NL2029011B1 - Method for identifying cow milk and horse milk using Mid-infrared spectrum MIR - Google Patents
Method for identifying cow milk and horse milk using Mid-infrared spectrum MIR Download PDFInfo
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- 235000020247 cow milk Nutrition 0.000 title claims abstract description 39
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims abstract description 18
- 235000020252 horse milk Nutrition 0.000 title abstract description 36
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 22
- 238000012549 training Methods 0.000 claims abstract description 21
- 238000012360 testing method Methods 0.000 claims abstract description 19
- 238000001228 spectrum Methods 0.000 claims abstract description 12
- 230000035945 sensitivity Effects 0.000 claims abstract description 6
- 238000007781 pre-processing Methods 0.000 claims abstract description 5
- 238000005070 sampling Methods 0.000 claims abstract description 5
- 238000002790 cross-validation Methods 0.000 claims abstract description 4
- 235000013336 milk Nutrition 0.000 claims description 16
- 239000008267 milk Substances 0.000 claims description 16
- 210000004080 milk Anatomy 0.000 claims description 16
- 238000007637 random forest analysis Methods 0.000 claims description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 6
- 239000007788 liquid Substances 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 239000000523 sample Substances 0.000 claims description 6
- 238000002834 transmittance Methods 0.000 claims description 5
- 238000002835 absorbance Methods 0.000 claims description 4
- 238000010521 absorption reaction Methods 0.000 claims description 4
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 2
- 239000013307 optical fiber Substances 0.000 claims description 2
- 239000007787 solid Substances 0.000 claims description 2
- 239000013598 vector Substances 0.000 claims description 2
- 230000005540 biological transmission Effects 0.000 claims 2
- 230000001537 neural effect Effects 0.000 claims 1
- 238000004476 mid-IR spectroscopy Methods 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 abstract description 2
- 238000012706 support-vector machine Methods 0.000 description 10
- 108090000623 proteins and genes Proteins 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
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- 241000283690 Bos taurus Species 0.000 description 1
- 208000035150 Hypercholesterolemia Diseases 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
- 238000004497 NIR spectroscopy Methods 0.000 description 1
- 241000293869 Salmonella enterica subsp. enterica serovar Typhimurium Species 0.000 description 1
- 230000000172 allergic effect Effects 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
- 230000000845 anti-microbial effect Effects 0.000 description 1
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- 238000007796 conventional method Methods 0.000 description 1
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- 235000014113 dietary fatty acids Nutrition 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
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- 150000004665 fatty acids Chemical class 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000002496 gastric effect Effects 0.000 description 1
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- 238000004128 high performance liquid chromatography Methods 0.000 description 1
- 235000020256 human milk Nutrition 0.000 description 1
- 210000004251 human milk Anatomy 0.000 description 1
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- 238000013507 mapping Methods 0.000 description 1
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- 235000021122 unsaturated fatty acids Nutrition 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
- 229940088594 vitamin Drugs 0.000 description 1
- 229930003231 vitamin Natural products 0.000 description 1
- 235000013343 vitamin Nutrition 0.000 description 1
- 239000011782 vitamin Substances 0.000 description 1
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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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/02—Food
- G01N33/04—Dairy products
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/129—Using chemometrical methods
- G01N2201/1293—Using chemometrical methods resolving multicomponent spectra
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Abstract
The present disclosure relates to a method for quickly identifying cow milk and horse milk, which is related to mid—infrared spectroscopy analysis. The method comprises the following steps: (1) selecting fresh cow milk and fresh 5 horse milk samples; (2) scanning the samples in the mid- infrared spectrum range to obtain mid—infrared spectrum data; (3) preprocessing' the original mid—infrared. spectrum. to remove outliers; (4) dividing the preprocessed data set into a training set and a test set according to the principle of 10 stratified sampling; (5) reducing the dimension of the training set to improve the speed of model training; (6) using K Nearest Neighbor (KNN) algorithm and other algorithms on the training set to build models through lO— fold cross—validation that are able to distinguish between 15 cow milk and horse milk, and accuracy, specificity, sensitivity and AUC were used to evaluate and filter the models; (7) predicting the generalization ability of the optimal model.
Description
Method for identifying cow milk and horse milk using Mid- infrared spectrum MIR
Field
The invention belongs to the field of milk product analysis technology, and in particular, relates to a rapid method for identifying cow milk and horse milk.
