CN114062298A - Quantitative detection method of furosine - Google Patents
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- YQHPCDPFXQXCMV-VIFPVBQESA-N (2s)-2-amino-6-[[2-(furan-2-yl)-2-oxoethyl]amino]hexanoic acid Chemical compound OC(=O)[C@@H](N)CCCCNCC(=O)C1=CC=CO1 YQHPCDPFXQXCMV-VIFPVBQESA-N 0.000 title claims abstract description 61
- ZOEGQXCAXOUFHN-UHFFFAOYSA-N Furosin Natural products OC1C2OC(=O)C(C=3C4C5(O)O)=CC(O)=C(O)C=3OC5(O)C(=O)C=C4C(=O)OC1C(CO)OC2OC(=O)C1=CC(O)=C(O)C(O)=C1 ZOEGQXCAXOUFHN-UHFFFAOYSA-N 0.000 title claims abstract description 61
- 238000001514 detection method Methods 0.000 title claims abstract description 50
- 238000000034 method Methods 0.000 claims abstract description 37
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 31
- 238000001228 spectrum Methods 0.000 claims abstract description 17
- 238000012216 screening Methods 0.000 claims abstract description 6
- 239000000523 sample Substances 0.000 claims description 47
- 239000002253 acid Substances 0.000 claims description 24
- 235000013336 milk Nutrition 0.000 claims description 13
- 239000008267 milk Substances 0.000 claims description 13
- 210000004080 milk Anatomy 0.000 claims description 13
- 235000020122 reconstituted milk Nutrition 0.000 claims description 13
- 230000003595 spectral effect Effects 0.000 claims description 10
- 238000010276 construction Methods 0.000 claims description 9
- 238000004128 high performance liquid chromatography Methods 0.000 claims description 7
- 102000004169 proteins and genes Human genes 0.000 claims description 7
- 108090000623 proteins and genes Proteins 0.000 claims description 7
- 238000003908 quality control method Methods 0.000 claims description 7
- 238000001195 ultra high performance liquid chromatography Methods 0.000 claims description 6
- 238000000611 regression analysis Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 2
- 238000012569 chemometric method Methods 0.000 claims description 2
- 239000012468 concentrated sample Substances 0.000 claims description 2
- 239000003153 chemical reaction reagent Substances 0.000 abstract description 3
- 235000020191 long-life milk Nutrition 0.000 description 23
- 235000013365 dairy product Nutrition 0.000 description 20
- 238000005516 engineering process Methods 0.000 description 8
- HYBBIBNJHNGZAN-UHFFFAOYSA-N furfural Chemical compound O=CC1=CC=CO1 HYBBIBNJHNGZAN-UHFFFAOYSA-N 0.000 description 8
- 235000020200 pasteurised milk Nutrition 0.000 description 7
- 150000001413 amino acids Chemical class 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 6
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- 238000000411 transmission spectrum Methods 0.000 description 5
- 239000007788 liquid Substances 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- IAIWVQXQOWNYOU-FPYGCLRLSA-N nitrofural Chemical compound NC(=O)N\N=C\C1=CC=C([N+]([O-])=O)O1 IAIWVQXQOWNYOU-FPYGCLRLSA-N 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000010438 heat treatment Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 239000000843 powder Substances 0.000 description 3
- 238000010561 standard procedure Methods 0.000 description 3
- 239000004472 Lysine Substances 0.000 description 2
- WUKWITHWXAAZEY-UHFFFAOYSA-L calcium difluoride Chemical compound [F-].[F-].[Ca+2] WUKWITHWXAAZEY-UHFFFAOYSA-L 0.000 description 2
- 229910001634 calcium fluoride Inorganic materials 0.000 description 2
- 230000000052 comparative effect Effects 0.000 description 2
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- JCQLYHFGKNRPGE-FCVZTGTOSA-N lactulose Chemical compound OC[C@H]1O[C@](O)(CO)[C@@H](O)[C@@H]1O[C@H]1[C@H](O)[C@@H](O)[C@@H](O)[C@@H](CO)O1 JCQLYHFGKNRPGE-FCVZTGTOSA-N 0.000 description 2
- 229960000511 lactulose Drugs 0.000 description 2
- PFCRQPBOOFTZGQ-UHFFFAOYSA-N lactulose keto form Natural products OCC(=O)C(O)C(C(O)CO)OC1OC(CO)C(O)C(O)C1O PFCRQPBOOFTZGQ-UHFFFAOYSA-N 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000001320 near-infrared absorption spectroscopy Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- GUBGYTABKSRVRQ-QKKXKWKRSA-N Lactose Natural products OC[C@H]1O[C@@H](O[C@H]2[C@H](O)[C@@H](O)C(O)O[C@@H]2CO)[C@H](O)[C@@H](O)[C@H]1O GUBGYTABKSRVRQ-QKKXKWKRSA-N 0.000 description 1
