CN112161968A - Donkey-hide gelatin brand identification method based on data fusion - Google Patents
Donkey-hide gelatin brand identification method based on data fusion Download PDFInfo
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- 108010010803 Gelatin Proteins 0.000 title claims abstract description 62
- 229920000159 gelatin Polymers 0.000 title claims abstract description 62
- 239000008273 gelatin Substances 0.000 title claims abstract description 62
- 235000019322 gelatine Nutrition 0.000 title claims abstract description 62
- 235000011852 gelatine desserts Nutrition 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000004927 fusion Effects 0.000 title claims abstract description 10
- 238000001228 spectrum Methods 0.000 claims abstract description 38
- 238000012937 correction Methods 0.000 claims abstract description 18
- 238000002536 laser-induced breakdown spectroscopy Methods 0.000 claims abstract description 16
- 238000004458 analytical method Methods 0.000 claims abstract description 11
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 11
- 238000012795 verification Methods 0.000 claims abstract description 11
- 230000003595 spectral effect Effects 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 5
- 238000011160 research Methods 0.000 claims abstract description 3
- 229910052757 nitrogen Inorganic materials 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 6
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- 238000010987 Kennard-Stone algorithm Methods 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- 238000005070 sampling Methods 0.000 claims description 2
- 238000006467 substitution reaction Methods 0.000 claims description 2
- 229910044991 metal oxide Inorganic materials 0.000 claims 3
- 150000004706 metal oxides Chemical class 0.000 claims 3
- 239000003814 drug Substances 0.000 description 5
- 238000007781 pre-processing Methods 0.000 description 5
- 238000010239 partial least squares discriminant analysis Methods 0.000 description 4
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- 238000004497 NIR spectroscopy Methods 0.000 description 2
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- 238000010586 diagram Methods 0.000 description 2
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- 238000010200 validation analysis Methods 0.000 description 2
- 208000032467 Aplastic anaemia Diseases 0.000 description 1
- 229920000045 Dermatan sulfate Polymers 0.000 description 1
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 208000015710 Iron-Deficiency Anemia Diseases 0.000 description 1
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- 238000010521 absorption reaction Methods 0.000 description 1
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- 238000001636 atomic emission spectroscopy Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 108010052008 colla corii asini Proteins 0.000 description 1
- AVJBPWGFOQAPRH-FWMKGIEWSA-L dermatan sulfate Chemical compound CC(=O)N[C@H]1[C@H](O)O[C@H](CO)[C@H](OS([O-])(=O)=O)[C@@H]1O[C@H]1[C@H](O)[C@@H](O)[C@H](O)[C@H](C([O-])=O)O1 AVJBPWGFOQAPRH-FWMKGIEWSA-L 0.000 description 1
- 229940051593 dermatan sulfate Drugs 0.000 description 1
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- 238000004880 explosion Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 238000004128 high performance liquid chromatography Methods 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000001095 inductively coupled plasma mass spectrometry Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000004895 liquid chromatography mass spectrometry Methods 0.000 description 1
- 239000002366 mineral element Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 108090000623 proteins and genes Proteins 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
Abstract
The invention discloses a donkey-hide gelatin identification method based on data fusion, which comprises the following steps: 1) collecting a plurality of donkey-hide gelatin samples, collecting a first original spectrum by using a Laser Induced Breakdown Spectroscopy (LIBS), and processing to obtain first spectrum data; meanwhile, a near infrared spectrum analyzer (NIR) is adopted to collect a second original spectrum of the donkey-hide gelatin sample in an integrating sphere diffuse reflection mode, and second spectrum data are obtained after processing; 2) splicing the first spectrum data and the second spectrum data to obtain fused spectrum data; 3) dividing a sample set into a correction set and a verification set by taking the fused spectral data as a research object; 4) and setting specific brand donkey-hide gelatin in the correction set sample as 1, setting non-specific brand donkey-hide gelatin as-1, and further establishing a partial least square specific brand donkey-hide gelatin brand identification analysis model by using the correction set sample. The invention establishes a comprehensive, rapid, nondestructive and high-accuracy donkey-hide gelatin brand distinguishing method.