The present invention is related to the field of analyzing the components of dairy products using infrared spectrum.
Background
The contents and proportions of protein, amino acids,
lactose, minerals and other ingredients in horse milk are close to those in breast milk, which is easier to be absorbed by infants and young children and causes reduced allergic symptoms 14% Further, in the horse milk, abundant vitamins and minerals are involved in human metabolism, and have the effects of regulating human physiological functions, improving human immunity and preventing and treating diseases.
Unsaturated fatty acids and low-molecular fatty acids are effective in preventing hypercholesterolemia and arteriosclerosis.
Free glycans in horse milk have a wide range of biological effects, with potential prebiotics, antimicrobial, anti-adhesion and immunomodulatory activities.
In particular, free glycans are able to promote the growth of beneficial microorganisms in intestinal tract, which are extremely valuable to human health !®., Studies have found that horse milk can regulate the expression of Salmonella typhimurium pathogenic genes and has anti- reproductive effects on Caco-2 cells, which provides new evidence to demonstrate horse milk can promote gastrointestinal health [5},
At present, the methods for determining the components of cow milk and horse milk include high performance liquid chromatography (HPLC), gas chromatography (GC), Coomassie brilliant blue-ultraviolet detection method 3, near infrared (NIR) spectroscopy 13 and Mid-infrared (MIR) spectroscopy, etc. Based on the D-LO0OP gene of horse mitochondria and the 16S-RNA gene of bovine mitochondria, Lu Deng et al. designed specific primers for double-stranded PCR which can detect 0.1% cow milk mixed in horse milk [71 But there is no method to quickly and accurately identify a batch of cow milk and horse milk. Mid-infrared spectroscopy is a rapid, non-destructive, non-polluted, modern technology that can analyze multiple ingredients at the same time, which has been quickly developed in recent years and widely used in the quality inspection of agricultural products and food. However, there are no related research and literature reports on the identification of cow milk and horse milk by mid-infrared spectrum.
Summary To overcome the defects in the conventional art, the present closure provides a method for quickly identifying a batch of cow milk and horse milk based on Fourier transform mid-infrared spectrum, which features rapid, high precision, low cost, simple operation, batch identification and strong practicability.
The present closure is implemented by the solutions as follows, A method for quickly identifying cow milk and horse milk includes the following steps: Step 1 collecting samples of fresh cow milk and fresh horse milk, respectively; Step 2 collecting mid-infrared spectrum of samples in Step 1, the samples of cow milk and horse milk are respectively poured into a cylindrical sampling tube with a diameter of
3.5 cm and a height of 98 cm, the height of the liquid level is ensured greater than 6cm, after heating in a water bath at 42°C for 15-20 minutes, a solid optical fiber probe is extended into the liquid to absorb samples for detection, a mid-infrared spectrometer is used to scan the samples of cow milk and horse milk in the wave number range of 4000-400cm 1, and the corresponding light transmittance of the samples is output through a connected computer to obtain a sample spectrum; Step 3, preprocessing the collected original mid-infrared spectrum data by converting the spectrum data from transmittance (T) to absorbance (A) according to A=log:a(1/T) at first, then removing the absorption area of water, and detecting spectrum anomaly using LOF (Local Outlier Factor) to remove outliers at last; Step 4, dividing the data set into a training set and a test set; Step 5, reducing dimension of principal components of the training set by selecting the number of principal components when the cumulative explained variance ratio is greater than
99.9%; Step 6, building and filtering models: with the mid- infrared spectrum of the milk samples in the training set as the input value, and the categories of cow milk and horse milk as the output value, K Nearest Neighbor (KNN) algorithm, BP neural network algorithm, random forest (RF) algorithm and support vector machine (SVM) algorithm are followed to build models on the training set through 10-fold cross- validation for comparison, and accuracy, specificity, sensitivity and AUC indicators are used to evaluate and filter the models; Step 7, evaluating the generalization ability of the optimal model by predicting the samples in test set, the corresponding evaluation indicators are used to evaluate the model's performance on the test set, and a confusion matrix is used to refine the performance of the model on the test set; Step 8, realizing the above-mentioned preprocessing the mid-infrared spectrum data, building and validating models, and outputting confusion matrix using Python 3.8.3.
