CN104964962B - Quick determination method based on delicate flavour material inosinicacid in the fresh flesh of fish of Raman spectrum - Google Patents
Quick determination method based on delicate flavour material inosinicacid in the fresh flesh of fish of Raman spectrum Download PDFInfo
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- 235000013902 inosinic acid Nutrition 0.000 title claims abstract description 108
- GRSZFWQUAKGDAV-UHFFFAOYSA-N Inosinic acid Natural products OC1C(O)C(COP(O)(O)=O)OC1N1C(NC=NC2=O)=C2N=C1 GRSZFWQUAKGDAV-UHFFFAOYSA-N 0.000 title claims abstract description 92
- AUHDWARTFSKSAC-HEIFUQTGSA-N (2S,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)-2-(6-oxo-1H-purin-9-yl)oxolane-2-carboxylic acid Chemical compound [C@]1([C@H](O)[C@H](O)[C@@H](CO)O1)(N1C=NC=2C(O)=NC=NC12)C(=O)O AUHDWARTFSKSAC-HEIFUQTGSA-N 0.000 title claims abstract description 91
- 229940028843 inosinic acid Drugs 0.000 title claims abstract description 91
- 239000004245 inosinic acid Substances 0.000 title claims abstract description 91
- 238000001237 Raman spectrum Methods 0.000 title claims abstract description 77
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- 230000003595 spectral effect Effects 0.000 claims description 12
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 11
- 238000004128 high performance liquid chromatography Methods 0.000 claims description 10
- 238000005070 sampling Methods 0.000 claims description 9
- 238000009499 grossing Methods 0.000 claims description 7
- 239000011521 glass Substances 0.000 claims description 6
- 235000019583 umami taste Nutrition 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 abstract description 8
- 238000011156 evaluation Methods 0.000 abstract description 4
- 229930010555 Inosine Natural products 0.000 abstract description 3
- UGQMRVRMYYASKQ-KQYNXXCUSA-N Inosine Chemical compound O[C@@H]1[C@H](O)[C@@H](CO)O[C@H]1N1C2=NC=NC(O)=C2N=C1 UGQMRVRMYYASKQ-KQYNXXCUSA-N 0.000 abstract description 3
- 229960003786 inosine Drugs 0.000 abstract description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 239000002253 acid Substances 0.000 abstract 2
- 238000001344 confocal Raman microscopy Methods 0.000 abstract 1
- 235000013622 meat product Nutrition 0.000 abstract 1
- 235000013372 meat Nutrition 0.000 description 26
- GRSZFWQUAKGDAV-KQYNXXCUSA-N IMP Chemical compound O[C@@H]1[C@H](O)[C@@H](COP(O)(O)=O)O[C@H]1N1C(NC=NC2=O)=C2N=C1 GRSZFWQUAKGDAV-KQYNXXCUSA-N 0.000 description 16
- 239000000047 product Substances 0.000 description 12
- 241000252230 Ctenopharyngodon idella Species 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 5
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- 238000004809 thin layer chromatography Methods 0.000 description 2
- UDMBCSSLTHHNCD-UHFFFAOYSA-N Coenzym Q(11) Natural products C1=NC=2C(N)=NC=NC=2N1C1OC(COP(O)(O)=O)C(O)C1O UDMBCSSLTHHNCD-UHFFFAOYSA-N 0.000 description 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
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- LNQVTSROQXJCDD-UHFFFAOYSA-N adenosine monophosphate Natural products C1=NC=2C(N)=NC=NC=2N1C1OC(CO)C(OP(O)(O)=O)C1O LNQVTSROQXJCDD-UHFFFAOYSA-N 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
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- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The invention discloses a kind of quick determination method of delicate flavour material inosinicacid in fresh flesh of fish based on Raman spectrum, it is related to aquatic products quality and safety detection technology field.This method is：1. inosinicacid standard items Raman spectrum is determined；2. fresh flesh of fish sample Raman spectrum is obtained；3. chemical determination inosine acid content is utilized；4. Raman spectrum correction is carried out；5. preferred inosinicacid characteristic peak；6. Characteristic Raman peak area is calculated；7. the detection model of inosine acid content is set up.The present invention searches out a kind of effective, simple to operate, cost and time-consuming less fresh flesh of fish delicate flavour material inosinicacid quick determination method by quick detection of the confocal micro Raman spectrum technology to delicate flavour material inosinicacid in the fresh flesh of fish；The detection method is simple, safe and efficient, and being adapted to industrialization production needs and requirement of the consumer to product quality, meets the evaluation needs of the fresh processed fish meat products flavor quality in actual production process.
