the step (4) is as follows: opening a hyperspectral image of a salmon sample by using ENVI 4.5, and then selecting a range of 200 pixels multiplied by 200 pixels near the center of the sample as a region of interest (ROI) by using a rectangular tool; after the ROI is selected, the spectral values of all pixel points in the ROI range are averaged to serve as the spectral value of the sample, and each sample corresponds to one spectral value.
Preferably, in step (5), the spectral data is preprocessed by different preprocessing methods: the preprocessing method is First derivative (1 st Der), Second derivative (Second derivative, 2nd Der), Multiple Scattering Correction (MSC), Standard Normal Variable Transformation (SNVT), Normalization (N), and Mean Centering (MC); the pretreatment can effectively solve the problems of poor spectral reproducibility, high noise and the like caused by the factors such as environment, instruments and the like in the acquisition process.
Preferably, the different variable screening methods in step (6) are a Competitive adaptive weighted reconstruction algorithm (CARS), a Successive Projection Algorithm (SPA), and a Competitive adaptive weighted reconstruction-Successive projection algorithm (CARS-SPA).
Preferably, the Sample division method in step (7) is a spectrum-physicochemical value co-occurrence distance method (SPXY); the method is provided on the basis of the Kennard-Stone method, and is different from the Kennard-Stone method in that the SPXY method can give consideration to both the variable matrix X and the index matrix Y to be measured in the selection of the correction set samples, so that the acquired correction set data can be ensured to be more representative.
Preferably, the specific operation steps of implementing the visualization of the distribution of umami intensity in step (9) are as follows: respectively adopting CARS, SPA and CARS-SPA methods to screen spectral variables, and establishing a PLS quantitative model of the EUC value by taking the screened spectral values as variables; after an optimal quantitative model of spectral information on the umami intensity index is determined, extracting spectral values corresponding to all pixel points in a hyperspectral image of a salmon sample, substituting the spectral values into the established optimal quantitative model to realize prediction of the index value on each pixel point of the hyperspectral image of the sample, and finally reconstructing according to the coordinate information of the pixels and the index content corresponding to the pixel information to obtain a distribution diagram of the umami intensity index EUC of the salmon on the plane.
Compared with the prior art, the invention has the following beneficial effects:
(1) compared with the existing method for representing the index of the umami intensity of the salmon, the hyperspectrum can not only quickly predict the umami intensity in the salmon sample, but also realize the distribution of the index EUC of the umami intensity of the salmon fillet through an optimized prediction model; for the national standard method that nucleotide is measured by using a high performance liquid chromatography, free amino acid is measured by using an amino acid analyzer, and then the umami intensity index is obtained by formula calculation, the method can obtain corresponding spectral image information within 1min, has short detection time, has no special requirements on detection personnel and operation conditions, and can clearly display the distribution condition of the umami intensity index of the salmon sample.
(2) The invention provides a rapid characterization of salmon umami intensity based on a hyperspectral imaging technology, on one hand, two technologies of spectroscopy and chemometrics can be realized, and a rapid and efficient prediction of salmon umami intensity is established; on the other hand, the three technologies of spectrum, image and chemometrics can be realized to establish visual and clear distribution of the umami intensity indexes in the salmon fillet; meanwhile, samples with different freshness and different freezing and thawing times are added for representation, and the method is more practical.
Drawings
FIG. 1 is a flow chart of sample preparation according to the present invention.
FIG. 2 is a schematic view of a hyperspectral imaging system.
FIG. 3 is a three-dimensional data block of a hyperspectral image.
Fig. 4 shows the variation of the equivalent values of monosodium glutamate in frozen fresh and frozen and thawed salmon.
FIG. 5 is a graph of the EUC values distribution (a) for chilled and frozen and thawed salmon; (b) FT-1; (c) FT-2; (d) FT-3.
