CN113125378A - Near infrared spectrum-based method for rapidly detecting nutritional components in camel meat at different parts - Google Patents
Near infrared spectrum-based method for rapidly detecting nutritional components in camel meat at different parts Download PDFInfo
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- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 42
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- 235000012041 food component Nutrition 0.000 title claims description 3
- 238000001514 detection method Methods 0.000 claims abstract description 42
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- 235000019625 fat content Nutrition 0.000 claims description 20
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- 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
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Abstract
The invention provides a method for quickly detecting nutritional ingredients in camel meat at different parts based on near infrared spectrum, which comprises the steps of collecting near infrared spectrum original data of a camel meat standard product, carrying out different pretreatments on the obtained original data, and establishing a quick detection model of the moisture content, the protein content and the fat content of the camel meat; and detecting the camel meat sample to be detected through the model. Aiming at different nutritional ingredients of camel meat, the data are subjected to different preprocessing before a rapid detection model is established, and errors caused by uneven or polluted samples, interference of other components on the samples, inaccurate spectrum acquisition, incorrect selection of a spectrum range, high spectrum noise and the like are pertinently eliminated. The invention compares the combination of different data preprocessing modes and different modeling methods to obtain the optimal spectrum preprocessing method, and verifies that the correlation between the predicted value and the chemical measured value of the rapid detection model is high, and the model prediction accuracy is good.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the field of detection of nutritional ingredients of camel meat, in particular to a method for rapidly detecting nutritional ingredients in different parts of camel meat by using a near infrared spectrum technology.
[ background of the invention ]
Moisture, protein and fat content are important indicators for assessing the quality of camel meat. The national standard determination method has the defects of complex process, difficult control of conditions, high reagent consumption, long time consumption and the like. The near infrared spectrum analysis is a green analysis technology which is rapidly developed in recent years, and has the advantages of convenience, rapidness, high efficiency, accuracy, lower cost, no damage to samples, no consumption of chemical reagents, no environmental pollution and the like, so that the technology is favored by more and more people, and a new way is provided for rapid detection of nutritional ingredients of camel meat.
Infrared Near Infrared Spectroscopy (NIRS) is a wave of electromagnetic radiation between the visible (Vis) and mid-Infrared (MIR) regions, the American Society for Testing and Materials (ASTM) defines the Near Infrared spectral region as the 780-2526nm region, the first non-visible region one finds in the absorption Spectrum. The near infrared spectrum region is consistent with the complex frequency of the vibration of the hydrogen-containing group (O-H, N-H, C-H) in the organic molecule and the absorption region of each level of frequency doubling, and the characteristic information of the hydrogen-containing group of the organic molecule in the sample can be obtained by scanning the near infrared spectrum of the sample. The near infrared spectrum information of the substance has an internal relation with the moisture, protein and fat contents of the substance, the near infrared spectrum information and the moisture, protein and fat contents of the substance are correlated by a chemometric method, and a quantitative relation between the near infrared spectrum information and the protein and fat contents is established to obtain a quantitative model; the content of water, protein and fat in the unknown sample can be obtained according to a quantitative model by collecting a near infrared spectrogram of the unknown sample. At present, because camels are not widely distributed due to strong territory, the research on camel meat is not a lot, people have already preliminarily studied the nutrient components and meat products of camel meat, but the research on predicting the quality characteristics of camel meat by using the near infrared spectrum technology is not reported yet.
For example, chinese patent application CN 105548062a discloses a method for multi-index rapid nondestructive synchronous detection of fresh beef, which utilizes a portable near-infrared spectrometer to detect the multi-indexes of cholesterol, moisture, fat and protein content, shearing force and water holding capacity in fresh beef, establishes a multi-index prediction model, and realizes the rapid detection of the multi-indexes of fresh beef. However, the data processing method selected by the model building method is different, and the beef and the camel meat have different properties, so that the method cannot be applied to detection of the camel meat.
[ summary of the invention ]
The invention aims to detect the nutritional quality of meat at different parts of Alxa bactrian camel by using a near infrared spectrum technology, establish a prediction model, and provide a method for rapidly detecting the quality of camel meat and realizing multi-component synchronous rapid detection.
