CN114166790B - Mid-infrared rapid batch detection method for content of free methionine in milk - Google Patents
Mid-infrared rapid batch detection method for content of free methionine in milk Download PDFInfo
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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
- G01N21/17—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
- G01N21/3577—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
Abstract
The invention belongs to the field of dairy cow performance measurement and milk quality detection, and discloses a medium infrared rapid batch detection method for the content of free methionine in milk. The applicant breaks through the common algorithm screening features in the aspect of feature band selection, and uses a method of manual selection and multiple traversal. Finally, the characteristic absorption band of methionine (methionine) is selected. The MIR for the first time is determined by comparing and confirming the same milk sample, so that the modeling effect is better, the optimal pretreatment and algorithm combination established by the methionine (methionine) model are selected, the optimal parameters are determined, and the accuracy of the model is improved. Finally, the rapid, accurate and low-cost detection of the methionine (methionine) content in the raw milk is realized.
Description
Technical Field
The invention belongs to the field of dairy cow performance measurement and milk quality detection, and particularly relates to a medium infrared rapid batch detection method for the content of free methionine in milk.
Background
Milk is a well-known and delicious food for human beings, and is rich in various nutrients such as amino acids, unsaturated fatty acids, vitamins, minerals and the like, and particularly various essential amino acids in milk such as methionine (methionine), tryptophan, valine and the like are important for the growth and development of human bodies. Therefore, milk is known as the 'most perfect food' by nutritionist, and is one of the most important nutrition sources for national people.
Methionine (Met) also known as Methionine, of formula C 5 H 11 O 2 NS, an important sulfur-containing essential amino acid in milk, belongs to the aspartic acid family as threonine and lysine. Methionine has been shown to play a vital role in the growth and metabolism of animals, such as enhancing the antioxidant capacity of the body [1] Providing raw materials for protein synthesis and regulating organism immunity [2-3] Regulating intestinal health [4] Detoxication and animal growth promotion [5] Etc. According to the amino acid residuesIn the form, amino acids in milk can be classified into hydrolyzed amino acids and free amino acids, methionine (methionine) is present in both the hydrolyzed amino acids and the free amino acids of milk, and the content used in the present invention is the methionine (methionine) content in the free amino acids of milk, i.e., the methionine (methionine) content in free form.
Methionine (methionine) plays an important role in human health and development, and is of great interest to researchers, so that the technology for detecting methionine (methionine) content in milk has been rapidly developed in recent years and is continuously perfected and optimized. At present, the common method is an automatic amino acid analyzer analysis method [6-8] High performance liquid chromatography [9-10] And the like, although the method can meet the detection requirement of quantitative measurement of the free methionine (methionine) in milk, the defects of complex sample pretreatment, high cost, low efficiency and the like limit the application of the method to a certain extent. The infrared spectrum is the rotation spectrum of molecules or the vibration spectrum of certain functional groups, and the infrared spectrum technology can obtain abundant chemical component information in a sample by determining the molecular structure of substances, is a nondestructive testing technology which is rapidly developed in recent years, and has the characteristics of high sensitivity, high resolution, high computerization, high efficiency and the like [11] At present, mid-infrared spectrum technology is applied to quantitative analysis of indexes such as milk protein, milk fat and the like in milk in China.
In China, the prediction of the nutrient content of milk and the physiological condition of milk cows by a mid-infrared spectrum technology is gradually raised, a model for predicting indexes such as the total protein content, the total fat content and the like in milk is established, but a detection method suitable for the free methionine (methionine) content in milk of Chinese milk cows is not available, and the method is used for rapidly detecting the free methionine (methionine) content in batches, so that the working efficiency of milk quality detection and genetic improvement of milk cows can be obviously improved, the industrial development of milk is promoted, and the method has important theoretical significance and application prospect for stabilizing milk markets and monitoring milk quality. The invention breaks through the technical bottleneck, establishes a rapid batch detection technology for the content of free methionine (methionine) in milk, and provides technical support for low-carbon, healthy and sustainable development of the Chinese milk industry.