- 238000005903 acid hydrolysis reaction Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000013351 cheese Nutrition 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 235000020186 condensed milk Nutrition 0.000 description 1
- 235000020247 cow milk Nutrition 0.000 description 1
- 239000006071 cream Substances 0.000 description 1
- PSUFRPOAICRSTC-UHFFFAOYSA-N crispatine Natural products O1C(=O)C(C)C(O)(C)C(C)C(=O)OCC2=CCN3C2C1CC3 PSUFRPOAICRSTC-UHFFFAOYSA-N 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 239000008101 lactose Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 235000016046 other dairy product Nutrition 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 235000020183 skimmed milk Nutrition 0.000 description 1
- 238000011895 specific detection Methods 0.000 description 1
- 230000001954 sterilising effect Effects 0.000 description 1
- 238000004659 sterilization and disinfection Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 235000008939 whole milk Nutrition 0.000 description 1
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- G01—MEASURING; TESTING
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- 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
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
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Abstract
The invention relates to the technical field of detection, in particular to a quantitative detection method of furosine. The quantitative detection method of the furosine comprises the steps of constructing a mid-infrared calibration model of the furosine, scanning a sample to be detected through mid-infrared spectrum to obtain mid-infrared spectrum original data of the sample to be detected, screening characteristic waveband spectrum data of the furosine in the original data, and introducing the characteristic waveband spectrum data into the mid-infrared calibration model to obtain a content result of the furosine in the sample to be detected. The method realizes rapid, simple and convenient quantitative detection of the furosine, greatly reduces the time, reagents and equipment required by detection, and has better repeatability, higher precision and accuracy and better application prospect.
Description
Technical Field
The invention relates to the technical field of detection, in particular to a quantitative detection method of furosine.
Background
Furfurin is a compound produced during the heating of milk. During the heating process of the milk, the amino acid, the protein and the lactose generate epsilon-N deoxidized lactosyl-L-lysine through Maillard reaction, and then the deoxidized lactosyl-L-lysine is converted into more stable furosine through acid hydrolysis. The sterilization mode of the milk product and the condition of adding the reconstituted milk are different, so that the content of the furosine in the milk product is obviously different, therefore, the content of the furosine can be used as an index for evaluating the heat treatment effect of the milk and whether the reconstituted milk is added or not, the furosine is an important index for controlling the quality of the milk, and whether the reconstituted milk is added or not can be judged by measuring the content of lactulose and furosine in the sterilized milk and establishing a model by combining the ratio of lactulose and furosine.
The method commonly used for quantitatively detecting the content of the furfuryl amino acid in the dairy product at present is high performance liquid chromatography or ultra high performance liquid chromatography, more than 10 reagents, a high performance liquid chromatograph or an ultra high performance liquid chromatograph and a Kjeldahl azotometer are needed to be used in the method, the detection time is long, the detection sample needs to be hydrolyzed for 1 day, the operation for detecting the furfuryl amino acid and the protein is carried out for 1 day, and the result is integrally issued for 2 days. The quality control of the online product is carried out by using the method, the result is delayed, and the quality control requirement is difficult to meet. Therefore, it is highly desirable to develop a method for shortening the detection time of the content of the furosine in the dairy product and improving the quality control efficiency of the dairy product.