Description
The technical field is as follows:
the invention relates to the technical field of traditional Chinese medicine detection, in particular to a donkey-hide gelatin brand identification method based on data fusion.
Background art:
donkey-hide gelatin (Colla Corii Asini) is firstly recorded in Shen nong Ben Cao Jing, is a traditional and famous and precious traditional Chinese medicine, has been applied for 3000 years so far, is known as blood-enriching holy medicine and nourishing national treasure from ancient times, and can be used for nourishing blood-loss anemia, iron-deficiency anemia, aplastic anemia, the elderly and the weak, children and women. Along with the increasing demand of medicinal materials of donkey-hide gelatin, the price of donkey-hide gelatin is also increasing. The phenomenon of adulteration in high-price donkey-hide gelatin is often prohibited, and the reputation and brand value of genuine medicinal materials are seriously influenced. Currently, the quality evaluation of the donkey-hide gelatin is mostly to evaluate the authenticity and quality of the donkey-hide gelatin based on the appearance, color, texture, smell, contents of protein, amino acid, dermatan sulfate, mineral elements and the like. The quality control method of donkey-hide gelatin comprises the steps of measuring the content of amino acid by a high performance liquid chromatography, measuring a characteristic peptide segment by a liquid chromatography-mass spectrometry, and measuring elements by an inductively coupled plasma mass spectrometry, can only solve the problem of adulteration, and consumes time and labor in the identification process.
Laser Induced Breakdown Spectroscopy (LIBS) is a rapid analytical technique for simultaneous detection of multiple elements in micro-areas, uses laser pulses as an excitation source to induce atomic emission spectroscopy of laser plasma, and is widely applied to the fields of industry, agriculture, medicine, environment, art and archaeology, space exploration, military explosion detection and the like. Compared with the traditional method, the laser-induced breakdown spectroscopy technology has the advantages of simple sample preparation, small sample destructiveness, high analysis speed and high sensitivity, is suitable for detecting element components in solid, liquid or gaseous samples, can realize the advantages of multi-element, in-situ, on-line, remote and real-time detection and the like, and is widely concerned. The near infrared spectroscopy is a rapid, lossless and environment-friendly analysis technology, and mainly consists of absorption bands of frequency doubling and combined frequency of hydrogen-containing groups such as C-H, N-H, S-H, O-H and the like. The information content in the near infrared spectrum is relatively rich, and most substances respond in the spectrum. The near infrared spectrum region not only reflects the chemical information of each substance, but also indirectly analyzes the physical and biological information of the substances, has holographic characteristics, and is widely applied to the fields of agriculture, food, petrochemical industry, tobacco, pharmacy and the like. However, no relevant report applied to donkey-hide gelatin identification exists at present.
The invention content is as follows:
the invention integrates laser-induced breakdown spectroscopy and near infrared spectroscopy data, information of inorganic elements and organic elements, a traditional Chinese medicine quality evaluation method of multi-source information, a partial least square discriminant analysis model, holographic organic components and inorganic element factors are established, a set of rapid, nondestructive, rapid and high-discriminant-rate donkey-hide gelatin identification method is established by a multivariate analysis method, and rapid discrimination of the brand of donkey-hide gelatin is realized.
