CN114184571B - Mid-infrared rapid batch detection method for total casein content in milk - Google Patents

Mid-infrared rapid batch detection method for total casein content in milk Download PDF

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CN114184571B
CN114184571B CN202111283548.8A CN202111283548A CN114184571B CN 114184571 B CN114184571 B CN 114184571B CN 202111283548 A CN202111283548 A CN 202111283548A CN 114184571 B CN114184571 B CN 114184571B
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CN114184571A (en
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张淑君
樊懿楷
张静静
李春芳
王海童
南良康
阮健
张依
罗雪路
褚楚
杜超
彭松悦
向世馨
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Huazhong Agricultural University
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Abstract

The invention belongs to the field of dairy cow performance measurement and milk quality detection, and discloses a mid-infrared spectrum rapid batch detection method for total casein in milk. The applicant selects the characteristic wave band for modeling finally by using a manual selection and multiple traversal method during the selection of the characteristic wave band, particularly screens out the absorption region containing partial water, proves that increasing the partial water absorption wave band can improve the accuracy of the model, and simultaneously selects the spectrum MIR measured for the second time by the same milk sample for modeling, thereby improving the accuracy of the model modeled by the first spectrum measurement data. And finally, the optimal combination of the data preprocessing method and the model algorithm is screened out, the optimal parameters are determined, and the accuracy of the model is improved. The method of the invention realizes the rapid, accurate and low-cost detection of the total casein content in the raw milk, and can be widely applied to the dairy cow performance measurement and the milk quality detection.