Note: in the figure, C is a chilled group; the FT is a freeze-thaw group, which is specifically divided into three groups of FT-1, FT-2 and FT-3, wherein the FT-1 group is frozen at-30 ℃ for 30 days and then taken out for thawing analysis; taking the FT-2 group out of the freezing layer on day 15, thawing for 12 hours at 4 ℃, then continuing freezing, taking out the group on day 30 for thawing analysis, and taking out the group as a twice freezing-thawing cycle group; FT-3 was taken out on days 10 and 20 for one freeze-thaw cycle, and finally taken out on day 30 for analysis as a three freeze-thaw cycle group.
Detailed Description
The invention will be further illustrated, but not limited, by the following detailed description, taken in conjunction with the accompanying drawings.
Example 1:
(1) preparing salmon sample
As shown in figure 1, the salmon is peeled, cleaned, and then the middle section of the salmon is taken by a sterilized blade, cut into fillets with the length of 3cm, the width of 3cm and the height of 2cm, the individual mass is about 20 +/-5 g, and the fillets are vacuum-packed. The divided salmon samples were divided into two groups according to fig. 1, the first group was named group C, i.e., the chilled group, and the salmon samples were analyzed on the same day. The second group was named FT group, i.e. freeze-thaw group. Referring to the circulating freeze-thaw method of Ali and the like, the FT component is divided into an FT-1 group, an FT-2 group and an FT-3 group, namely a primary freeze-thaw group, a secondary freeze-thaw group and a tertiary freeze-thaw group, and the freeze storage time is 30 days. Wherein the FT-1 group is frozen at-30 ℃ for 30 days and then taken out for thawing analysis; taking the FT-2 group out of the freezing layer on day 15, thawing for 12 hours at 4 ℃, then continuing freezing, taking out the group on day 30 for thawing analysis, and taking out the group as a twice freezing-thawing cycle group; FT-3 was taken out on days 10 and 20 for one freeze-thaw cycle, and finally taken out on day 30 for analysis as a three freeze-thaw cycle group.
(2) The method comprises the following steps of (1) carrying out spectral image information acquisition on a salmon sample by using a hyperspectral imaging system:
the hyperspectral Imaging acquisition system used by the invention mainly comprises a hyperspectral camera (ImSpector V10E, Spectral Imaging Ltd, Oulu, Finland), a quartz halogen lamp (Fiber-LiteDC-950Illuminator, Dolan-Jenner Industries Inc, America), a precise automatic translation device (SC30021A, Zolix Instruments co.Ltd., China) and a computer (P4P800-X model, Asus computer co.Ltd., Taiwan, China) provided with an image acquisition card. The hyperspectral camera consists of a spectrometer and a CCD (charge coupled device) camera, the spectrometer is the most core part of the whole hyperspectral imaging acquisition system, and spectral information of each point on a sample surface in a 421-963 nm waveband can be acquired during testing. The main composition diagram of the system hardware part is shown in fig. 2. Before collecting the spectrum image of the sample, the hyperspectral image collection system needs to be preheated for half an hour to adjust the hyperspectral image collection system to the optimal working state. After the instrument is preheated, placing the salmon sample on a sample table, and setting system parameters: the translation speed of the sample stage is 90 mm/s; the stroke is 180 mm; the camera exposure time is set to 50 ms; the image resolution was 1628 × 775 pixels. Since the spectral scan range is 618 wavelengths in the 431-962nm band, the size of the three-dimensional data block obtained finally is 1628 × 775 × 618, as shown in fig. 3.
Dark current and uneven illumination factors may exist in the process of acquiring the spectral image, so that the acquired spectral image presents larger noise at the wavelength with weaker illumination. The interference of such noise can be well eliminated by black and white correction. The black and white correction formula is:
in the formula IλThe reflection intensity of the original spectral image I at the wavelength lambda is taken as the reflection intensity of the original spectral image I; b isλCorrecting the reflection intensity of the spectral image at the wavelength λ for the blackboard; wλCorrecting the reflection intensity of the spectral image at the wavelength lambda for the whiteboard; rλThe reflection intensity at the wavelength λ of the resulting spectral image after correction.