The invention provides a donkey-hide gelatin identification method based on data fusion, which comprises the following steps:
1) collecting a plurality of donkey-hide gelatin samples, collecting a first original spectrum by using a Laser Induced Breakdown Spectroscopy (LIBS), selecting an element characteristic spectral line according to the LIBS, and carrying out normalization processing to obtain first spectrum data; meanwhile, a near infrared spectrum analyzer (NIR) is adopted to collect a second original spectrum of the donkey-hide gelatin sample in an integrating sphere diffuse reflection mode, a characteristic wave band is selected, and data normalization is carried out after pretreatment to obtain second spectrum data;
2) splicing the first spectrum data and the second spectrum data to obtain fused spectrum data;
3) taking the fused spectral data as a research object, dividing a sample set into a correction set and a verification set, and optimizing the potential variable number of the correction set by adopting a leave-one-cross verification method;
4) setting specific brand donkey-hide gelatin in the correction set sample as 1, setting non-specific brand donkey-hide gelatin as-1, further establishing partial least square specific brand donkey-hide gelatin brand identification analysis model by using the correction set sample, and using Rmsec and R2c. Se, Sp and Ta are used for judging the accuracy and precision of the correction set model; substituting the validation set sample into the established partial least squares discriminant analysis (PLS-DA) model to obtain Rmsep and R2p, Se, Sp, Ta and Y-predicted are used for judging the stability of the verification set model and the donkey-hide gelatin brand.
In one embodiment of the present invention, the instrument parameters for collecting the laser-induced breakdown spectroscopy in step 1) are specifically as follows: laser energy was set to 50mJ, pulse repetition frequency: 5 Hz; laser focusing light spot: 100 μm; the pulse energy is about: 340 mJ; delay time: 0.6 mu s; integration time: 1 ms; the door width: 1000 mus; and cumulatively collecting each sampling point for a plurality of times, and averaging the spectra.
In one embodiment according to the invention, the characteristic lines of tables C, Mn, Si, Ca, C-N, Al, Sr, Ba, Fe, Na, N, Li, H, K, O are selected in step 1).
In one embodiment according to the present invention, the instrument parameters for the near infrared spectrum acquisition in step 1) are as follows: an integrating sphere diffuse reflection measurement mode, air is taken as reference, and the acquisition range is 10000-4000cm-1Resolution of 4cm-1And collecting for a plurality of times, and repeatedly scanning each sample for a plurality of times to obtain an average spectrum.
In one embodiment according to the invention, the characteristic band of the near infrared spectrum selected in step 1) is 5500-9000cm-1。
In one embodiment according to the invention, the near infrared spectrum pre-treatment method described in step 1) is SNV, first derivative, MSC.
In one embodiment according to the present invention, the sample set is partitioned in step 3) by employing the KS algorithm.
In one embodiment according to the invention, the ratio of the correction set to the validation set in step 3) is 2: 1.
In one embodiment according to the present invention, in step 4), if the predicted result Y-predicted is less than 0, it is determined as non-specific brand donkey-hide gelatin; if the predicted Y-predicted is greater than 0, the donkey-hide gelatin with the specific brand is judged.
In one embodiment according to the present invention, the specific brand of donkey-hide gelatin is dong donkey-hide gelatin.
The invention also discloses application of the donkey-hide gelatin identification method based on data fusion in authenticity identification and brand identification of donkey-hide gelatin.
The invention has the beneficial effects that:
the invention establishes a comprehensive, rapid, nondestructive and high-accuracy donkey-hide gelatin brand distinguishing method. The invention establishes the Dong donkey-hide gelatin brand qualitative discrimination analysis model with the model discrimination accuracy reaching 95 percent for the first time.
Drawings
Fig. 1 is an original spectrum diagram of 78 donkey-hide gelatin samples collected by a laser-induced breakdown spectrometer, wherein 40 donkey-hide gelatin samples are collected by the laser-induced breakdown spectrometer, and 38 donkey-hide gelatin samples are collected by the laser-induced breakdown spectrometer.
FIG. 2 is a near-infrared original spectrum of a donkey-hide gelatin sample collected by a near-infrared spectrometer at room temperature.
FIG. 3 is a diagram showing the result of a PLS-DA qualitative discriminant analysis model for obtaining LIBS data of samples of different brands according to the present invention.