Disclosure of Invention
The invention aims to provide a construction method of a medium infrared calibration model of furosine. Another object of the present invention is to provide a method for quantitatively detecting furfuryl amino acid.
Furfurin is produced in the processing process of cow milk, but the content of the Furfurin in the dairy product is very low, and is usually 100-200 mg/L. The detection accuracy of the low-content furfuryl amino acid in the dairy product by adopting the high performance liquid chromatography or the ultra-high performance liquid chromatography is higher, but the time consumption is longer. Although the near infrared spectrum technology can detect the content of various components in the dairy products relatively quickly, the near infrared spectrum technology is limited to quantitative detection of some constant indexes, such as fat, protein and other high-content components. For the detection of trace indexes such as furosine and the like, the near infrared spectrum technology is difficult to realize more accurate detection. At present, the mid-infrared spectrum technology is more applied to qualitative detection of substances, has higher detection limit of the qualitative detection, and is generally used for judging whether a product contains a certain limited amount of certain substances. The intermediate infrared spectrum technology is utilized to realize more accurate quantitative detection of substances, and particularly, the quantitative detection of trace substances in dairy products still has great difficulty.
The invention develops a rapid quantitative detection method for trace amount of furaldehyde in dairy products, and in the continuous research and development process, the invention unexpectedly discovers that the intermediate infrared spectrum technology can realize more accurate quantitative detection of the furaldehyde through specific optimization, thereby providing a set of methods for quantitatively detecting the furaldehyde by using the intermediate infrared spectrum technology.
Specifically, the invention provides the following technical scheme:
firstly, the invention provides a construction method of a medium infrared calibration model of furosine, which comprises the following steps:
selecting samples to construct a calibration set (also called a calibration set),
scanning and correcting all concentrated samples by adopting the mid-infrared spectrum to obtain the original data of the mid-infrared spectrum,
and screening the characteristic waveband spectrum data of the furfuryl acid from the original data, performing regression analysis on the characteristic waveband spectrum data of each sample and the corresponding furfuryl acid content reference data by a chemometric method by adopting a Partial Least Squares (PLS) method, and establishing the intermediate infrared calibration model.
For spectral data, in the prior art, preprocessing is usually performed by adopting processing methods such as first-order derivatives and second-order derivatives, however, in the research and development process, it is found that preprocessing a sample can reduce the accuracy of model prediction for a medium-infrared quantitative model of the furosine, and the spectral data is not preprocessed, and the original data is directly adopted to screen the spectral data of the characteristic wave band, so that the prediction capability and the prediction accuracy of the established model are obviously better.
In addition, the modeling method is screened and found, and compared with other methods (MLR, PCA and the like), the model constructed by adopting the partial least square method for the characteristic waveband spectrum data obtained from the original spectrum data has better fitting effect and smaller prediction error.
In the above-mentioned method, the characteristic band of the furosine is 1700-1900cm-1。
Preferably, the sample is a dairy product. The dairy products comprise liquid milk (such as sterilized milk and the like), milk powder (whole milk powder, skim milk powder and the like) and other dairy products (condensed milk, cream, cheese and the like). The dairy product is preferably sterilized milk.
The content of the furosine in the dairy product is low, so that the measurement of the furosine content is easily interfered by other components, and the error of content prediction is increased. According to the invention, mid-infrared spectrum scanning is carried out on the furfuryl acid standard substance and a large amount of dairy product samples, and the fact that the characteristic waveband spectrum data is adopted to construct the correction model is found, so that the interference of other components in the dairy product is reduced, and the accuracy of model prediction is improved.