Brief description of the drawings FIG 1. is a mid-infrared spectrum of horse milk after anomaly detection processing.
FIG 2. is a mid-infrared spectrum of cow milk after anomaly detection processing.
FIG 3. is a comparison diagram of mid-infrared spectrums of cow milk and horse milk after anomaly detection processing.
FIG 4. is a TSNE visualization diagram of the preprocessed data.
FIG 5. is a ROC curve of the model on the test set.
FIG 6. is a confusion matrix diagram of the model on the test set.
Detailed description The present disclosure will be further described in detail below in conjunction with the drawing and the embodiment.
The present disclosure provides a method for quickly identifying cow milk and horse milk, which includes the following specific steps. The experimental methods mentioned in the examples without specifying specific conditions shall be carried out in accordance with conventional methods and conditions, or in accordance with the conditions recommended by the manufacturer (such as product instruction manual).
Example 1 Instruments and equipment: Milkoscan MRM milk composition detector produced by FOSS Analytical (according to the product instruction manual). The operation steps were provided as follows:
1. 60 samples of fresh horse milk and 60 samples of fresh cow milk were collected, respectively;
2. The samples of cow milk and horse milk were respectively poured into a cylindrical sampling tube with a diameter of
3.5 cm and a height of 9 cm, the height of the liquid level was ensured greater than 6cm, after heating in a water bath at 42°C for 15-20 minutes, a Milkoscan "RM milk composition detector produced by FOSS Analytical was extended into the liquid to absorb samples for detection, a mid-infrared spectrometer was used to scan the samples of cow milk and horse milk in the wave number range of 4000-400cmt, and the 5 corresponding light transmittance of the samples was output through a connected computer to obtain a sample spectrum.
3. The collected original mid-infrared spectrum data were preprocessed by converting the spectrum data from transmittance (T) to absorbance (A) according to A=logig (1/T) at first, then removing the absorption area of water, and detecting spectrum anomaly using LOF (Local Outlier Factor) to remove outliers at last;
4. The data set was divided into training set and test set by stratified sampling method. The training set comprising mid-infrared spectrum data of 92 samples was used for building a qualitative discrimination model, and the test set comprising mid-infrared spectrum data of 24 samples was used to evaluate the prediction effect of the qualitative discrimination model.
5. The dimension of principal components of the training set was reduced: the number of principal components was selected when the cumulative explained variance ratio was greater than 99.9% to obtain 16 principal components.
6. With the mid-infrared spectrum of the milk samples in the training set as the input value, and the categories of cow milk and horse milk as the output value, K Nearest Neighbor (KNN) algorithm, BP neural network algorithm, random forest (RF) algorithm and support vector machine (SVM) algorithm are followed to build models on the training set through 10-fold cross-validation; The accuracy, specificity, sensitivity and AUC of the models built by the four algorithms were all 1 on the training set, indicating that these four algorithms had good modeling effects on the identification of cow milk and horse milk, and all four models were able to accurately identifying cow milk and horse milk. Generally speaking, the SVM algorithm had a good performance in various classification problems. The SVM algorithm was based on nonlinear mapping and was a novel small-sample learning method; in the SVM classification decision, a small number of support vectors played a decisive role, which avoided the possibility of "dimensional b disaster" to a certain extent and make the training model have better stability. Therefore, the models built by the SVM algorithm was chosen for the identification of cow milk and horse milk.
7. The generalization ability of the optimal model was evaluated by predicting 24 samples in test set. The results represented by accuracy, specificity, sensitivity and AUC indicate the predicted value of the mid-infrared spectrum obtained was consistent with the actual value. The accuracy, specificity, sensitivity and AUC of the model on the test set were all 1, indicating that the SVM algorithm had a high learning ability for classifying cow milk and horse milk, and the built model can accurately identify between cow milk and horse milk. FIG 6 used a confusion matrix to represent the performance of the model on the test set. Comparing the classification results with the actual values, no error classification can be seen from the figure, showing that the model can correctly classify cow milk and horse milk with high accuracy.
Table 1. The performance of the models on the training set and test set Model evaluation indicators Algorithm Data set
The invention has high learning ability, and the built model can accurately identify cow milk and horse milk. FIG 6 uses the confusion matrix to represent the performance of the model on the test set, comparing the classification results with the actual value, no error classification can be seen from the figure, showing that the model can accurately classify cow milk and horse milk with high accuracy.