Description

Mid-infrared rapid batch detection method for total casein content in milk
Technical Field
The invention belongs to the field of dairy cow performance measurement and milk quality detection, and particularly relates to a mid-infrared spectrum rapid batch detection method for total casein in milk.
Background
Milk has been considered as one of the most perfect foods naturally formed in nature, and the milk is rich in nutrients and various in variety, and especially proteins in milk, such as casein, whey proteins and the like, have important effects on human health, and are currently one of the most important nutritional sources for people in China.
Casein is the most predominant constituent of milk protein and accounts for 70% -80% of the milk protein content, and is mainly divided into five subtypes, namely alpha S1 casein (alpha S1-CN), alpha S2 casein (alpha S2-CN), beta casein (beta-CN), gamma casein (gamma-CN) and kappa casein (kappa-CN), but since gamma-CN is a product after beta-CN hydrolysis and is very low in content and is generally only referred to as the degradation degree of beta-CN, researchers rarely conduct intensive studies on gamma-CN, and more related studies on other four types of casein [1] The total casein content used in the present invention is the sum of the above four casein contents.
Casein is closely related to cheese yield and quality. The content of total casein is closely related to the amount of casein micelles, and directly affects the yield of cheese. For cheese and other applications where casein micelles play a critical role in structure and stability, not only the casein content but also the characteristics of the casein micelles determine the processability [2] . Several studies have shown that casein content in milk has no effect on Rennet Clotting Time (RCT), is inversely related to clotting time (k 20), is positively related to firmness (a 30) and Cheese Yield (CY), and that the form of protein micelles is more resistant to proteolysis than the water-soluble form, and that the ratio of micellar phase and soluble phase casein has a great effect on cheese production [3] 。
At present, no national standard exists for casein determination methods, but high performance liquid chromatography is a common method [4-5] ELISA method [6] And the like, but the problems of high cost, low efficiency and the like exist, and the method is difficult to be used in a rapid batch in production practice. Mid-IR (Mid-Infrared Spectroscopy) spectrum technology is a non-standardThe detection tool is economical and efficient, and the property or content of the substance and the property can be predicted by the difference of the frequency of absorption of specific chemical bonds in molecules by middle infrared light and the corresponding wave peaks, so that the information such as the physiological state of the organism can be obtained. In the livestock industry, indexes such as nutrition, physiology and the like of dairy cows are gradually raised in China, and indexes such as total protein and total fat can be detected, but a rapid batch detection model of total casein with high precision is not available at present.
At present, research based on mid-infrared detection of the components of substances in milk is started abroad, but the problems of low precision, inaccurate characteristic wave bands and the like exist [7-8] There is no relevant report in China. Compared with other countries, the method has the advantages that the Chinese cows are influenced by domestic climate, geographical environment, feeding conditions and the like, have larger difference with foreign cows, and the milk quality is also characterized in that a foreign detection model is not necessarily suitable for Chinese cows, so that a method for detecting the total casein content of Chinese cow milk with independent property rights in China needs to be established as soon as possible, the method can be used for rapidly, efficiently, accurately and noninvasively measuring the total casein content of important performance indexes in the lactation traits of the cows, provides phenotypic data and theoretical basis for genetic breeding of the cows, can detect and analyze the total casein content in raw milk and dairy products, and provides references for dairy processing industry and consumers. The invention aims to solve the existing problems, establish a rapid detection technology of total casein and provide technical support for the development of the Chinese milk industry.
Disclosure of Invention
The invention aims to provide a mid-infrared rapid batch detection method for total casein content 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:
the mid-infrared rapid batch detection method for total casein content in milk comprises the following steps:
1. the characteristic wave bands in the infrared spectrum of the collected milk sample are as follows: 952.93cm -1 -1666.66cm -1 、1705.24cm -1 -1755.39cm -1 、1793.97cm -1 -1805.54cm -1 、2735.32cm -1 -2754.61cm -1 And 3580.22cm -1 -3661.24cm -1 MIR data in (a);
2. the MIR data obtained by the measurement was substituted into SG (w=21, p=3) +normalized+ridge regression (α= 0.2245), and the result of predicting the total casein content was outputted.
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. And finally, a characteristic wave band for modeling is selected, particularly an absorption region containing part of water is screened out, and the model accuracy can be improved by increasing the part of water absorption wave band.
2. The average spectrum MIR of the same milk sample measured twice is selected for modeling, and the model accuracy of single spectrum measurement data modeling is improved.
3. The optimal pretreatment and algorithm combination established by the total casein 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 total casein 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 performance measurement of dairy cows and the quality detection of milk.
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 spectrum of the selected five 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.
Detailed Description
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 277 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 collected by 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 samples are slowly shaken to be fully dissolved, ice bags (2-4 ℃) are placed around the milk samples 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 total casein in milk
3.1 instruments, devices and reagents
Electric constant temperature water bath (Wuhan-Hengsu Sujing scientific instruments Co., ltd.); waters liquid chromatograph, including autosampler, column oven, sample bottle, vortex oscillator, needle tube filter, 0.22 μm Ni Long Lvmo, RP-HPLC chromatographic column: ZORBAX 300SB-C18 (250 mm. Times.4.6 mm,5 μm, pore size: 300A).
Milk protein standards such as αs-casein (lot C-6780, purity. Gtoreq.70%), kappa-casein (lot C-0406, purity. Gtoreq.80%), beta-casein (lot C-6905, purity. Gtoreq.90%) were purchased from Sigma company; acetonitrile (chromatographic grade, purity
99.8% or more), guanidine hydrochloride, and trifluoroacetic acid (TFA) from Shanghai Ind; other reagents are all of domestic analytical purity.
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, 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 and preserving, and measuring the content of alpha s-casein, kappa-casein and beta-casein.
3.2.2 high performance liquid chromatography determination of Total Casein content
(1) Treatment of standard samples
The mixed standard sample is fully dissolved by deionized water until the concentration of the alpha s-casein and the kappa-casein is about 10g/L, 400 mu L of the prepared mixed standard sample solution is added into 1600 mu L of treatment solution (6 mol/L guanidine hydrochloride solution), and after being fully and uniformly mixed, the mixed standard sample solution is incubated for 90min at room temperature and is filtered by a 0.22 mu m nylon filter membrane before being put on a machine.
(2) Treatment of milk samples
Adding 80 μl of milk into 320 μl of the processing solution, incubating at room temperature for 90min, setting the rotation speed of the centrifuge to 14000r/min, centrifuging for 5min, and collecting supernatant. Before loading, the mixture was filtered through a 0.22 μm nylon filter.
(3) Chromatographic conditions for RP-HPLC