Fig. 4A, fig. 4B, fig. 4C, and fig. 4D are graphs of PLS-DA qualitative discriminant analysis model results of fusion of LIBS and NIR data obtained from different brands of samples according to the present invention, respectively, where fig. 4A is a graph of results of Raw data preprocessing, fig. 4B is a graph of results of SNV data preprocessing, fig. 4C is a graph of results of first-order derivative data preprocessing, and fig. 4D is a graph of results of MSC data preprocessing.
The specific implementation mode is as follows:
the following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention more readily understood by those skilled in the art, and thus will more clearly and distinctly define the scope of the invention.
Example 1 brand identification of donkey-hide gelatin
Selecting 78 donkey-hide gelatin samples, wherein 40 parts of Dong donkey-hide gelatin and 38 parts of non-Dong donkey-hide gelatin are shown in Table 1.
TABLE 1 donkey-hide gelatin sample Table
Collecting original spectra of 78 donkey-hide gelatin samples by adopting laser-induced breakdown spectroscopy, wherein the instrument parameters are set as follows: laser energy was set to 50mJ, pulse repetition frequency: 5 Hz; laser focusing light spot: 100 μm; the pulse energy is about: 340 mJ; delay time: 0.6 mu s; integration time: 1 ms; the gate width is set to 1000 mus; each sample point was sampled 100 times cumulatively and the average spectrum was taken. The spectrum is shown in FIG. 1.
Selecting characteristic wavelengths representing C, Mn, Si, Ca, C-N, Al, Sr, Ba, Fe, Na, N, Li, H, K and O, and showing in the following table 2:
table 2 characteristic wavelength table of representative elements
Further carrying out normalization processing on the spectrum under the selected characteristic wavelength.
The method comprises the following steps of collecting original spectra of 78 donkey-hide gelatin samples by using a near-infrared spectrometer, wherein the instrument parameters are as follows: an integrating sphere diffuse reflection measurement mode, air is taken as reference, and the acquisition range is 10000-4000cm-1Resolution of 4cm-1The number of collection was 64 times, and each sample was scanned three times in duplicate to obtain an average spectrum. The near infrared diffuse reflection spectrum obtained by collecting 78 donkey-hide gelatin samples according to the parameters is shown in figure 2.
Selecting a near-infrared original spectrum characteristic wave band of 5500-9000cm-1SNV, first derivative and MSC are respectively adopted for pretreatment and then normalization treatment is carried out.
And splicing the LIBS spectrum of the selected characteristic spectral line with the NIR spectrum after different pre-treatments to obtain fused spectral data.
A KS algorithm is adopted to divide 78 donkey-hide gelatin samples into a correction set and a verification set according to a ratio of 2:1, a minimum two-discrimination model is established, and relevant parameters of each model are obtained so as to determine the accuracy, precision, stability and prediction capability of the established prediction model. Rmesc, R2c. Se, Sp and Ta are used for judging the accuracy and precision of the correction set model, Rmsep and R2p, Se, Sp, Ta and Y-predicted are used for judging the stability and the prediction capability of the verification set model, and the results are shown in Table 3 and FIGS. 3, 4A, 4B, 4C and 4D.
TABLE 3 fused data sheet
Note: se (%) sensitivity; sp (%) specificity; ta (%) Total Rate of Authority
The invention establishes a partial least square discrimination model based on a data fusion method, R2The c is more than 80 percent, so that the model is stable, and the total correction rate of Ta (%) is more than 95 percent, so that the model can be used for brand identification.
In summary, as shown in fig. 4A, 4B, 4C and 4D, the east donkey-hide gelatin brand identification method based on data fusion provided by the present invention adopts the PLS-DA method, and can realize the identification of donkey-hide gelatin brands by LIBS and NIR data fusion of different pretreatments (SNV, first derivative, MSC data preprocessing method of NIR), which has feasibility. LIBS and NIR have the advantages of no damage, no pollution, rapidness and the like, so that the method can be used as a new method for identifying the Dong donkey-hide gelatin brand.