Disclosure of Invention
The invention aims to provide a mid-infrared rapid batch detection method for the content of free methionine in milk, which is simple and rapid, and has high accuracy compared with a true value.
In order to achieve the above object, the present invention adopts the following technical measures:
a mid-infrared rapid batch detection method for the content of free methionine in milk comprises the following steps:
1. the characteristic wave bands in the infrared spectrum of the collected milk sample are as follows: 925.92cm -1 -1041.66cm -1 、1277.00cm -1 -1589.50cm -1 、1720.67cm -1 -1932.86cm -1 、2357.24cm -1 -2488.41cm -1 、2635.01cm -1 -2893.50cm -1 And 3638.09cm -1 -4016.18cm -1 MIR data in (a);
2. the MIR data obtained by the measurement was substituted into SG (w=5, p=2) +plsr (n_component=24) model, and the result of predicting the total free amino acid content was outputted.
In the above method, it is preferable that the difference between two wave points is allowed before and after each wave band in step 1.
The protection content of the invention also comprises: the detection method is used for detecting the content of free methionine in milk.
Compared with the prior art, the invention has the advantages that:
1. in the aspect of characteristic band selection, the common method of screening characteristics by using an algorithm is broken, and a manual selection and multiple traversal method is used. Finally, the characteristic absorption band of methionine (methionine) is selected.
2. The MIR for the first time determined by the same milk sample is determined through comparison and confirmation to have better modeling effect, and a reference basis is provided.
3. The optimal pretreatment and algorithm combination established by the methionine (methionine) model is selected, the optimal parameters are determined, and the accuracy of the model is improved.
4. The method realizes the rapid, accurate and low-cost detection of the methionine (methionine) content in the raw milk, realizes the rapid batch detection, only needs 10-15 seconds for the measurement time of each sample, improves the detection efficiency, has stronger practicability, and can be widely applied to the dairy cow performance measurement and the milk quality detection.
Drawings
FIG. 1 shows the infrared spectrum (a) and the average spectrum (b) of an untreated milk sample.
Fig. 2 is an infrared spectrum of the SG pretreated milk sample.
Fig. 3 is a total view of the six selected characteristic bands (a) and an enlarged view of each characteristic band (b).
Fig. 4 is a graph of correlation of model milk data true and predicted values and fitted straight lines.
The specific embodiment is as follows:
the technical scheme of the invention is a conventional scheme in the field unless specifically stated; the reagents or materials, unless otherwise specified, are commercially available.
1. Experimental materials
The test material is derived from 187 Chinese Holstein cows in 9 dairy farms in four regions of China, each cow collects one milk sample, the milk sample collection is completed by an automatic milking device, a disinfected towel is used for wiping a milk house, then an iodized glycerol mixed solution is used for disinfecting breasts, after the front three milks are squeezed out, the milk samples in the whole milking process are collected, each milk sample is 40ml and is packaged into cylindrical brand-new sampling bottles with the diameter of 3.5cm and the height of 9cm, the serial numbers are sequentially carried out, a bromonitropropylene glycol preservative is immediately added into each sampling bottle, the milk sample is slowly shaken to be fully dissolved, an ice bag (2-4 ℃) is placed around the milk sample in the middle of transportation to prevent deterioration, and the samples are immediately subjected to spectrum collection after reaching a laboratory. Two spectral data were collected for all milk samples.
Sample information statistics table
2. mid-IR spectrum measurement and acquisition
Pouring the sample into a cylindrical sample tube with diameter of 3.5cm and height of 9cm, and water-bathing in a water bath at 42deg.C for 15-20min, using Milkoscan from FOSS company TM The 7RM milk component detector stretches the solid optical fiber probe into the liquid, and scans the sample after mixing evenly.
3. Method for detecting true (reference) value of free methionine (methionine) in milk
3.1 instruments, devices and reagents
Full-automatic amino acid analyzer (Sykam S433D, germany); free amino acid analytical column (LCA K07/Li,4.6 mm. Times.150 mm); analyzing free amino acid to remove ammonia column; a needle tube filter; nylon filter membrane of 0.45 μm; a vortex oscillator; a centrifuge; sample injection bottle.