Chromatographic column: ZORBAX 300SB-C18; sample injection amount: 50 μl; column temperature: 40 ℃; flow rate: 1ml/min; elution time: 42min; detection wavelength: 214nm; phase A: pure water; a phase B; pure acetonitrile.
Mobile phase gradient elution conditions and flow rates
And finally, immediately balancing the chromatographic column for 1min by using an initial gradient, preparing detection of the next sample, and detecting 20-30 samples in each batch. After the detection of the same batch is finished, the chromatographic column is cleaned by 10 percent methanol+90 percent deionized water and 100 percent methanol for maintenance so as to ensure the normal detection of the samples of the next batch.
4. Selection of valid samples
Among 277 samples, invalid data caused by operations such as sample deterioration, loss, abnormal mid-infrared spectrometry, abnormal sample reference value measurement and the like are removed, 13 abnormal samples are removed altogether, and 264 samples are selected for model establishment and optimization.
Example 1:
selection of a predictive model algorithm for total casein:
the purpose of the application is to build a quantitative measurement model of total casein, so a modeling algorithm is used as a regression algorithm. Regression algorithms are of various kinds, and in this example, ridge regression (Ridge) and Partial Least Squares Regression (PLSR) are mainly used [9] The algorithm builds and compares the model for the following reasons:
ridge regression is one of the linear regressions. 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 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 measured twice may improve modeling accuracy, the embodiment models the whole band of the average spectrum MIR of the first time, the second time and the second time respectively, and compares the accuracy of the analysis model, and the results are shown in the following table:
comparing the results by the Ridge algorithm:
PLSR algorithm compares results:
and finally, two average spectrums MIR are selected for modeling through comprehensive consideration of comparison results of two algorithms.
Example 3:
establishment of a method for detecting total casein content in milk by mid-infrared spectrum:
1. partitioning of modeling data sets
In the modeling data set division in this embodiment, 80% is a training set and 20% is a test set. The ratio of training set to test set is 4:1, 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 secondary characteristic preprocessing on 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 primary characteristic preprocessing on the spectrum data, and a sample data set is divided after the primary preprocessing; the partitioned dataset is then subjected to a second pre-treatment, namely Normalization (Normalization) or Normalization (Normalization), respectively.
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 the effect of the partial least squares regression algorithm was overall lower as seen in example 2, so that the ridge regression algorithm was finally selected as the total casein prediction algorithm.
(2) An optimal pre-treatment combination is determined. The sample is subjected to pretreatment in example 3 in the full band of the infrared spectrum, and compared, and the optimal pretreatment combination is determined according to the result: sg+ normalization, results are shown in the table:
the manual selection process of the modeling characteristic band is as follows:
(1) Removed 1593.35cm -1 -1709.1cm -1 And 3059.39cm -1 -3641.95cm -1 Two sections of spectral regions related to water absorption, according to pre-experiments, it was found that preserving part of the spectral regions related to water absorption improves the accuracy of the model, and therefore, in this step, the spectral regions related to water absorption are not completely removed.
(2) Removing 4000cm -1 -5011.54cm -1 Two spectral regions, since this region is a non-mid infrared absorbing region.
(3) Dividing the remaining region into six sections smaller than 1593.35cm -1 Is a first section, greater than 3641.95cm -1 1709.1cm as the last stage -1 And 3059.39cm -1 The middle wave band is equally divided into four sections.
(4) The method comprises the steps of taking 50 wave points as a group, using a ridge 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 for an optimal effect, carrying out similar operation on a second wave band based on the optimal effect, and finally completing first traversal after completing one round of operation on all six 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, five rounds of screening are carried out to obtain the optimal result, as shown in the table:
the final selected characteristic band results are: 952.93cm -1 -1666.66cm -1 、1705.24cm -1 -1755.39cm -1 、1793.97cm -1 -1805.54cm -1 、2735.32cm -1 -2754.61cm -1 And 3580.22cm -1 -3661.24cm -1 Each segment is allowed to have a gap of two wave points. As a result, after a part of the first-stage water absorption region and a part of the second-stage water absorption region are added into the model, the model can achieve the optimal effect, and the characteristic wave band of casein is indicated to comprise a part of the water absorption region.
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 to be 21 according to the comparison result.
The optimal parameter alpha (l 2 regularization degree) of the ridge regression algorithm is determined as follows; 0.2245
The optimal regression model of total casein was, through comparative analysis: SG (w=21, p=3) + normalized+ridge regression (α= 0.2245) model. The correlation coefficients of the training set and the test set are 0.8456 and 0.8661 respectively; the training set and the test set have root mean square errors of 4.7953 and 5.0167, respectively.
Example 4:
the application of the quick batch detection method of the mid-infrared spectrum MIR of the total casein in the milk comprises the following steps:
randomly selected 5 milk samples (one of the non 264 experimental materials) were predicted using the established total casein optimal regression model (SG (w=21, p=3) +normalized+ridge regression (α= 0.2245)) 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: 952.93cm -1 -1666.66cm -1 、1705.24cm -1 -1755.39cm -1 、1793.97cm -1 -1805.54cm -1 、2735.32cm -1 -2754.61cm -1 And 3580.22cm -1 -3661.24cm -1 MIR data in (a);
and simultaneously, detecting the true value of the total casein in the milk in the same batch by utilizing a liquid chromatography.
2. Substituting the MIR data obtained by the measurement into the SG (w=21, p=3) +normalized+ridge regression (α= 0.2245) model constructed in example 3, the prediction result of the total casein content can be output.
The result of the model prediction is very similar to the real result (as shown in the table below), so the model has higher accuracy and can be used for predicting the total casein content of milk.
Reference to the literature
[1] Zhang Junlong analysis of casein results in milk [ J ]. Basic medical forum 2011,15 (16): 494-496.
[2]E.Bijl et al.Protein,casein,and micellar salts in milk:Current content and historical perspectives[J].Journal of Dairy Science,2013,96(9):5455-5464.
[3]G.Bittante and M.Penasa and A.Cecchinato.Invited review:Genetics and modeling of milk coagulation properties[J].Journal of Dairy Science,2012,95(12):6843-6870.
[4] Wang Hao, zhang Zhiguo, chang Yanzhong, duan Xianglin, zhao Shujiang, zhang Nan, dan Zhenhua. RP-HPLC method for the isolation and quantitative determination of major milk proteins in dairy products [ J ]. Food science, 2009,30 (24): 376-380.
[5] Zong, yao Yuze HPLC method for determining casein phosphopeptide content in milk powder and milk products [ J ]. Food industry 2020,41 (10): 295-298.
[6] Ai Zhengwen enzyme-linked immunosorbent assay for detecting A1β -casein in cow milk [ J ]. Food industry 2021,42 (08): 263-266.
[7]McDermott A et al.Cow and environmental factors associated with protein fractions and free amino acids predicted using mid-infrared spectroscopy in bovine milk.[J].Journal of dairy science,2017,100(8):6272-6284.
[8]Frizzarin M.et al.Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods[J].Journal of Dairy Science,2021,104(7):7438-7447.
[9] Sun Daiqing, xie Lirong, zhou, guotao, vehicle hyposensitization SG-MSC-MC-UVE-PLS algorithm based on near infrared spectrum is used in whole blood hemoglobin concentration detection [ J ]. Spectroscopy and spectroscopic analysis, 2021,41 (09): 2754-2758.