For the samples used to construct the calibration set, it is preferable to control the concentration of furfuryl acid in the samples of the calibration set to 80-200mg/100g protein, while requiring the distribution of the concentration of furfuryl acid in the samples of the calibration set to conform to a normal distribution.
Abnormal samples may exist in the correction set, so that the accuracy and stability of the model are influenced.
Specifically, samples with a global mahalanobis distance value greater than 3 are rejected by analyzing the global mahalanobis distance of each sample and setting the threshold value of the global mahalanobis distance to 3.
The calculation formula of the global Mahalanobis distance is as follows:
in the formula: MDi-mahalanobis distance of the set sample i is scaled;
ti-the spectral scores of the calibration set sample i;
M-Markov matrix of calibration set samples (Mahalanobis matrix),
t is a calibration set sample spectrum score matrix.
Threshold value of global Mahalanobis Distance (MD) valueL)
In the formula:
-averaging the mahalanobis distances of the calibration set samples;
SDMD-standard deviation of mahalanobis distance of the calibration set samples.
The calculation formula of (a) is as follows:
by using the method to remove abnormal samples in the correction set, the prediction accuracy and stability of the model can be obviously improved.
Preferably, the invention adopts the Unscamber software to construct the intermediate infrared calibration model.
For the parameter settings of the model build, the optimal default settings recommended by the uncramber software may be used.
In the model construction method, the reference data of the content of the furosine is preferably content data obtained by measuring the content of the furosine in the sample by using a high performance liquid chromatography or an ultra high performance liquid chromatography.
Compared with reference data obtained by other detection methods, the detection result of the high performance liquid chromatography or the ultra high performance liquid chromatography has higher accuracy. The method for detecting the furfuryl amino acid by the high performance liquid chromatography or the ultra-high performance liquid chromatography is referred to NY/T939-2016 identification of pasteurized milk and UHT sterilized milk reconstituted milk.
In the above model construction method, preferably, the mid-infrared spectrum scanning is performed at room temperature of 24-28 deg.C and relative humidity of 20-30%, and the scanning range is 4000--1Resolution of scanningIs 4cm-1The number of scans was 32, and each sample was scanned 3 times.
In the invention. The mid-infrared spectral scan is preferably a transmission spectral scan.
The transmission spectrum scan can be based on the milk transmission spectrum of a CaF2 sample cell using a fourier mid-infrared analyzer.
On the basis of the model construction method, the invention also provides a quantitative detection method of the furosine, which comprises the following steps: the method comprises the steps of constructing a medium infrared calibration model of the furosine by the model construction method, scanning a sample to be detected by a medium infrared spectrum to obtain original data of the medium infrared spectrum of the sample to be detected, screening characteristic waveband spectrum data of the furosine in the original data, and leading the characteristic waveband spectrum data into the medium infrared calibration model to obtain a content result of the furosine in the sample to be detected.
Wherein the characteristic wave band of the furosine is 1700-1900cm-1。
The scanning conditions of the mid-infrared spectrum of the sample to be detected are preferably as follows: performing mid-infrared spectrum scanning at room temperature of 24-28 deg.C and relative moisture of 20-30%, wherein the scanning range is 4000-400cm-1Scanning resolution of 4cm-1The number of scans was 32, and each sample was scanned 3 times.
Furthermore, the invention also provides an application of the construction method of the medium infrared calibration model of the furosine or the quantitative detection method of the furosine in the identification of reconstituted milk or the quality control of milk products in dairy products.
The sample to be tested is preferably a dairy product. More preferably sterilized milk. The sterilized milk includes pasteurized milk, UHT sterilized milk, etc.
In the above application, the reconstituted milk is preferably identified in an amount of reconstituted milk added to sterilized milk. The quality control of the dairy product is preferably the quality control of sterilized milk.