(3) And (3) measuring the umami intensity index of the salmon sample which has collected hyper-spectral image data in the step by using a national standard method. The method adopts a monosodium glutamate Equivalent methods (EUC) to evaluate the strength of the synergistic effect of the fresh taste and the fresh taste of the freeze-thaw salmon. The gourmet powder equivalent method was first proposed by Yamaguchi, a japanese scholar, and is commonly used to shape the umami intensity of foods. The monosodium glutamate equivalent refers to the umami intensity generated by the umami synergistic effect between the flavor-producing nucleotide and the flavor-producing free amino acid in the sample, which is equivalent to the umami intensity of monosodium glutamate with what concentration. At present, most of the scholars at home and abroad analyze the freshness strength of substances by adopting a monosodium glutamate equivalent method. The calculation formula is as follows:
EUC＝∑aibi+1218∑aibi*∑ajbj
in the formula: a isi-concentration of Aspartic acid (Asp) or Glutamic acid (glumic acid, Glu) in umami amino acids in g/100 g; bi-relative freshness coefficients of Asp and Glu relative to sodium glutamate (MSG) (wherein Asp is 0.077 and Glu is 1); a isjAmong flavour-producing nucleotides, Inosine 5 '-monophosphosphate (IMP), guanylic acid (Guanosine5' -monophosphosphate, GMP) and Adenosine monophosphate (Adenosine)5' -monoposphate, AMP) in g/100 g; bj-relative freshness coefficients of the flavour nucleotides IMP, GMP and AMP relative to IMP (where IMP is 1, GMP is 2.3 and AMP is 0.18); 1218 — synergy constant. Determination of nucleotide associations:
putting the fillets into a meat grinder for grinding, then weighing 5 +/-0.05 g of ground salmon sample, putting the salmon sample into a 50mL centrifuge tube, pouring 10mL of perchloric acid solution with the mass fraction of 10%, homogenizing at a high speed for 1min, then carrying out refrigerated centrifugation (4 ℃,9500r/min, 10min), filtering centrifuged supernatant, adding 5mL of perchloric acid solution with the mass fraction of 5% into the precipitate for washing, continuing to centrifuge, combining the supernatant with the previous supernatant, and repeating twice in total. The combined supernatants were adjusted to a pH of approximately 6.0 with 10mol/L sodium hydroxide solution and then to a pH of 6.5 with 1mol/L sodium hydroxide solution. After the pH adjustment is finished, the supernatant is poured into a volumetric flask and is subjected to constant volume to 50mL by using ultrapure water, and the supernatant is sealed and stored at 4 ℃ for detection after being filtered by a 0.22 mu m microporous membrane.
A chromatographic column: waters SunfireTMA C18 chromatography column; column temperature: 30 ℃; DAD detection wavelength: 254 nm; sample introduction amount: 10 mu L of the solution; flow rate of mobile phase: 0.8 mL/min; mobile phase A: 1.36g of monopotassium phosphate and 0.49g of tetrabutylammonium hydrogen sulfate are metered to 1L, and the pH is adjusted to 4.6 by 0.1mol/L of dipotassium hydrogen phosphate; mobile phase B: methanol; the proportion of mobile phase is A: b is 99:1, isocratic elution; before sample injection test, the chromatographic column is washed by 10% methanol solution for 20min, then the chromatographic column is balanced by mobile phase, and sample injection analysis is carried out after the base line is stable.
Determination of free amino acids:
mincing a sample by using a meat mincer, weighing 1g of the minced sample, putting the minced sample into a 50mL centrifuge tube, adding 15mL of trichloroacetic acid solution with the mass fraction of 15%, homogenizing at a high speed for 1min, standing at 4 ℃ for 2h, then carrying out refrigerated centrifugation (4 ℃,9500r/min, 15min), filtering the centrifuged supernatant, taking 5mL of the filtered supernatant, adjusting the pH to 2.0 by using a 3mol/L NaOH solution, pouring the supernatant into a volumetric flask after the pH adjustment, fixing the volume to 10mL by using ultrapure water, and sealing and storing the supernatant at 4 ℃ for detection after passing through a 0.22 mu m microporous filter membrane.