Description
Technical Field
The invention relates to the technical field of aquatic product quality and safety detection, in particular to a method for quickly detecting inosinic acid serving as a delicious substance in fresh fish meat based on a Raman spectrum.
Background
The aquatic products are widely popular with consumers due to the unique delicate flavor, and a plurality of scholars research the delicate flavor substances in the aquatic products. Research shows that the good flavor of aquatic products is not the result of single substance action, but the result of subtly balancing a plurality of different components in quantity, wherein the two types of substances which have the greatest contribution to the delicate flavor are inosinic acid and amino acid. Inosinic acid, also known as Inosine Monophosphate (IMP), is an intermediate product in the process of ATP metabolism, is widely distributed in the body, has biological activity in the living body, is involved in the regulation of biological energy metabolism, and is in dynamic balance. A large amount of inosinic acid is generated after the fish is dead, the concentration of the inosinic acid gradually rises during the slaughtering, and the concentration of the inosinic acid gradually falls after a certain time reaches the peak, so that the flavor of the fish gradually becomes unacceptable. Therefore, inosinic acid not only can enhance the freshness of fish meat as a flavor substance, but also has received wide attention to the freshness and quality of fish meat evaluated by nucleotide degradation products, and the determination of the accumulation amount of IMP has been proved to be an index for measuring the freshness of aquatic products.
The traditional fish inosinic acid content detection method mainly comprises a capillary electrophoresis method, a thin layer chromatography method and a High Performance Liquid Chromatography (HPLC). The capillary electrophoresis method has the problems of low separation degree and interference of similar substances such as inosine, adenosine monophosphate and the like; the thin layer chromatography has complicated operation steps and long time consumption. Compared with the 2 methods, the HPLC method is relatively simple and accurate in determination, still consumes time and labor, is difficult to realize online detection, needs to utilize a high-corrosivity chemical reagent to carry out sample pretreatment, causes environmental pollution, and cannot meet the industrial development requirements of fish meat quality nondestructive detection and real-time grading.
The spectrum detection technology avoids the defects of destructiveness, chemical reagent pollution, time and labor consumption and the like of the traditional chemical method, is widely applied to quality detection and safety evaluation of the fresh meat, and mainly reflects on the aspects of nutrient component analysis, food quality detection and grading, variety identification and judgment, safety evaluation and the like of the fresh meat. The problems that signals are difficult to obtain, other substances with IMP similar groups interfere with IMP characteristic spectra and the like mainly exist in the fish IMP umami substance spectrum detection research process, and quantitative research is difficult to realize. In recent years, with the development of raman spectroscopy, instrumentation, chemometrics and laser technology, various raman spectrum enhancement means have been used to broaden the application range of raman spectroscopy technology, and the application of raman spectrum technology in meat quality inspection is gradually increased. The Raman spectrum has the characteristics of rapidness, no damage, environmental protection, small interference of water or alcohol solution and the like, and is particularly beneficial to the detection of biological sample tissues. The comparative Raman spectrum of the fish sample and the inosinic acid standard shows that the fish sample contains abundant information and is relatively obviously matched with a peak. Therefore, the technology has feasibility for rapid detection and analysis of fresh fish meat IMP, establishes a prediction model for inosinic acid by using the optimal spectral characteristic variables, has high calculation speed and high accuracy, and can meet the requirement for rapid detection of fresh fish meat inosinic acid.
Disclosure of Invention
The invention aims to solve the problems that the flavor quality of fresh fish meat is difficult to evaluate, the content of inosinic acid is difficult to rapidly detect in a large scale in the detection process based on the traditional chemical detection method and the like, and provides a rapid detection method of the umami substance inosinic acid in the fresh fish meat based on Raman spectrum.