The beneficial effects of the invention at least comprise the following:
1. the quantitative detection method for the furosine provided by the invention realizes the rapid and simple quantitative detection of trace furosine by quantitatively analyzing the content of the furosine in a sample by combining the mid-infrared spectrum technology with the partial least square method (PLS). when the method is used for detecting the content of the furosine in a dairy product, compared with the agricultural industry standard method NY/T939-2016 (identification of reconstituted milk in pasteurized milk and UHT sterilized milk), the method shortens the time for outputting the result for 2 days, greatly reduces reagents and equipment required by detection, greatly improves the detection efficiency of the furosine, and is specifically shown in Table 1.
TABLE 1 comparison of different detection methods
2. The quantitative detection method of the furosine provided by the invention has higher accuracy, and compared with a furosine content detection result of an agricultural industry standard method, the quantitative detection method of the furosine has better repeatability, higher precision and accuracy, meets the detection requirement and has better application prospect.
Drawings
FIG. 1 is a multiple regression curve of a mid-infrared quantitative model of furfuryl acid in example 1 of the present invention.
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The spectrometer of the Fourier mid-infrared analyzer FT-A used in the following mutexamples was SP2 from PerkinElmer, USA, based on the milk transmission spectrum of a CaF2 sample cell.
Example 1 construction of mid-Infrared calibration model for Furfurin
The embodiment provides a method for constructing a medium-infrared calibration model of furosine, which comprises the following specific steps:
1. selecting sterilized milk samples (liquid sterilized milk) of different batches, detecting the content of the furosine in each sample by adopting a furosine detection method in NY/T939-2016 (identification of pasteurized milk and reconstituted milk in UHT sterilized milk), selecting 200 samples from the samples, wherein the content of the furosine is 95-200mg/100g of protein, the concentration distribution of all the samples accords with normal distribution in the concentration range, taking the 200 samples as a correction set, and taking the furosine content of each sample as the reference data of the furosine content;
2. scanning 200 samples in the correction set by using a Fourier infrared analyzer FT-A to obtain the original data of the mid-infrared spectrum of each sample, wherein the scanning conditions are as follows: transmission spectrum scanning, performing mid-infrared spectrum scanning at room temperature of 24-28 deg.C and relative moisture of 20-30%, wherein the scanning range is 4000-400cm-1Scanning resolution of 4cm-1The scanning times are 32 times, and each sample is repeatedly scanned for 3 times;
3. screening the spectral data of the characteristic wave band of the furosine in the original data of the mid-infrared spectrum, wherein the characteristic wave band is 1700-1900cm-1Analyzing the Mahalanobis distance and the global Mahalanobis distance of each sample, setting the threshold value of the global Mahalanobis distance to be 3, and rejecting the samples with the global Mahalanobis distance value larger than 3; and performing regression analysis on the characteristic waveband spectrum data of the correction set sample after the abnormal sample is removed and the corresponding reference data of the content of the furosine by adopting an Unscramber software and a Partial Least Squares (PLS) method through a chemometry method, fitting to obtain a multiple regression curve, and establishing a mid-infrared calibration model.
The multiple regression curve obtained by the above fitting is shown in fig. 1.
The above-constructed infrared calibration model of furfuryl acid was verified to have a prediction error (RMSE, also referred to as SEP) of 13.0mg/100g protein and a correlation (RSQ, also referred to as a coefficient of determination, R)2) Is 0.67. The result shows that the correlation between the furfuryl acid content of the established calibration model and mid-infrared spectrum data is good, the prediction accuracy of the model is high, and the model performance is good.
The calculation formula of the prediction error is as follows:
yi-the actual value of sample i;
in the formula:
NIRS detection value for sample i;
np-number of samples collected.