Conditions of the amino acid autoanalyzer: a separation column (4.6mm × 60mm), wherein the separation resin is cation exchange resin; the temperature of the separation column is 57 ℃; the detection wavelength is 570nm (the proline is 440 nm); the flow rate of the buffer solution is 0.40 mL/min; the reaction liquid is ninhydrin reagent; the flow rate of the reaction solution is 0.35 mL/min; the temperature of a reaction unit is 135 ℃; the amount of the sample was 20. mu.L.
The national standard method determines that the iced fresh salmon has higher monosodium glutamate equivalent value compared with the freeze-thaw salmon, the EUC value of the iced fresh salmon is 11.69g MSG/100g, namely, the freshness intensity of 100g iced fresh salmon (wet base) is equal to the freshness contained in 11.69g monosodium glutamate. Due to the loss of juice, the EUC value of the freeze-thaw salmon is reduced, and the EUC value is in a descending trend along with the increase of the freeze-thaw times; specific results can be seen in fig. 4.
(4) And extracting the spectral value of the interested area in the center of the sample by adopting ENVI 4.5 software. According to the method, ENVI 4.5 software is adopted to select a range of 200 pixels multiplied by 200 pixels near the center of a sample as a Region of interest (ROI), and the spectral values of all pixel points in the ROI range are averaged to be used as the spectral value of the sample.
(5) And (4) preprocessing the spectrum values in the step (4) by adopting different preprocessing methods. The invention selects 6 preprocessing methods, namely First derivative (1 st Der), Second derivative (Second derivative, 2nd Der), Multiple Scattering Correction (MSC), Standard Normal Variable Transformation (SNVT), Normalization (N) and Mean Centering (MC). Wherein the first derivative may reduce the shifting phenomenon of the baseline; the second derivative may reduce the rotation of the baseline; the multivariate scattering correction method can reduce the spectral shift and the scattering phenomenon; the standard normal variable transformation method can greatly reduce interference information in the spectrum, and normalization can make the distribution of variables more balanced; the mean centering can enable the variables to be distributed around the zero point, and the subsequent operation is simplified well. The quality of the preprocessing effect is determined according to the prediction performance of a model established at the later stage.
(6) Extracting characteristic variables in the spectrum data preprocessed in the step (5) by using different variable screening methods; the method selects 3 variable screening methods, namely CARS, SPA and CARS-SPA, establishes a PLS quantitative model of the EUC value by taking the screened spectral values as variables, and compares the prediction effects of the models.
(7) Carrying out sample division on the new sample generated after the characteristic variable is extracted in the step (6) by adopting a sample set division method to obtain a sample set after the sample set is divided again; the method adopts an SPXY method to divide a sample set, and the division result is shown in Table 1; as can be seen from the table, the quality indexes of the correction set and the prediction set divided based on the SPXY method are uniform, and each quality index value of the prediction set can be contained in the quality index value range of the correction set, which is beneficial to improving the precision of the quantitative model.
TABLE 1 correction set and prediction set partitioning results
(8) And establishing a salmon delicate flavor intensity index prediction model. The method adopts a PLS model to establish the corresponding relation between the spectral information of the salmon and the umami intensity index ECU. The prediction capability of the model adopts a correlation coefficient (R) of correction setc) Prediction coefficient of prediction, Rp) The Root Mean Square Error (RMSEC) of the corrected set and the Root Mean Square Error (RMSEP) of the predicted set are compared. RcAnd RpThe closer to 1 and the smaller RMSEC and RMSEP, the better the prediction performance of the model, and firstly, the method establishes a full spectrum PLS model after preprocessing. The prediction effect of the PLS model established by the all variables under each preprocessing condition is shown in Table 2, when the multivariate scatter correction preprocessing method is adopted, the prediction effect of the PLS model on the EUC is better, and the R at the momentcAnd RpThe values are 0.8005 and 0.6668, respectively, and RMSECV and RMSEP are 1.31gMSG/100g and 1.64gMSG/100g, respectively, for optimal pretreatment of EUC predictions based on spectral variables.