The purpose of the invention is realized as follows:
by optimizing the Raman spectrum, comparing the Raman spectrum of the inosinic acid standard substance with that of the fresh fish meat sample, optimizing the characteristic Raman peak, establishing a prediction model for the fresh fish meat inosinic acid, and improving the detection speed and efficiency.
Specifically, the method comprises the following steps:
measuring Raman spectrum of inosinic acid standard product
Detecting a Raman spectrogram of the inosinic acid standard substance by using a microscopic confocal Raman spectrometer, setting the laser wavelength of 633nm, the laser power of 17mW, the exposure time of 5s and the average value of the scanning times of 3 times, and collecting to obtain a Raman spectrum of the inosinic acid standard substance, wherein the Raman spectrum range is 1000-4000 cm--1；
② obtaining the Raman spectrum of the fresh fish sample
After a fresh live fish is slaughtered, cutting a fish slice sample with the thickness of 1mm according to the fish texture by using a slicer, placing the fish slice sample on a glass slide, placing the fish slice sample on a microscope Raman spectrometer stage, selecting automatic focal length adjustment to adjust a microscope Raman laser probe, enabling the sample to be located under the probe, collecting sample measuring points at multiple points, setting the laser output power to be 17mW, the laser wavelength to be 633nm, the exposure time to be 5s, and the average value of scanning times to be 3 times, and obtaining a fish Raman spectrum;
measuring inosinic acid content by chemical method
Measuring the content of inosinic acid by high performance liquid chromatography according to the national standard GB/T19676-2005;
fourthly, carrying out Raman spectrum correction
After a cosmic ray irrelevant spectral peak and a Baseline Baseline in a Raman spectral line of a sample are removed from Reinshaw software, spectra acquired at different sampling points of the same slice sample are corrected by taking a characteristic spectrum of water as an internal standard, and the characteristic Raman peaks of the water are also equal because the water content of sampling micro-regions of the same slice is equal, so that the spectral difference caused by unstable light intensity, manual focusing difference and the like is corrected;
fifthly, inosinic acid characteristic peak is preferably selected
Comparing and analyzing the Raman spectra of the IMP standard product and the fish sample, analyzing the characteristic peak positions of the Raman spectra of the IMP in the fish Raman spectrum, and comparing to obtain 1322.31, 2620.83, 2645.46 and 2703.20, 2808.12, 2935.54, 3070.01, 3393.62, 3462.61, 3591.40, 3699.87 and 3750.74cm-1The Raman peaks which are matched obviously are compared nearby;
calculating characteristic Raman peak area
After the corrected Raman spectrum is subjected to Savitzky-Golay smoothing processing in Matlab software, the area of a corresponding curve section in a Raman spectrogram is obtained by integrating the optimized Raman characteristic peak, namely the characteristic Raman peak area;
seventhly, establishing a detection model of inosinic acid content
And establishing an inosinic acid quantitative detection model for the inosinic acid content by utilizing the characteristic Raman peak area, and evaluating the accuracy of the detection model by utilizing the correlation coefficient of the predicted value and the true value and the root mean square error of the correction set.
The invention has the following advantages and positive effects:
the method is characterized in that the method is an effective, simple to operate, low in cost and time consumption rapid detection method for the inosinic acid in the fresh taste substance of the fresh fish meat by the rapid detection of the inosinic acid in the fresh taste substance of the fresh fish meat through the micro-Raman spectrum technology;
the detection method is simple, safe and efficient, can meet the industrial production requirements and the requirements of consumers on the product quality, and meets the evaluation requirements of flavor quality and freshness of fish products in the actual production and processing processes.