Therefore, the invention provides a method for rapidly detecting nutrient components in camel meat at different parts based on near infrared spectrum, which comprises the following steps:
(1) collecting n kinds of camel meat at different parts, mincing to obtain meat paste, and preparing m parts of sample at each part to obtain nxm parts of camel meat standard substance;
(2) respectively collecting the original data of the near infrared spectrum of the standard substance of the n multiplied by m parts of camel meat, wherein the near infrared spectrum range is 4000.00cm-1～10000.00cm-1；
(3) Establishing a rapid detection model for water content of camel meat
Respectively carrying out Multivariate Scattering Correction (MSC) processing on the near infrared spectrum original data in the step (2), and establishing a relation between the obtained data and the optical path by utilizing minimum deviation two Multiplication to obtain a rapid detection model of the water content of the camel meat;
(4) establishing a rapid detection model for protein content of camel meat
Respectively processing the near infrared spectrum original data in the step (2) by a Standard Normal Variate conversion (SNV), and establishing a relation between the obtained data and an optical path by utilizing a minimum deviation two multiplication to obtain a rapid detection model of the protein content of the camel meat;
(5) establishing a rapid detection model for fat content of camel meat
Respectively carrying out filtering smoothing (Savitzky-Golay, SG) processing on the near infrared spectrum original data in the step (2), and establishing a relation between the obtained data and an optical path by utilizing minimum deviation two multiplication to obtain a camel meat fat content rapid detection model;
(6) taking a camel meat sample to be detected, and crushing the sample into meat paste;
(7) collecting the original data of the near infrared spectrum of the camel meat sample to be detected in the step (6), wherein the near infrared spectrum range is 4000.00cm-1～10000.00cm-1And (4) respectively carrying out multivariate scattering correction processing, standard normal variable conversion processing and filtering smoothing processing on the obtained data, and respectively substituting the obtained values into the rapid detection models in the steps (3) - (5) to obtain the rapid detection method for the nutrient components of the sample to be detected, wherein the nutrient components comprise water, protein and fat contents.
In the invention, the original data of the near infrared spectrum of the camel meat standard substance or the sample to be detected is acquired with the spectral resolution of 8cm-1And 64 scans at 2 x gain, with the resulting data collected every four hours.
When the near infrared spectrum data is collected, a Fourier transform near infrared spectrometer is used, and preferably, an integrating sphere module of the Fourier transform near infrared spectrometer is used for collecting the near infrared spectrum original data of a standard product or a sample to be measured.
Through comparison, the invention verifies that for different nutritional ingredients of camel meat, different data preprocessing should be carried out on the near infrared spectrum original data, and the obtained result can accurately realize the rapid detection of different nutritional ingredients. Wherein, for moisture detection, in step (3), the determination coefficient R of the multivariate scattering correction process2Corrected root mean square error RMSECV is 0.7459 and 0.0085, respectively.
For protein content detection, in step (4), the determination coefficient R of the standard normal variable conversion method is processed2Corrected root mean square error RMSECV is 0.6602 and 0.2879, respectively.
For fat content detection, in step (5), the decision coefficient R of the filtering smoothing process2Corrected root mean square error RMSECV is 0.9885 and 0.0863, respectively.
Aiming at different nutritional ingredients of camel meat, the data are subjected to different preprocessing before a rapid detection model is established, and errors caused by uneven or polluted samples, interference of other components on the samples, inaccurate spectrum acquisition, incorrect selection of a spectrum range, high spectrum noise and the like are pertinently eliminated. According to the invention, through a large number of experimental researches, the combination of different pretreatment modes and different modeling methods is compared to obtain the optimal spectrum pretreatment method, and the correlation between the predicted value and the chemical measurement value of the rapid detection model is high, so that the accuracy of the prediction model is good.
[ description of the drawings ]
FIG. 1 is a graph of 390 original spectra of camel meat samples at different positions; wherein the vertical axis is absorbance, and the horizontal axis is wavelength;
FIG. 2 is an average spectrum of camel meat samples at different positions; wherein the vertical axis is absorbance, and the horizontal axis is wavelength;
FIG. 3 is a principal component analysis chart of the spectrum of a camel meat sample at different parts;
FIG. 4 is a diagram of the internal cross validation result of a moisture near-infrared prediction model;
FIG. 5 is a graph of the results of internal cross-validation of a protein near-infrared prediction model;
fig. 6 is a diagram of the internal cross validation result of the fat near-infrared prediction model.
[ detailed description ] embodiments
The following examples serve to illustrate the technical solution of the present invention without limiting it.