To improve the prediction effect and decrease of the modelAnd the method also adopts CARS, SPA and CARS-SPA methods to screen the spectral variables respectively, establishes a PLS quantitative model of the EUC value by taking the screened spectral values as variables, and compares the prediction effects of the models. The results of the EUC value prediction model established after wavelength-based screening are shown in table 3. When the CARS method is used for screening the wavelengths, the sampling times are set to be 100, and the variable number when the RMSECV value is minimum is used as a final screening variable. Finally, 36 characteristic wavelengths are screened, 36 wavelengths of 120 samples are combined into a new variable matrix of 120 x 36, a PLS prediction model of salmon EUC values is established, and R of the PLS to EUC value prediction model established through the variables screened by CARS is obtainedcAnd Rp0.8571 and 0.8093, RMSECV and RMSEP are respectively 0.88g MSG/100g and 1.13g MSG/100g, and the prediction result is obviously improved compared with the effect of a PLS model based on full spectrum variables. When the SPA method is used for screening the spectral wavelengths, the number range of the extracted characteristic wavelengths is set to be 2-25, and 10 characteristic wavelengths are screened in the final range of 421-963 nm by the SPA method. The PLS model was established using 10 wavelengths of the sample as variables and their corresponding EUC values. R of PLS to EUC value prediction modelcAnd Rp0.7311 and 0.6756, respectively, and RMSECV and RMSEP 1.41g and 1.56g of MSG/100g, respectively. The prediction result is close to the PLS model prediction result based on the full spectrum variable, the improvement is not obvious, but the variable is simplified, and the operation complexity of the model is reduced. Carrying out secondary screening on the wavelengths screened by the CARS by utilizing an SPA method, screening 10 wavelengths in the band range of 421-963 nm by the CARS-SPA method, establishing a PLS (partial least squares) prediction model for the EUC (EUC) value by taking the 10 wavelengths as variables, and establishing R of the modelcAnd Rp0.6486 and 0.6127, RMSECV and RMSEP 1.71gMSG/100g and 1.86gMSG/100g, respectively, the prediction results were reduced compared to the PLS model prediction based on full spectrum variables. The results of the four prediction models for the EUC values are compared to find that the PLS model established by taking the spectrum after CARS screening as a variable has the best prediction effect on the EUC values, the correction set of the PLS model has higher correlation coefficient and lower RMSECV value, and the model effect of the prediction set is obviously higher than that of the other three modelsCARS-PLS can be used as an optimal quantitative model for prediction of EUC values by spectral variables.
TABLE 2 EUC value index prediction based on full spectrum variables
TABLE 3 EUC value prediction model results based on wavelength screening
(9) The distribution of the umami intensity index is visualized. Extracting a spectral value corresponding to each pixel point in the hyperspectral image of the salmon sample, substituting the spectral value into the established CARS-PLS to realize the prediction of the umami intensity index EUC value on each pixel point of the hyperspectral image of the salmon sample, and finally reconstructing according to the coordinate information of the pixel and the index content corresponding to the pixel to obtain a distribution graph of the umami intensity index EUC value of the salmon on a plane, as shown in fig. 5 (a-d). In the EUC content profile, the lighter the colour the higher the EUC content, the darker the colour the lower the EUC content. The iced fresh salmon has a higher EUC value and stronger freshness presenting capability; the EUC value of the freeze-thaw salmon is lower because the taste substances are reduced along with the loss of juice. The result corresponding to fig. 4 can be obtained, in the EUC content distribution diagram of the iced fresh salmon, the light-colored pixel points occupy most of the image, indicating that it has a higher EUC value, while in the EUC value distribution diagram of the freeze-thaw salmon, the light-colored pixel points are reduced, the dark-colored pixel points are increased, indicating that it has a lower EUC value.
Description of the drawings: the above embodiments are only used to illustrate the present invention and do not limit the technical solutions described in the present invention; thus, while the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted; all such modifications and variations are intended to be included herein within the scope of this disclosure and the present invention and protected by the following claims.