Drawings
FIG. 1 is a flow chart of the method;
FIG. 2 is a comparison graph of Raman spectrum of inosinic acid standard substance and average Raman spectrum of fresh fish meat (wave number range: 1000-4000 cm)-1）；
FIG. 3 is a comparison graph of Raman spectrum of inosinic acid standard substance and average Raman spectrum of fresh fish meat (wave number range: 2600-4000 cm)-1）；
FIG. 4 is a comparison graph of the Raman spectrum of the inosinic acid standard substance and the average Raman spectrum of fresh fish meat (wave number range: 1000-2000 cm)-1）；
FIG. 5 is a schematic diagram showing the calculation of the characteristic Raman peak area of fresh fish meat (wave number range: 2600-4000 cm)-1）；
FIG. 6 is a graph showing the calculation of the characteristic Raman peak area of fresh fish meat (wave number range: 1000-2000 cm)-1）。
Detailed Description
The following detailed description is made with reference to the accompanying drawings and examples:
method and device
Referring to fig. 1, the method comprises the following steps:
measuring a Raman spectrum 1 of an inosinic acid standard product;
acquiring a Raman spectrum 2 of the fresh fish sample;
measuring inosinic acid content by a chemical method 3;
fourthly, correcting the Raman spectrum 4;
optimally selecting inosinic acid characteristic peak 5;
sixthly, calculating the characteristic Raman peak area 6;
and creating an inosinic acid content detection model 7.
Second, example
1. Example 1: detection of inosinic acid content of fresh grass carp on the same day in market
A. Measuring Raman spectrum of inosinic acid standard substance
The model InVia of Renisshaw company is adoptedDetecting a Raman spectrogram of the inosinic acid standard substance by using a microscopic confocal Raman spectrometer, wherein the Raman spectrogram has the wavelength of laser light of 633nm, the laser power of 17mW, the exposure time of 5s and the average value of scanning times of 3 times, and acquiring a Raman spectrum of the inosinic acid standard substance, wherein the Raman spectrum range is 1000-4000cm-1；
B. Obtaining a Raman spectrum of a fresh fish sample
After a fresh live fish is slaughtered, a slicer is adopted to cut a fish slice sample with the thickness of 1mm according to the fish texture, the sample is placed on a glass slide and placed on an objective table of a micro-Raman spectrometer, a micro-Raman laser probe capable of automatically adjusting the focal length is selected to adjust, the sample is positioned under the probe, and sample measuring points are collected at multiple points. The laser output power is 17mW, the laser wavelength is 633nm, the exposure time is set to be 5s, the average value of the scanning times is 3 times, and the fish Raman spectrum is obtained;
C. measuring inosinic acid content by high performance liquid chromatography
Measuring inosinic acid content at 0.92-1.58mg/g by high performance liquid chromatography according to national standard GB/T19676-2005;
D. performing Raman spectrum correction
After irrelevant spectral peaks such as cosmic rays and the like in a sample Raman spectral line and Baseline base line correction are removed in Reinshaw software, spectra acquired at different sampling points of the same slice sample are corrected by taking a characteristic spectrum of water as an internal standard, the water content of sampling micro-areas of the same slice is equal, and spectrum differences caused by uneven sample surface, artificial focusing difference and the like are corrected;
E. the preferred characteristic peak of inosinic acid
And (4) comparing and analyzing the Raman spectra of the IMP standard product and the fish sample, and analyzing the characteristic peak position of the Raman spectrum of the IMP in the fish Raman spectrum. Comparison of 1322.31, 2620.83, 2645.46, 2703.20, 2808.12, 2935.54, 3070.01, 3393.62, 3462.61, 3591.40, 3699.87 and 3750.74cm can be found-1The obviously matched Raman peak is taken as a characteristic peak;
F. calculating the characteristic Raman peak area
After the Raman curve is subjected to Savitzky-Golay smoothing treatment in Matlab software, the area of a corresponding curve segment in the graph is solved by integration aiming at the optimized characteristic Raman peak, namely the characteristic Raman peak area;
G. establishing quantitative detection model of inosinic acid content
An inosinic acid quantitative detection model (formula 1) is established by utilizing the characteristic Raman peak area to the inosinic acid content, and the inosinic acid content can be obtained by substituting the Raman characteristic peak area into the formula 1:
Y=-1.212+0.018A
1
+0.152A
2
+0.042A
3
-0.052A
4
-0.188A
5
-0.353A
6
+0.007A
7
+0.074A
8
+0.131A
9
+0.001A
10
+0.144A
11
+0.1A
12
wherein,Ythe content of inosinic acid (mg/g),A 1 -A 12 respectively representing 1322.31, 2620.83, 2645.46, 2703.20, 2808.12, 2935.54, 3070.01, 3393.62, 3462.61, 3591.40, 3699.87 and 3750.74cm-1The correlation coefficient between the predicted value and the true value of the peak area reaches 0.974, the root mean square error RMSEP =0.0363mg/g of the prediction set, and the accuracy is high.