Amino acid standard solution (34 AA, PH free, accession number AA-S000031); ninhydrin (N105629-500 g, ala-dine); buffer A, buffer B, buffer C, and regeneration solution D were purchased from Sykam, germany; liOH×H 2 O; citric acid XH 2 O; HC1 (37% concentration); octanoic acid; sulfosalicylic acid; other reagents are all of domestic analytical purity.
Preparation of buffer A, buffer B, buffer C, regeneration solution D, and sample dilution
Note that:
(1) All solutions were used after filtration using a 0.45 μm membrane;
(2) pH means that the pH can be adjusted with this ingredient;
(3) EDTA is used for complexing heavy metals, and the use of the reagent is not affected;
(4) Octanoic acid is a bacteriostatic agent, and can prolong the storage period of the reagent without adding or affecting the use.
3.2 Experimental methods
3.2.1 acquisition of the mid-IR spectrum
Spectral acquisition was performed using a MilkoScanTM ft+ with specific acquisition steps: placing the milk sample in an electric heating constant temperature water bath kettle at 45 ℃ in batches for preheating for 5min, placing the preheated milk sample on a detection frame, shaking up and down for several times to uniformly mix milk gelatinous solution, placing the detection frame on a detection track, opening a bottle cap, sequentially detecting, placing the milk sample after spectrum collection at-20 ℃ for freezing preservation, and measuring the content of free methionine (methionine) in the subsequent milk.
3.2.2 determination of the free methionine (methionine) content in milk
(1) 100nmol/mL standard solution preparation
100. Mu.L of standard solution (original PH type amino acid standard solution of Sykam Co., ltd., in which the concentration of other amino acids except urea was 10. Mu. Mol/mL was 1. Mu. Mol/mL) was added to 900. Mu.L of sample dilution, and vortexed and mixed well. Before loading, the mixture was filtered through a 0.45 μm nylon filter.
(2) Treatment of milk samples
Sucking about 8ml of milk sample into a centrifuge tube, centrifuging at 3000rpm for 5min (to achieve the aim of separating solid matters), and skipping if suspended matters in the milk sample do not interfere with sampling uniformity;
accurately sucking 1ml of supernatant into another centrifugal test tube after centrifugation, adding 9ml of 2% sulfosalicylic acid, uniformly mixing and standing for 15min;
setting the rotation speed of a centrifugal machine to 3000rpm for centrifugation for 20min or 10000rpm for centrifugation for 10min, and taking supernatant after centrifugation;
before loading, the mixture was filtered through a 0.45 μm nylon filter.
(3) Chromatographic conditions
Chromatographic column: LCA K07/Li
Flow rate: elution pump 0.45 ml/min+derivatization pump 0.25ml/min
Detection wavelength: 570nm+440nm
Reactor temperature: gradient heating at 38-74 DEG C
Column temperature program
Gradient elution conditions of mobile phase
An average of 20 samples are detected in each batch, the standard solution is only needed to be injected once in one batch, and if reagents (mobile phase, ninhydrin and the like) are replaced halfway, the standard solution is needed to be injected again.
4. Selection of valid samples
And (3) removing invalid data caused by operations such as sample deterioration, loss, medium infrared spectrum measurement abnormality, sample reference value measurement abnormality and the like from 187 samples, removing 11 abnormal samples in total, and selecting 176 samples for model establishment and optimization.
Example 1:
selection of a predictive model algorithm for methionine (methionine):
the purpose of the application is to establish a quantitative determination model of methionine (methionine), so a modeling algorithm is used as a regression algorithm. The regression algorithm is of various kinds, and the model is established and compared mainly by using Ridge regression (Ridge) and Partial Least Squares Regression (PLSR) algorithms in this embodiment, for the following reasons:
ridge regression (Ridge algorithm) is one type of linear regression. Only when the algorithm establishes a regression equation, the ridge regression adds the regularized limit, so that the effect of solving the overfitting is achieved. Regularization has two kinds, namely l1 regularization l2 regularization, and the advantage of l2 regularization compared with l1 regularization is that: (1) A random gradient descent can be achieved by cross-validation (2). The ridge regression is one of the basic algorithms which are more commonly used, so that the algorithm is selected as the candidate algorithm in the embodiment.