Claims (8)
1. A method for rapidly representing the umami intensity of salmon based on a hyperspectral imaging technology is characterized by comprising the following steps:
(1) preparing and numbering salmon samples, and specifically dividing the salmon samples into iced fresh salmon and freeze-thaw salmon;
(2) performing hyperspectral image acquisition on the salmon sample in the step (1) by using a hyperspectral imaging acquisition system to obtain a hyperspectral image of the salmon sample;
(3) measuring the umami intensity index of the salmon sample which has collected hyper-spectral image data in the step (2) by using a national standard method;
(4) extracting the spectral value of the region of interest in the center of the hyperspectral image of the salmon sample collected in the step (2) by adopting ENVI 4.5 software;
(5) preprocessing the spectral values extracted in the step (4) by adopting different preprocessing methods to obtain preprocessed spectral data;
(6) extracting characteristic variables in the spectrum data after pretreatment in the step (5) by using different variable screening methods, and taking a sample formed by combining the extracted characteristic variables as a new sample;
(7) carrying out sample division on the new sample generated after the characteristic variable is extracted in the step (6) by adopting a sample set division method to obtain a sample set after the sample set is divided again;
(8) establishing a salmon umami intensity index prediction model by adopting the sample set divided in the step (7); the specific operation method comprises the following steps: collecting spectral image information of a salmon sample by adopting a hyperspectral imaging system, then calculating EUC (EUC) by using nucleotide and free amino acid values measured by high performance liquid chromatography as a true value, and establishing a partial least square prediction model by using spectral information subjected to pretreatment and variable screening as a variable;
(9) and (4) realizing the distribution visualization of the umami intensity index by using the prediction model established in the step (8).
2. The method for rapidly characterizing the umami taste intensity of the salmon based on the hyperspectral imaging technology according to claim 1, wherein the specific method for performing hyperspectral image acquisition on the salmon sample in the step (2) is as follows: in the process of acquiring spectral image information, the translation speed of the sample stage is 90mm/s, the stroke is 180mm, the exposure time of the camera is set to be 50ms, and the image resolution is 1628 multiplied by 775 pixels.
3. The method for rapidly characterizing the umami intensity of the salmon based on the hyperspectral imaging technology according to claim 1, characterized in that the umami intensity index of the salmon in the step (3) is a monosodium glutamate equivalent method, which is marked as EUC; the national standard method is to determine the nucleotide and free amino acid in the salmon sample by using a high performance liquid chromatography, and then further calculate to obtain the EUC value.
4. The method for rapidly characterizing the umami intensity of salmon based on the hyperspectral imaging technology according to claim 1, wherein in the step (4), the specific method for extracting the spectral value of the region of interest in the center of the sample by the ENVI 4.5 software comprises the following steps: opening a hyperspectral image of a salmon sample by using ENVI 4.5, and then selecting a range of 200 pixels multiplied by 200 pixels near the center of the sample as a region of interest (ROI) by using a rectangular tool; after the ROI is selected, the spectral values of all pixel points in the ROI range are averaged to serve as the spectral value of the sample, and each sample corresponds to one spectral value.
5. The method for rapidly characterizing the umami taste intensity of salmon based on the hyperspectral imaging technology according to claim 1, wherein in the step (5), the preprocessing method comprises first derivative, second derivative, multivariate scattering correction, standard normal variable transformation, normalization and mean centralization.
6. The method for rapidly characterizing the umami taste intensity of salmon based on the hyperspectral imaging technology according to claim 1, wherein in the step (6), the different variable screening methods are specifically as follows: competitive adaptive re-weighting algorithms, continuous projection algorithms, and competitive adaptive re-weighting-continuous projection algorithms.
7. The method for rapidly characterizing the umami intensity of salmon based on the hyperspectral imaging technology according to claim 1, wherein in the step (7), the specific method for dividing the sample set is as follows: spectrum-physical and chemical value symbiosis distance method.