2. Example 2: detection of inosinic acid content in fresh fish meat placed at different times after commercial grass carp slaughtering
a. Measuring Raman spectrum of inosinic acid standard substance
Detecting a Raman spectrum of an inosinic acid standard substance by adopting an InVia type microscopic confocal Raman spectrometer of Renisshaw company, wherein the laser wavelength is 633nm, the laser power is 17mW, the exposure time is 5s, and the average value of the scanning times is 3 times, and acquiring a Raman spectrum of the inosinic acid standard substance, wherein the frequency range of the Raman spectrum is 1000--1；
b. Obtaining a Raman spectrum of a fresh fish sample
After being slaughtered and cleaned, live fish is placed at room temperature (20 +/-1 ℃), IMP content is respectively measured at 0 hour, 1 hour, 2 hours, 3 hours, 4 hours, 6 hours, 12 hours and 24 hours, a slicer is adopted to cut fish slice samples with uniform thickness of 1mm according to fish textures, the samples are placed on a glass slide and placed on a microscope Raman spectrometer object stage, a microscope Raman laser probe is selected to automatically adjust focal length, so that the samples are positioned under the probe, and sample measuring points are collected at multiple points. The laser output power is 17mW, the laser wavelength is 633nm, the exposure time is set to be 5s, the average value of the scanning times is 3 times, and the fish Raman spectrum is obtained;
c. measuring inosinic acid content by high performance liquid chromatography
Measuring inosinic acid content at 0.07-1.66mg/g according to high performance liquid chromatography described in national standard GB/T19676-2005;
d. performing Raman spectrum correction
After irrelevant spectral peaks such as cosmic rays and the like in a sample Raman spectral line and Baseline base line correction are removed in Reinshaw software, spectra acquired at different sampling points of the same slice sample are corrected by taking a characteristic spectrum of water as an internal standard, the water content of sampling micro-areas of the same slice is equal, and spectrum differences caused by uneven sample surface, artificial focusing difference and the like are corrected;
e. the preferred characteristic peak of inosinic acid
And (4) comparing and analyzing the Raman spectra of the IMP standard product and the fish sample, and analyzing the characteristic peak position of the Raman spectrum of the IMP in the fish Raman spectrum. Comparison of 1322.31, 2620.83, 2645.46, 2703.20, 2808.12, 2935.54, 3070.01, 3393.62, 3462.61, 3591.40, 3699.87 and 3750.74cm can be found-1The obviously matched Raman peak is taken as a characteristic peak;
f. calculating the characteristic Raman peak area
After the Raman spectrum curve is subjected to Savitzky-Golay smoothing treatment, the area of a corresponding curve segment in the graph is calculated through integration, and the area is the characteristic Raman peak area;
g. establishing quantitative detection model of inosinic acid content
An inosinic acid quantitative detection model (formula 2) is established for the inosinic acid content by utilizing the characteristic Raman peak area, and the inosinic acid content can be obtained by substituting the characteristic Raman peak area into the formula 2:
Y=-2.185-0.21A
1
-0.114A
2
+0.043A
3
+1.519A
4
-0.077A
5
-0.180A
6
-0.601A
7
+0.121A
8
-
0.098A
9
+0.43A
10
+0.361A
11
-0.187A
12
wherein,Ythe content of inosinic acid (mg/g),A 1 -A 12 respectively representing 1322.31, 2620.83, 2645.46, 2703.20, 2808.12, 2935.54, 3070.01, 3393.62, 3462.61, 3591.40, 3699.87 and 3750.74cm-1The correlation coefficient between the peak area, the predicted value and the true value reaches 0.993, the root mean square error RMSEP =0.0541mg/g of the prediction set, and the accuracy is high.