The above summary and the detailed description are intended to demonstrate the practical application of the technical solutions provided by the present invention, and should not be construed as limiting the scope of the present invention. Various modifications, equivalent substitutions, or improvements may be made by those skilled in the art within the spirit and principles of the invention. The scope of the invention is to be determined by the appended claims.
Claims (10)
1. A donkey-hide gelatin identification method based on data fusion is characterized by comprising the following steps:
1) collecting a plurality of donkey-hide gelatin samples, collecting a first original spectrum by using a Laser Induced Breakdown Spectroscopy (LIBS), selecting an element characteristic spectral line according to the LIBS, and carrying out normalization processing to obtain first spectrum data; meanwhile, a near infrared spectrum analyzer (NIR) is adopted to collect a second original spectrum of the donkey-hide gelatin sample in an integrating sphere diffuse reflection mode, a characteristic wave band is selected, and data normalization is carried out after pretreatment to obtain second spectrum data;
2) splicing the first spectrum data and the second spectrum data to obtain fused spectrum data;
3) taking the fused spectral data as a research object, dividing a sample set into a correction set and a verification set, and optimizing the potential variable number of the correction set by adopting a leave-one-cross verification method;
4) setting specific brand donkey-hide gelatin in the correction set sample as 1, setting non-specific brand donkey-hide gelatin as-1, further establishing partial least square specific brand donkey-hide gelatin brand identification analysis model by using the correction set sample, and using Rmsec and R2c. Se, Sp and Ta are used for judging the accuracy and precision of the correction set model; substituting the sample of the verification set into the established partial least square discriminant analysis model through Rmsep and R2And p, Se, Sp, Ta and Y-predicted judge the stability of the verification set model and the donkey-hide gelatin brand.
2. The method according to claim 1, wherein the instrument parameters during the collection of the laser-induced breakdown spectroscopy in step 1) are as follows: laser energy was set to 50mJ, pulse repetition frequency: 5 Hz; laser focusing light spot: 100 μm; the pulse energy is about: 340 mJ; delay time: 0.6 mu s; integration time: 1 ms; the door width: 1000 mus; and cumulatively collecting each sampling point for a plurality of times, and averaging the spectra.
3. The method of claim 1, wherein: selecting characteristic spectral lines of substitution tables C, Mn, Si, Ca, C-N, Al, Sr, Ba, Fe, Na, N, Li, H, K and O in the step 1).
4. The method of claim 1, wherein: in the step 1), the instrument parameters during the near infrared spectrum acquisition are as follows: an integrating sphere diffuse reflection measurement mode, air is taken as reference, and the acquisition range is 10000-4000cm-1Resolution of 4cm-1And collecting for a plurality of times, and repeatedly scanning each sample for a plurality of times to obtain an average spectrum.
5. The method of claim 1, wherein the step of removing the metal oxide layer comprises removing the metal oxide layer from the metal oxide layer: the characteristic wave band of the near infrared spectrum selected in the step 1) is 5500-9000cm-1。
6. The method of claim 1, wherein: the near infrared spectrum pretreatment method in the step 1) comprises SNV, first derivative and MSC.
7. The method of claim 1, wherein: and 3) dividing the sample set by adopting a KS algorithm in the step 3).
8. The method of claim 1, wherein: the ratio of the correction set to the verification set in step 3) is 2: 1.
9. The method of claim 1, wherein: in the step 4), judging the donkey-hide gelatin with a non-specific brand if the Y-predicted result of the prediction is less than 0; if the predicted Y-predicted is greater than 0, the donkey-hide gelatin with the specific brand is judged.
10. The method of any one of claims 1-9, wherein the specific brand of donkey-hide gelatin is dong's donkey-hide gelatin.
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Application publication date: 20210101 |