8. The method for rapidly characterizing the umami intensity of salmon based on the hyperspectral imaging technology according to claim 1, wherein in the step (9), the specific operation steps for realizing the visualization of the distribution of the umami intensity are as follows: respectively adopting CARS, SPA and CARS-SPA methods to screen spectral variables, and establishing a PLS quantitative model of the EUC value by taking the screened spectral values as variables; after an optimal quantitative model of spectral information on the umami intensity index is determined, extracting spectral values corresponding to all pixel points in a hyperspectral image of a salmon sample, substituting the spectral values into the established optimal quantitative model to realize prediction of the index value on each pixel point of the hyperspectral image of the sample, and finally reconstructing according to the coordinate information of the pixels and the index content corresponding to the pixel information to obtain a distribution map of the umami intensity index of the salmon on a plane.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113588571A (en) * | 2021-09-29 | 2021-11-02 | 广东省农业科学院动物科学研究所 | Hyperspectral imaging-based method and system for identifying fishy smell of aquatic product |
| CN117092041A (en) * | 2023-08-22 | 2023-11-21 | 中国水产科学研究院 | Rapid detection method for muscle quality of living carp based on hyperspectral imaging technology |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104655761A (en) * | 2015-02-28 | 2015-05-27 | 华南理工大学 | Method for measuring fish freshness index value K in online way based on multi-spectral imaging |
| CN106596416A (en) * | 2016-11-25 | 2017-04-26 | 华中农业大学 | Chilled fresh meat quality non-destructive testing method based on hyperspectral imaging technology |
| CN107271375A (en) * | 2017-07-21 | 2017-10-20 | 石河子大学 | A kind of high spectral image detecting method of quality of mutton index |
| CN109765197A (en) * | 2019-02-20 | 2019-05-17 | 江苏大学 | A kind of method for quick identification of chilled atlantic salmon and freeze thawing atlantic salmon |
| CN110646546A (en) * | 2019-10-10 | 2020-01-03 | 中国农业科学院农产品加工研究所 | Meat product flavor compound profile analysis method |
| CN111289707A (en) * | 2020-03-11 | 2020-06-16 | 中国海洋大学 | Method for characterizing characteristic taste of steamed fish sample and application |
-
2020
- 2020-09-30 CN CN202011064908.0A patent/CN112285031A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104655761A (en) * | 2015-02-28 | 2015-05-27 | 华南理工大学 | Method for measuring fish freshness index value K in online way based on multi-spectral imaging |
| CN106596416A (en) * | 2016-11-25 | 2017-04-26 | 华中农业大学 | Chilled fresh meat quality non-destructive testing method based on hyperspectral imaging technology |
| CN107271375A (en) * | 2017-07-21 | 2017-10-20 | 石河子大学 | A kind of high spectral image detecting method of quality of mutton index |
| CN109765197A (en) * | 2019-02-20 | 2019-05-17 | 江苏大学 | A kind of method for quick identification of chilled atlantic salmon and freeze thawing atlantic salmon |
| CN110646546A (en) * | 2019-10-10 | 2020-01-03 | 中国农业科学院农产品加工研究所 | Meat product flavor compound profile analysis method |
| CN111289707A (en) * | 2020-03-11 | 2020-06-16 | 中国海洋大学 | Method for characterizing characteristic taste of steamed fish sample and application |
Non-Patent Citations (3)
| Title |
|---|
| 孙宗保 等: "高光谱成像的牛肉丸掺假检测", 《光谱学于光谱分析》 * |
| 石吉勇 等: "基于高光谱技术的乳杆菌快速鉴别", 《中国食品学报》 * |
| 章海亮 等: "基于高光谱成像技术的三文鱼肉脂肪含量可视化研究", 《江苏农业科学》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113588571A (en) * | 2021-09-29 | 2021-11-02 | 广东省农业科学院动物科学研究所 | Hyperspectral imaging-based method and system for identifying fishy smell of aquatic product |
| CN113588571B (en) * | 2021-09-29 | 2021-12-03 | 广东省农业科学院动物科学研究所 | Hyperspectral imaging-based method and system for identifying fishy smell of aquatic product |
| CN117092041A (en) * | 2023-08-22 | 2023-11-21 | 中国水产科学研究院 | Rapid detection method for muscle quality of living carp based on hyperspectral imaging technology |
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