Main references
[1] Liu Zhian. Study of fresh mare’s milk nutritional quality of grazing Yili horse [D]. Xinjiang Agricultural University, 2014;
[2] Wang Shuyang, etc., Determination and analysis of fatty acids in fresh camel milk [J]. Journal of Gansu Agricultural University, 2011, 46(01): 127-132;
[3] Yang Jinhui, Characteristics of near-infrared spectrum, fatty acids and protein composition of milk from different dairy animals [D]. Chinese Academy of Agricultural Sciences, 2013.
[4] AKBAR NIKKHAH. Equidae milk promises substitutes for cow and human breast milk[J]. turkish journal of veterinary & animal sciences, 2012, 36(5):470-475;
[5] Guri Anilda,Paligot Michele, Crèvecoeur Sebastien, et al. In vitro screening of mare's milk antimicrobial effect and antiproliverative activity. [J]. FEMS microbiology letters, 2016,363(2);
[6] Karav S , Salcedo J, Frese S A, et al. Thoroughbred mare's milk exhibits a unique and diverse free oligosaccharide profile[J]. FEBS Open Bio, 2018, 8;
[7] Lu Deng,Aili Li, Yang Gao, et, al. Detection of the Bovine Milk Adulterated in Camel, Horse, and Goat Milk Using Duplex PCRIJ]. Springer US,2020,13(2);
[8] Malacarne M , Martuzzi FF, Summer A, et al. Protein and fat composition of mare's milk: Some nutritional remarks with reference to human and cow's milk[J].
a International Dairy Journal, 2002, 12(11):869-877.
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| CN112666111A (en) * | 2020-10-01 | 2021-04-16 | 华中农业大学 | Method for quickly identifying milk and mare 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 |
| CN113310930A (en) * | 2021-05-10 | 2021-08-27 | 华中农业大学 | Spectral identification method of high-temperature sterilized milk, pasteurized milk and pasteurized milk mixed with high-temperature sterilized milk |
| CN113310934A (en) * | 2021-05-10 | 2021-08-27 | 华中农业大学 | Method for quickly identifying milk cow milk mixed in camel milk and mixing proportion thereof |
| CN113310928A (en) * | 2021-05-10 | 2021-08-27 | 华中农业大学 | Method for rapidly identifying high-temperature sterilized milk with shelf life within and out of date |
| CN113310932A (en) * | 2021-05-10 | 2021-08-27 | 华中农业大学 | Rapid identification method for adding high-temperature sterilized milk into pasteurized buffalo fresh milk |
| CN113310937A (en) * | 2021-05-10 | 2021-08-27 | 华中农业大学 | Method for rapidly identifying high-temperature sterilized milk, pasteurized fresh milk of dairy cow and reconstituted milk of milk powder |
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| CA2351943A1 (en) * | 1998-11-19 | 2000-06-02 | Ppl Therapeutics (Scotland) Limited | Stabilisation of milk from transgenic animals |
| JP2004008073A (en) * | 2002-06-06 | 2004-01-15 | Ajinomoto Co Inc | Method for preventing reduction in mammalian breast milk amount |
| WO2009020858A1 (en) * | 2007-08-08 | 2009-02-12 | Jon Baker | Milk production in a controlled environment |
| CN101929951B (en) * | 2009-06-19 | 2012-06-27 | 西北农林科技大学 | Method for distinguishing milk doped with ewe's milk by near infrared spectrum |
| CN103543123A (en) * | 2013-10-08 | 2014-01-29 | 江南大学 | Infrared spectrum recognition method for adulterated milk |
| CA2839027A1 (en) * | 2014-01-02 | 2015-07-02 | Alltech, Inc. | Systems and methods for estimating feed efficiency and carbon footprint for milk producing animal |
| CN107024450A (en) * | 2017-03-27 | 2017-08-08 | 云南小宝科技有限公司 | A kind of method for differentiating different brands and hop count milk powder based on near-infrared spectrum technique |
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| CN112525850A (en) * | 2020-10-01 | 2021-03-19 | 华中农业大学 | Spectral fingerprint identification method for milk, mare, camel, goat and buffalo milk |
| CN112666111A (en) * | 2020-10-01 | 2021-04-16 | 华中农业大学 | Method for quickly identifying milk and mare milk |
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