Example 1
Collecting 13 camel meat samples (including pepper strips, camel fingers, big cucumber strips, abdominal meat, tendon meat, inner spine, dragon rice, Shannao, gluteal meat, outer spine, breast meat, small cucumber strips and eye meat) at different positions, wherein 30 standard products are parallel at each position, and the total amount is 390 standard products, and preparing meat paste by a meat grinder for later use. With 21 replicates per site as the correction set and 9 replicates per site as the verification set.
The content of nutrient components in each standard product is respectively determined by adopting national standard as a reference value, and the specific determination mode is as follows: according to GB 5009.3-2016 (determination of moisture in food), moisture content in camel meat at different parts is determined by adopting a direct drying method at 105 ℃; according to the standard GB 5009.5-2016, the protein content in camel meat at different parts is determined by a Kjeldahl method of protein determination in food; the fat content of camel meat at different parts is determined according to the standard GB 5009.6-2016 method for determining fat content in food. The results obtained are shown in table 1 as modeled chemical values.
TABLE 1 statistics of water, protein and fat content of different parts of camel meat
And (3) collecting the spectrum information of the camel meat paste of each standard product by utilizing an integrating sphere module of the Fourier transform near-infrared spectrometer. At a spectral resolution of 8cm-1Scanning 64 times under 2 multiplied gain, collecting spectrum background information once every four hours to obtain 4000.00cm-1～10000.00cm-1Near infrared spectrum information of camel meat in the range. As shown in FIG. 1, the spectra are 8400, 6800, 5100cm-1Obvious absorption peaks are generated nearby, and are 4200 and 5700cm-1Noise near the spectrum is large. 6900cm-1The broadband is mainly caused by water, is the first harmonic of oxyhydrogen stretching vibration and is 5000cm-1The absorption band at this point is mainly related to protein, and the absorption band corresponding to the fat content mainly appears at 8350cm-1、5800～5700cm-1And 5300-5260 cm-1To (3).
The original data of the near infrared spectrum of the 273 calibration sets of the standard samples at 13 different positions are averaged to obtain an average spectrogram of 13 different positions, as shown in fig. 2. Wherein, the wavelength is 10000cm-1The curves from high to low absorbance represent: tendon meat, camel-like line, eye meat, small cucumber strip, big cucumber strip, abdominal meat, hip meat, shannao, back bone, breast meat, Milong, pepper strip and external bone.
The principal component analysis was performed on the raw spectral data of the 13 different parts of the camel meat samples, and the first two principal component scores of the samples were plotted to obtain PC1 and PC2, and when the whole sample set was represented by PC1 and PC2, the external ridge parts (WJ) of the 13 different parts of the camel meat samples showed significantly different distribution characteristics, as shown in FIG. 3. It can be seen that samples from different parts of the camel meat had similar scoring characteristics, with the aggregation in the middle. However, individual parts such as the outer ridge, eye flesh, small cucumber strip, hip flesh, etc. are different from other parts because the near infrared spectrum is also different due to the difference in carcass position.
The original data of the near infrared spectrum of 273 Standard calibration sets of 13 different parts are respectively subjected to preprocessing methods such as filtering smoothing (Savitzky-Golay, SG), Standard Normal variable transformation (SNV), Multiple Scattering Correction (MSC) and the like, and are compared with each other by using unprocessed data, and partial least squares discriminant analysis (PLS-DA) is respectively utilized. Because the predicting ability of infrared to chemical indexes is strong, CH and OH groups in water, fat and protein have strong near infrared absorption, and protein and fat are main components in meat and are main reasons causing spectral difference, the predicting models of three nutrient components are established, and the determining coefficients R of the models are respectively calculated2And correcting for the root mean square error RMSECV.
TABLE 2 Effect of different preprocessing methods on modeling Effect
Determining a coefficient R from the model2And correcting the root mean square error RMSECV to select the best model. As can be seen from table 2, the optimal data preprocessing methods for different nutritional ingredients are different. Wherein, the optimal pretreatment method of the water content model is MSC, and the determination coefficient R of the model2RMSECV are 0.7459 and 0.0085, respectively; the optimal pretreatment method of the protein content model is SNV, and the determination coefficient R of the model2RMSECV is 0.6602 respectivelyAnd 0.2879; the optimal pretreatment method of the fat content model is S-G, and the determination coefficient R of the model2RMSECV are 0.9885 and 0.0863, respectively. Therefore, the three specific data preprocessing methods are selected to carry out data preprocessing on the rapid detection models of the three nutrient components.