3. Example 3: detection of inosinic acid content in fresh fish meat of commercial grass carp killed and frozen
A. Measuring Raman spectrum of inosinic acid standard substance
Detecting a Raman spectrum of an inosinic acid standard substance by adopting an InVia type microscopic confocal Raman spectrometer of Renisshaw company, wherein the laser wavelength is 633nm, the laser power is 17mW, the exposure time is 5s, and the average value of the scanning times is 3 times, and acquiring a Raman spectrum of the inosinic acid standard substance, wherein the frequency range of the Raman spectrum is 1000--1；
B. Obtaining a Raman spectrum of a fresh fish sample
The method comprises the steps of killing and cleaning the grass carp, freezing the grass carp at the temperature of minus 60 ℃ for 2 days, cutting a fish slice sample with the thickness of 1mm according to fish texture by using a slicer, placing the sample on a glass slide, placing the glass slide on an objective table of a micro-Raman spectrometer, selecting a micro-Raman laser probe capable of automatically adjusting the focal length and adjusting the micro-Raman laser probe, enabling the sample to be located under the probe, and collecting sample measuring points at multiple points. The laser output power is 17mW, the laser wavelength is 633nm, the exposure time is set to be 5s, the average value of the scanning times is 3 times, and the fish Raman spectrum is obtained;
C. measuring inosinic acid content by high performance liquid chromatography
Measuring inosinic acid content at 1.13-2.05mg/g according to high performance liquid chromatography described in national standard GB/T19676-2005;
D. performing Raman spectrum correction
After irrelevant spectral peaks such as cosmic rays and the like in a sample Raman spectral line and Baseline base line correction are removed in Reinshaw software, spectra acquired at different sampling points of the same slice sample are corrected by taking a characteristic spectrum of water as an internal standard, the water content of sampling micro-areas of the same slice is equal, and spectrum differences caused by uneven sample surface, artificial focusing difference and the like are corrected;
E. the preferred characteristic peak of inosinic acid
And (4) comparing and analyzing the Raman spectra of the IMP standard product and the fish sample, and analyzing the characteristic peak position of the Raman spectrum of the IMP in the fish Raman spectrum. Comparison of 1322.31, 2620.83, 2645.46, 2703.20, 2808.12, 2935.54, 3070.01, 3393.62, 3462.61, 3591.40, 3699.87 and 3750.74cm can be found-1The obviously matched Raman peak is taken as a characteristic peak;
F. calculating the characteristic Raman peak area
After the Raman spectrum curve is subjected to Savitzky-Golay smoothing treatment, the area of a corresponding curve segment in the graph is calculated through integration, and the area is the characteristic Raman peak area;
G. establishing quantitative detection model of inosinic acid content
An inosinic acid quantitative detection model (formula 3) is established by utilizing the characteristic Raman peak area to the inosinic acid content, and the inosinic acid content can be obtained by substituting the characteristic Raman peak area into the formula 3:
Y=-2.533+0.055A
1
+0.139A
2
+0.077A
3
-0.3A
4
-0.289A
5
-0.324A
6
-0.273A
7
+0.268A
8
+
0.151A
9
+0.182A
10
+0.463A
11
+0.109A
12
wherein,Ythe content of inosinic acid (mg/g),A 1 -A 12 respectively representing 1322.31, 2620.83, 2645.46, 2703.20, 2808.12, 2935.54, 3070.01, 3393.62, 3462.61, 3591.40, 3699.87 and 3750.74cm-1The correlation coefficient between the predicted value and the true value of the peak area reaches 0.978, the root mean square error RMSEP =0.0920mg/g of the prediction set, and the accuracy is high.
Thirdly, implementation of the results
As can be seen from fig. 2:
comparing the Raman information of the inosinic acid pure substance standard substance with the obtained Raman information of the fresh fish meat sample, the spectrum information is clear, the number of matched peak positions is large, and the Raman characteristic information of the inosinic acid in the fresh fish meat sample can be effectively obtained according to the conditions in the step 1.