The partial least squares regression algorithm (PLSR algorithm) is one of the most efficient algorithms in multi-feature samples. In mid-infrared spectrum data, 1060 wave points are corresponding to each sample, and the mid-infrared spectrum data is representative of a multi-characteristic sample. Meanwhile, the partial least square regression algorithm rarely has the over-fitting condition, so that a plurality of researchers of the mid-infrared spectrum can choose to use the partial least square regression algorithm to build a model, and the algorithm is selected as a candidate algorithm in the embodiment.
Example 2:
screening of times of mid-infrared spectrometry and using modes thereof:
all samples used in the method are subjected to spectrum acquisition twice continuously, and the purpose is to screen out the most effective MIR data for modeling by comparing different measurement times of the same sample and the influence of three MIR data (first time, second time and second average) on modeling accuracy. Since researchers believe that the average spectrum MIR of the two determinations may improve modeling accuracy, this embodiment removes the first, second, and second average spectra MIR from the water band and is greater than 4000cm -1 Part of the modeling was performed and the accuracy of the analytical model was compared, with the results shown in the following table:
comparing the results by the Ridge algorithm:
PLSR algorithm compares results:
and finally, the first spectrum MIR is selected for modeling by comprehensively considering comparison results of the two algorithms.
Example 3:
establishment of method for detecting methionine (methionine) content in milk by mid-infrared spectrum:
1. partitioning of modeling data sets
In the modeling data set division in this embodiment, 75% is the training set and 25% is the test set. The ratio of training set to test set is 3:1, meanwhile, the training set is also called a cross-validation set, and 10-fold cross-validation is carried out in the process of training a model.
2. Screening of modeling MIR data preprocessing method
The effective feature screening is a basic operation for processing the spectral data, and aims to eliminate noise and lay a foundation for extracting features. The effective feature screening mainly comprises three types of feature extraction, feature pretreatment and feature dimension reduction. The embodiment mainly carries out characteristic preprocessing on the spectrum data, firstly, five processing methods of SG (convolution smoothing), MSC (multi-component scattering correction), SNV (standard normal variable transformation), diff1 (first order difference) and diff2 (second order difference) are adopted to carry out characteristic preprocessing on the spectrum data,
3. manual selection process and determination of modeling feature bands
The method for selecting the characteristic wave band is quite many and mainly comprises two types of algorithm selection characteristics and manual selection characteristics, and the principle of the algorithm selection characteristics mainly comes from the correlation between each wave point and the reference value. The manual selection of the characteristics has the advantages that the effect of wave bands (namely adjacent wave points) can be enhanced in the selection process, meanwhile, the original information state of the spectrum can be more reserved in the model lifting process, the inclusion and generalization capability are stronger, the wave bands are selected accurately, and the defects of low selection speed and low efficiency are overcome.
The characteristic wave band selection method of the embodiment adopts a manual selection method, and the selection steps are as follows:
(1) The basic algorithm was determined, and example 2 shows that the partial least squares regression algorithm works well and there is no obvious overfitting, so the partial least squares regression algorithm was ultimately chosen as the methionine (methionine) prediction algorithm.
(2) An optimal pretreatment is determined. Removing the water wave band from the sample and more than 4000cm -1 Part of the procedure in example 3Preprocessing and comparing, and determining the optimal preprocessing from the result to be: SG (w=5, p=2), the results are shown in table (fig. 2):
on the basis of determining that MIR data is SG (w=5, p=2) pre-processed, the manual selection procedure of modeling feature bands is as follows:
(1) Removed 1593.35cm -1 -1709.1cm -1 And 3059.39cm -1 -3641.95cm -1 Two spectral regions associated with water absorption.
(2) Removing 4000cm -1 -5011.54cm -1 Two spectral regions, since this region is a non-mid infrared absorbing region.