The formula for the correlation is as follows:
yi-the actual value of sample i;
in the formula:
NIRS detection value for sample i;
n-number of samples, calibration set ncTest set is np。
Example 2 quantitative detection of Fulvin in Dairy product samples
The infrared calibration model of the furfuryl acid constructed in the mutexample 1 is introduced into FT-A operation software Results Plus, quantitative prediction analysis is carried out on the furfuryl acid in a blind sample (a sterilized milk sample which does not participate in model establishment), and repeatability and accuracy of the calibration model for predicting the furfuryl acid content are analyzed, and the specific detection method comprises the following steps:
scanning the sterile milk sample to be detected by using a Fourier mid-infrared analyzer FT-A to obtain mid-infrared spectrum original data of each sample, wherein the scanning conditions are as follows: transmission spectrum scanning, performing mid-infrared spectrum scanning at room temperature of 24-28 deg.C and relative moisture of 20-30%, wherein the scanning range is 4000-400cm-1Scanning resolution of 4cm-1The number of scans was 32, and each sample was scanned 3 timesSecondly;
screening the spectral data of the characteristic wave band of the furaldehyde acid in the original number of the mid-infrared spectrum, wherein the characteristic wave band is 1700-1900cm-1And importing the characteristic waveband spectrum data into the intermediate infrared calibration model constructed in the embodiment 1 to obtain the result of the content of the furosine in the sterilized milk sample to be detected.
1. Analysis of repeatability
Selecting 63 sterilized milk samples, detecting each sterilized milk sample for 2 times, and judging the repeatability deviation according to the precision requirement of NY/T939-2016 & ltidentification of reconstituted milk in pasteurized milk and UHT sterilized milk & gt, namely: and taking the absolute difference value of two independent measurement results obtained under the repeatability condition not more than 10% of the arithmetic mean as a judgment standard, and meeting the detection requirement when the judgment standard is met. The results show that 63 sterilized milk samples all meet the detection requirements, and the specific comparative data are shown in table 2.
TABLE 2 results of repeatability analysis
2. Analysis of accuracy
Selecting 40 sterilized milk samples, respectively carrying out mid-infrared spectrum scanning detection and furinic acid high performance liquid chromatography detection in an agricultural industry standard method NY/T939-2016 (identification of reconstituted milk in pasteurized milk and UHT sterilized milk), carrying out deviation analysis on detection results of the two methods, and judging the deviation of the detection results according to the precision requirements of NY/T939-2016 (identification of reconstituted milk in pasteurized milk and UHT sterilized milk), namely: and taking the absolute difference value of two independent measurement results obtained under the repeatability condition not more than 10% of the arithmetic mean as a judgment standard, and if the judgment standard is met, meeting the detection requirement.
The result shows that 40 sterilized milk samples all meet the detection requirements. Specific comparative data are shown in table 3.
TABLE 3 deviation analysis of test results of different test methods
The results show that the quantitative detection method for the furosine has better repeatability and higher accuracy, and can be used for detecting the content of the furosine in products such as dairy products and the like in practice.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. The construction method of the medium infrared calibration model of the furosine is characterized by comprising the following steps:
selecting a sample to construct a calibration set,
scanning and correcting all concentrated samples by adopting the mid-infrared spectrum to obtain the original data of the mid-infrared spectrum,
and screening the characteristic waveband spectrum data of the furosine in the original data, performing regression analysis on the characteristic waveband spectrum data of each sample and the corresponding furosine content reference data by a chemometric method by adopting a partial least square method, and establishing the intermediate infrared calibration model.
2. The method for constructing the mid-infrared calibration model of the furoic acid as claimed in claim 1, wherein the characteristic band of the furoic acid is 1700-1900cm-1。
3. The method for constructing the mid-infrared calibration model of the furfuryl acid according to claim 1 or 2, wherein the concentration of the furfuryl acid in the sample of the calibration set is 80-200mg/100g of protein, and the distribution of the concentration of the furfuryl acid in the sample of the calibration set conforms to a normal distribution.
4. The method for constructing the mid-infrared calibration model of furoic acid as claimed in claim 3, wherein the global mahalanobis distance of each sample is analyzed, the threshold value of the global mahalanobis distance is set to 3, and samples with the global mahalanobis distance value > 3 are rejected.