As can be seen in fig. 3:
2600-4000cm-1in the wave band range, 11 characteristic Raman peaks which are closely related to the content of inosinic acid in the fish meat sample are preferably selected, wherein the characteristic Raman peaks are 2620.83, 2645.46, 2703.20, 2808.12, 2935.54, 3070.01, 3393.62, 3462.61, 3591.40, 3699.87 and 3750.74cm-1。
As can be seen from fig. 4:
1000-2000cm-1within the wave bandPreferably 1 characteristic Raman peak which is closely related to the content of inosinic acid in the fish sample is 1322.31cm-1。
As can be seen from fig. 5:
after the Raman spectrum curve is subjected to Savitzky-Golay smoothing treatment, the curve is smoother than the original spectrum curve, so that the calculation of characteristic Raman peak areas is facilitated, and black parts in the graph are 2620.83, 2645.46, 2703.20, 2808.12, 2935.54, 3070.01, 3393.62, 3462.61, 3591.40, 3699.87 and 3750.74cm which are obtained through integration-1The characteristic raman peak area range of the location.
As can be seen in fig. 6:
after the Raman spectrum curve is subjected to Savitzky-Golay smoothing treatment, the curve is smoother than the original spectrum curve, the calculation of the characteristic Raman peak area is facilitated, and the black part in the graph is 1322.31cm obtained through integration-1The characteristic raman peak area range of the location.
Claims (1)
1. A method for rapidly detecting umami substance inosinic acid in fresh fish based on Raman spectrum is characterized by comprising the following steps:
measuring Raman spectrum of inosinic acid standard (1)
Detecting a Raman spectrogram of an inosinic acid standard product by using a microscopic confocal Raman spectrometer, setting laser wavelength, laser power, exposure time and scanning times, and collecting a Raman spectrum of the inosinic acid standard product;
② obtaining the Raman spectrum of the fresh fish sample (2)
After a fresh live fish is slaughtered, a slicing machine is adopted to cut a fish slice sample with uniform thickness according to fish texture and place the fish slice sample on a glass slide, a micro-Raman laser probe is placed on a micro-Raman spectrometer stage and then the focal length is automatically adjusted to adjust the micro-Raman laser probe, so that the sample is positioned under the probe, sample measuring points are collected at multiple points, the laser output power, the laser wavelength, the exposure time and the scanning times are set, and a fish Raman spectrum is obtained;
third, measuring inosinic acid content by chemical method (3)
Measuring the content of inosinic acid by the high performance liquid chromatography in the national standard GB/T19676-2005;
fourthly, carrying out Raman spectrum correction (4)
Removing irrelevant spectral peaks and base lines in a Raman spectral line of a sample in Reinshaw software, and correcting acquired spectra of different sampling points of the same slice sample by taking a characteristic spectrum of water as an internal standard;
fifthly, inosinic acid characteristic peak (5) is preferred
Comparing and analyzing the Raman spectra of the inosinic acid standard substance and the fish sample, analyzing the position of a Raman spectrum characteristic peak of IMP in the fish Raman spectrum, and obtaining a Raman characteristic peak which is obviously matched through comparison;
sixthly, calculating the characteristic Raman peak area (6)
Smoothing the corrected Raman spectrum in Matlab software, and then calculating the area of a corresponding curve section in a Raman spectrogram by integrating the optimized Raman characteristic peak, namely the characteristic Raman peak area;
seventhly, establishing a detection model (7) of inosinic acid content
And establishing an inosinic acid quantitative detection model for the inosinic acid content by utilizing the characteristic Raman peak area, and evaluating the accuracy of the detection model by utilizing the correlation coefficient of the predicted value and the true value and the root mean square error of the correction set.
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| CN109298086A (en) * | 2018-09-18 | 2019-02-01 | 江西省农业科学院农产品质量安全与标准研究所（江西省农业科学院农产品加工研究所） | It is a kind of while measuring the efficient liquid-phase chromatography method of a variety of flavour nucleotides and application in fresh meat |
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