(3) Dividing the rest region into eight sections smaller than 1593.35cm -1 The region of (2) is divided into two sections, which are larger than 3641.95cm -1 1709.1cm as the last stage -1 And 3059.39cm -1 The middle wave band is equally divided into five sections.
(4) The method comprises the steps of taking 50 wave points as a group, using a partial least square regression algorithm, firstly increasing or decreasing the wave points at two ends of a critical position of a first wave band by one group of wave points, searching the optimal effect, carrying out similar operation on a second wave band based on the optimal effect, and finally completing the first traversal after completing one round of operation on all eight wave bands.
(5) And performing manual traversal for the second time, the third time or more after the first time of traversal is finished until all wave points are not changed, namely the optimal characteristic wave band.
Finally, nine rounds of screening are carried out to obtain the optimal result, and the optimal result is shown in the table:
the final selected characteristic band results are: 925.92cm -1 -1041.66cm -1 、1277.00cm -1 -1589.50cm -1 、1720.67cm -1 -1932.86cm -1 、2357.24cm -1 -2488.41cm -1 、2635.01cm -1 -2893.50cm -1 And 3638.09cm -1 -4016.18cm -1 Each segment is allowed to be separated by two wave points (fig. 3).
4. Model parameter screening determination
The model parameters comprise parameters of a preprocessing method and parameters of an algorithm;
the parameter selection results of the SG convolution smoothing pretreatment in the pretreatment algorithm are compared as follows:
and finally selecting the SG smooth point number of 5 and the window width of 2 according to the comparison result.
Parameters of partial least squares regression algorithm: the main component (n_component), the parameter selection results are compared as follows:
based on the comparison result, the main component (n_component) was finally selected to be 24.
The optimal regression model for methionine (methionine) was, through comparative analysis, as follows: SG (w=5, p=2) +plsr (n_component=24) model. The correlation coefficients of the training set and the test set are 0.8518 and 0.8959 respectively; the training set and the test set have root mean square errors of 1.8814 and 1.7173, respectively.
Example 4:
application of a rapid batch detection method of methionine (methionine) in milk by using a mid-infrared spectrum MIR:
randomly selected 4 milk samples (not one of 176 experimental materials) were predicted using an established methionine (methionine) optimal regression model (SG (w=5, p=2) +plsr (n_component=24)) and the predicted results were compared with the true values.
The model using method comprises the following steps:
1. the characteristic wave bands in the infrared spectrum of the collected milk sample are as follows: 925.92cm -1 -1041.66cm -1 、1277.00cm -1 -1589.50cm -1 、1720.67cm -1 -1932.86cm -1 、2357.24cm -1 -2488.41cm -1 、2635.01cm -1 -2893.50cm -1 And 3638.09cm -1 -4016.18cm -1 MIR data in (a);
meanwhile, the full-automatic amino acid analyzer is used for detecting the real value of methionine (methionine) in the milk in the same batch.
2. The MIR data obtained by the measurement was substituted into the SG (w=5, p=2) +plsr (n_component=24) model constructed in example 3, and the result of the prediction of methionine (methionine) content could be outputted.
The predicted result of the model is very close to the real result (as shown in the following table and fig. 4), so the model has higher accuracy and can be used for predicting the methionine (methionine) content of milk.
| Sample label | Actual value (nmol/ml) | Predictive value (nmol/ml) |
| 1 | 5.215 | 7.443 |
| 2 | 2.625 | 2.626 |
| 3 | 0.104 | 0.399 |
| 4 | 10.802 | 12.688 |
Reference to the literature
[1] Zhao Ruiying, zhao, to Reid, tang Xue, le Guowei, administration of the hydroxy analogue of methionine has an effect on the antioxidant capacity of broiler chickens [ J ]. Amino acids and biological resources, 2012,34 (04): 7-12.
[2]Z Zhou et al.Physiological and molecular mechanisms associated with performance,immunometabolic status,and liver function in transition dairy cows fed rumenprotected methionine or choline[J].Journal of Animal Science,2016,94:22-23.