In addition, as can be seen from table 2, the rapid detection effect of the near infrared spectrum technology on the camel meat-like fat content at different parts is the best, and then the prediction of the moisture content is performed, while the result of the protein content detection is less ideal.
And (3) taking 117 verification sets as samples to be tested to verify the rapid detection model. The near infrared spectrum original data of the sample to be detected is obtained through the same data acquisition method and data processing method, the obtained data is subjected to multivariate scattering correction processing, standard normal variable conversion processing and filtering smoothing processing, the obtained values are respectively substituted into rapid detection models of moisture, protein content and fat content for verification, and the verification result is shown in fig. 4-5.
The result shows that the water content of different parts of the camel meat sample in the verification set is verified by the determination coefficient R of the verification set2RMSEV 0.7741, 0.0134; coefficient of determination R of protein content model2RMSEV 0.6725, 0.2760; coefficient of determination R of fat content model2RMSEV is 0.9963, 0.0567. The verification result is consistent with the correction set result, which shows that the near infrared spectrum technology has the best prediction capability on the fat content of the camel meat samples at different parts, and although the prediction result of the protein content is not ideal, the further research is needed.
Claims (7)
1. A method for rapidly detecting nutritional components in camel meat at different parts based on near infrared spectrum is characterized by comprising the following steps:
(1) collecting n kinds of camel meat at different parts, mincing to obtain meat paste, and preparing m parts of sample at each part to obtain nxm parts of camel meat standard substance;
(2) respectively collecting the original data of the near infrared spectrum of the standard substance of the n multiplied by m parts of camel meat, wherein the near infrared spectrum range is 4000.00cm-1～10000.00cm-1；
(3) Establishing a rapid detection model for water content of camel meat
Respectively carrying out multivariate scattering correction processing on the near infrared spectrum original data in the step (2), and establishing a relation between the obtained data and the optical path by utilizing minimum bias two multiplication to obtain a rapid detection model of the water content of the camel meat;
(4) establishing a rapid detection model for protein content of camel meat
Respectively carrying out standard normal variable conversion method processing on the near infrared spectrum original data in the step (2), and establishing a relation between the obtained data and the optical path by utilizing minimum bias-two multiplication to obtain a rapid detection model of the protein content of the camel meat;
(5) establishing a rapid detection model for fat content of camel meat
Respectively carrying out filtering smoothing treatment on the near infrared spectrum original data in the step (2), and establishing a relation between the obtained data and the optical path by utilizing minimum bias multiplication to obtain a camel meat fat content rapid detection model;
(6) taking a camel meat sample to be detected, and crushing the sample into meat paste;
(7) collecting the original data of the near infrared spectrum of the camel meat sample to be detected in the step (6), wherein the near infrared spectrum range is 4000.00cm-1～10000.00cm-1And (4) respectively carrying out multivariate scattering correction processing, standard normal variable conversion processing and filtering smoothing processing on the obtained data, and respectively substituting the obtained values into the rapid detection models in the steps (3) - (5) to obtain the rapid detection method for the nutrient components of the sample to be detected, wherein the nutrient components comprise water, protein and fat contents.
2. The rapid detection method according to claim 1, wherein in steps (2) and (7), the raw data of the near infrared spectrum is acquired at a spectral resolution of 8cm-1And 64 scans at 2 x gain, with the resulting data collected every four hours.
3. The rapid detection method according to claim 1, wherein in steps (2) and (7), the near infrared spectrometer is a Fourier transform near infrared spectrometer.
4. The rapid detection method according to claim 3, wherein in steps (2) and (7), the original data of the near infrared spectrum of the standard or the sample to be detected is collected by an integrating sphere module of a Fourier transform near infrared spectrometer.
5. The rapid detection method according to claim 1, wherein in step (3), the decision coefficient R of the multivariate scatter correction process2Corrected root mean square error RMSECV is 0.7459 and 0.0085, respectively.
6. The rapid detection method according to claim 1, wherein in the step (4), the decision coefficient R is processed by a normal-to-normal conversion method2Corrected root mean square error RMSECV is 0.6602 and 0.2879, respectively.
7. The fast detection method according to claim 1, wherein in step (5), the decision coefficient R of the filter smoothing process2Corrected root mean square error RMSECV is 0.9885 and 0.0863, respectively.
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Application publication date: 20210716 |