Claims (1)

1. The mid-infrared rapid batch detection method for total casein content in milk comprises the following steps:
1) Characteristic wave bands in the infrared spectrum of the collected milk sample are as follows: 952.93cm -1 -1666.66 cm -1 、1705.24 cm -1 -1755.39 cm -1 、1793.97 cm -1 -1805.54 cm -1 、2735.32 cm -1 -2754.61 cm -1 And 3580.22cm -1 -3661.24 cm -1 MIR data in (a);
2) Substituting MIR data obtained by measurement into SG convolution smoothing+standardization+ridge regression, and outputting a prediction result of total casein content; the number of smoothing points of SG convolution smoothing is 21, and the window width is 3; the regularization parameter α of the ridge regression algorithm was 0.2245.
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CN111077214A (en) * 2019-12-31 2020-04-28 北京毅新博创生物科技有限公司 Mass spectrum model for detecting A1 and A2 type β casein in dairy products by mass spectrum and construction method thereof
CN111504942A (en) * 2020-04-26 2020-08-07 长春理工大学 Near infrared spectrum analysis method for improving prediction accuracy of protein in milk
CN113310936A (en) * 2021-05-10 2021-08-27 华中农业大学 Rapid identification method for four high-temperature sterilized commercial milks

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108181399A (en) * 2018-01-23 2018-06-19 新希望乳业股份有限公司 The detection method of A2- beta-casein contents in a kind of dairy products
CN108414628A (en) * 2018-01-23 2018-08-17 新希望双喜乳业（苏州）有限公司 The detection method of A2- beta-caseins in a kind of milk
CN111077214A (en) * 2019-12-31 2020-04-28 北京毅新博创生物科技有限公司 Mass spectrum model for detecting A1 and A2 type β casein in dairy products by mass spectrum and construction method thereof
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