[3]Tang Yulong,Tan Bie,Xiong Xia,et al.Methionine deficiency reduces autophagy and accelerates death in intestinal epithelial cells infected with enterotoxigenic Escherichia coli.[J].Amino Acids,2015,47(10):2199-204.
[4] In (c), zhao Ruiying, zhao, tangyuan, applied to the meat chicken, glory, le Guowei. Influence of hydroxy methionine analogue on the redox state of the intestinal tract of meat chicken [ J ]. Food industry science and technology, 2012,33 (21): 99-103.
[5] Cold army, tian Juan, chen Bingai, li Xiaoqin, wenhua tilapia comparative study of crystalline methionine and coated methionine utilization [ J ]. Proc. Aquatic biol, 2013,37 (02): 235-242.
[6] Li Yu, zhao Bing, jiang Hui, zhang Jing, tu Yiwei, yao Luxun, gu Wei, zhang Ji donkey milk and milk free amino acid composition analysis research [ J ]. Chinese milk industry, 2017 (10): 68-71.
[7] Wang Jianguang, sun Yujiang, analysis of the nutritional composition of mare's milk in comparison with several milks [ J ]. Food research and development, 2006 (08): 146-149.
[8] Pan analysis of fatty acid and amino acid components in Hubei hybrid buffalo milk and evaluation of nutritive value thereof [ D ]. University of agriculture in China, 2014.
[9]Liang X,Han H,Zhao X,Cao X,Yang M,Tao D,Wu R,Yue X.Quantitative analysis of amino acids in human and bovine colostrum milk samples through iTRAQ labeling.J Sci Food Agric.2018Oct；98(13):5157-5163.doi:10.1002/jsfa.9032.PMID:29577310.
[10] Guan Boyuan, zhang Zhenghan, dan Jiaxin, shi Xu, yueXetiqing, waxberry, comparison of full spectrum free amino acids and hydrolyzed amino acids in human normal milk with bovine normal milk [ J ]. Food science, 2019,40 (10): 193-198.
[11] Yao Sen, zhang Ji, liu Honggao, li Jieqing, wang Yuanzhong application of infrared spectroscopic techniques in edible fungi research [ J ]. Food science, 2018,39 (01): 305-312.
Claims (3)
1. A mid-infrared rapid batch detection method for the content of free methionine in milk comprises the following steps:
1) The characteristic wave bands in the infrared spectrum of the collected milk sample are as follows: 925.92cm -1 -1041.66 cm -1 、1277.00 cm -1 -1589.50 cm -1 、1720.67 cm -1 -1932.86 cm -1 、2357.24 cm -1 -2488.41 cm -1 、2635.01 cm -1 -2893.50 cm -1 And 3638.09cm -1 -4016.18 cm -1 MIR data in (a);
2) Substituting MIR data obtained by measurement into a SG convolution smoothing and partial least squares regression PLSR model, and outputting a prediction result of free methionine content; the number of smoothing points of SG convolution smoothing is 5, and the window width is 2; the principal component of partial least squares regression PLSR was 24.
2. The method according to claim 1, characterized in that: the front and back of each wave band in the step 1) are allowed to have the difference of two wave points.
3. Use of the method according to claim 1 for detecting the free methionine content in milk.
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| CN104819953A (en) * | 2015-04-21 | 2015-08-05 | 通威股份有限公司 | DL-methionine rapid detecting method based on near-infrared spectroscopy |
| CN109164201A (en) * | 2018-10-25 | 2019-01-08 | 吕梁学院 | The extraction purification and the method for inspection of selenomethionine in a kind of BEIQI MUSHROOM |
| CN111007034A (en) * | 2019-12-16 | 2020-04-14 | 贵州医科大学 | Method for detecting content of amino acid in hereditary metabolic disease |
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| CN104819953A (en) * | 2015-04-21 | 2015-08-05 | 通威股份有限公司 | DL-methionine rapid detecting method based on near-infrared spectroscopy |
| CN109164201A (en) * | 2018-10-25 | 2019-01-08 | 吕梁学院 | The extraction purification and the method for inspection of selenomethionine in a kind of BEIQI MUSHROOM |
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