5. The method for constructing the mid-infrared calibration model of the furosine according to any one of claims 1 to 4, wherein the mid-infrared calibration model is constructed by using Unscramber software.
6. The method for constructing the mid-infrared calibration model of the furfuryl acid according to any one of claims 1 to 5, wherein the reference data of the content of the furfuryl acid is content data obtained by measuring the content of the furfuryl acid in a sample by using a high performance liquid chromatography or an ultra high performance liquid chromatography.
7. The method for constructing the mid-infrared calibration model of furosine as claimed in any one of claims 1 to 6, wherein the mid-infrared spectrum scanning is performed under the conditions of room temperature of 24-28 ℃ and relative moisture and humidity of 20-30%, and the scanning range is 4000-400cm-1Scanning resolution of 4cm-1The number of scans was 32, and each sample was scanned 3 times.
8. The method for constructing a mid-infrared calibration model of furosine according to claim 7, wherein said mid-infrared spectral scan is a transmission spectral scan.
9. A quantitative detection method of furosine is characterized in that a mid-infrared calibration model of furosine is constructed by the method of any one of claims 1-8, mid-infrared spectrum scanning is carried out on a sample to be detected, mid-infrared spectrum original data of the sample to be detected are obtained, characteristic waveband spectrum data of furosine are screened from the original data, and the characteristic waveband spectrum data are led into the mid-infrared calibration model, so that a content result of furosine in the sample to be detected is obtained.
10. Use of the method for constructing a mid-infrared calibration model of furfuryl acid according to any one of claims 1 to 8 or the method for quantitatively detecting furfuryl acid according to claim 9 for reconstituted milk identification or milk quality control.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1804582A (en) * | 2006-01-18 | 2006-07-19 | 中国农业大学 | Method for identifying reductive milk in fresh milk and commodity milk by using near infrared spectrum |
| CN105466882A (en) * | 2015-11-13 | 2016-04-06 | 厦门出入境检验检疫局检验检疫技术中心 | Identification method of single carbohydrate adulteration in raw milk |
| CN108072627A (en) * | 2017-12-08 | 2018-05-25 | 上海海洋大学 | It is a kind of that the method for amino-acid nitrogen and total acid content in soy sauce is quickly detected with middle infrared spectrum |
| CN109307761A (en) * | 2018-10-09 | 2019-02-05 | 华南农业大学 | A kind of indirect competitive ELISA method detecting chaff propylhomoser |
| CN109540838A (en) * | 2019-01-24 | 2019-03-29 | 广东产品质量监督检验研究院（国家质量技术监督局广州电气安全检验所、广东省试验认证研究院、华安实验室） | A kind of method of acidity in quick detection acidified milk |
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2021
- 2021-11-19 CN CN202111391649.7A patent/CN114062298A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1804582A (en) * | 2006-01-18 | 2006-07-19 | 中国农业大学 | Method for identifying reductive milk in fresh milk and commodity milk by using near infrared spectrum |
| CN105466882A (en) * | 2015-11-13 | 2016-04-06 | 厦门出入境检验检疫局检验检疫技术中心 | Identification method of single carbohydrate adulteration in raw milk |
| CN108072627A (en) * | 2017-12-08 | 2018-05-25 | 上海海洋大学 | It is a kind of that the method for amino-acid nitrogen and total acid content in soy sauce is quickly detected with middle infrared spectrum |
| CN109307761A (en) * | 2018-10-09 | 2019-02-05 | 华南农业大学 | A kind of indirect competitive ELISA method detecting chaff propylhomoser |
| CN109540838A (en) * | 2019-01-24 | 2019-03-29 | 广东产品质量监督检验研究院（国家质量技术监督局广州电气安全检验所、广东省试验认证研究院、华安实验室） | A kind of method of acidity in quick detection acidified milk |
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