CN112666110A - Spectral identification model and method for milk and goat milk - Google Patents
Spectral identification model and method for milk and goat milk Download PDFInfo
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Abstract
The invention belongs to the field of milk product analysis, and particularly relates to a spectral identification model and a spectral identification method for milk and goat milk. Related to mid-infrared spectroscopy. The method comprises the following steps: 1) taking fresh goat milk and cow milk samples; 2) scanning the sample in the middle red spectrum range to obtain middle infrared spectrum data; 3) preprocessing the original mid-infrared spectrum to remove abnormal values; 4) dividing the preprocessed data set into a training set and a test set according to a layered sampling principle; 5) carrying out dimension reduction processing on the training set to improve the model training speed; 6) on a training set, a model for distinguishing mare milk and milk is established through 10-fold cross validation by using algorithms such as nearest neighbor and the like, and the model is evaluated and screened by using accuracy, specificity, sensitivity and AUC; 7) and predicting the generalization performance of the optimal model.
Description
Technical Field
The invention belongs to the technical field of milk product analysis, and particularly relates to a spectrum identification model and a method for identifying milk and goat milk. The invention relates to the field of analysis for identifying milk product components by infrared spectroscopy.
Background
The goat milk contains milk fat and milk protein which are higher than those of cow milk, and contains a plurality of essential amino acids; the content of medium-chain fatty acid is twice of that of cow milk, and is more easily digested and absorbed by the attack of human body lipase, and the volume of its fat globules is only one third of that of cow milk, so that the goat milk can be digested more quickly than cow milk[2](ii) a The content of Epidermal Growth Factor (EGF) is removedThe content of breast milk and the like and a few of milks is the highest, which is 8 times as much as that of milk, and the milk has an auxiliary treatment effect on allergic skin. Protein in goat milk meeting compositional standards is suitable as a protein source for infant and follow-on formulas[5]。
At present, methods for measuring the components of goat and milk include High Performance Liquid Chromatography (HPLC), Gas Chromatography (GC), Coomassie brilliant blue-ultraviolet detection, and Near Infrared (NIR) spectroscopy[3]And mid-infrared (MIR) spectroscopy, and the like. Martin et al developed a Polymerase Chain Reaction (PCR) method, designed specific primers for 12S RNA mitochondrial gene, to achieve the discrimination of goat milk and cow milk with a sensitivity of 0.1%[4](ii) a The method uses a guide ring isothermal amplification technology to detect the milk in goat milk, such as littin, and the like, and the sensitivity of the method can reach 1 percent, and the concentration is about 0.1ng/mL[1]. However, there is no method for rapidly and accurately identifying goat milk and cow milk in batches.
Mid-infrared spectral analysis is a modern technology which is rapidly developed in recent years, is free from damage and pollution and can simultaneously analyze multiple components, is widely applied to quality detection of agricultural products and food, but related research and literature reports of mid-infrared spectral identification of goat milk and cow milk do not exist.
Disclosure of Invention
The invention aims to solve the technical problem of providing a fast batch identification method of goat milk and cow milk based on Fourier transform mid-infrared spectrum, which has the characteristics of high identification speed, high precision, low cost, simple operation, batch detection and strong practicability.
The technical scheme of the invention is as follows:
the spectral discrimination model and method of milk and goat milk comprises the following steps:
(1) selecting a milk sample: samples of fresh goat milk and fresh cow milk were collected separately.
(2) Collecting mid-infrared spectrum by pouring goat milk and cow milk sample into cylindrical sampling tubes with diameter of 3.5cm and height of 9cm respectively to ensure liquid level height of more than 6cm, and placing in 42 deg.C water bathWater bath for 15-20min, extending the solid fiber probe into the liquid, sucking sample, and detecting by using a mid-infrared spectrometer at 4000--1Scanning goat milk and cow milk samples in a wave number range, and outputting light transmittance corresponding to the samples through a computer connected with the goat milk and cow milk samples to obtain a sample spectrogram;
(3) preprocessing the collected original mid-infrared spectrum data, comprising the following steps:
converting the spectral data from transmittance (T) to absorbance (a):
A＝log10(1/T)；
removing the water absorption area;
detecting abnormal spectrum: removing abnormal values by using an LOF (loss of tolerance) abnormality detection algorithm;
(4) dividing the data set into a training set and a testing set;
(5) and (3) reducing the dimensionality of the main components of the training set: selecting the main component number when the accumulative variance interpretation rate is more than 99.9%;
(6) establishing and screening a model: the method comprises the following steps of taking the mid-infrared spectrum of a milk sample of a training set as an input value, taking the categories of goat milk and cow milk as output values, using a nearest neighbor (KNN) algorithm, a BP neural network algorithm, a Random Forest (RF) algorithm and a Support Vector Machine (SVM) algorithm, constructing a model on the training set through 10-fold cross validation, and evaluating and screening the model according to the principle that indexes such as accuracy, specificity, sensitivity and AUC are high;
(7) estimating the generalization performance of the optimal model: predicting samples in the test set by using the model, and evaluating the performance of the model on the test set by using the corresponding evaluation index; the behavior of the model on the test set is refined using a confusion matrix.
(8) The preprocessing of the mid-infrared spectrum data, the model construction and verification and the output of the confusion matrix are realized by Python 3.8.3.
Drawings
FIG. 1: and (5) detecting the intermediate infrared spectrogram of the processed goat milk.
FIG. 2: and (5) detecting the intermediate infrared spectrogram of the treated milk of the cow after the abnormality detection.
FIG. 3: and (4) comparing infrared spectra of the goat milk and the cow milk after anomaly detection treatment.
FIG. 4: and (4) carrying out TSNE visualization on the preprocessed data.
FIG. 5: ROC curve of model on test set.
FIG. 6: a confusion matrix map of the model on the test set.
Detailed Description
The invention is further illustrated by the following figures and examples.
Examples the experimental procedures, in which specific conditions are not specified, were carried out according to conventional methods and conditions, or according to conditions recommended by the manufacturers.
Example 1
Instruments and equipment:
MilkoScan manufactured by FOSS CorpTM7RM milk ingredient detector (according to the product instruction).
The operation steps are as follows:
1) selecting a milk sample:
73 samples of fresh goat milk and 80 samples of fresh cow's milk were collected, respectively.
2) And (3) collecting the mid-infrared spectrum:
respectively pouring goat milk and cow milk samples into cylindrical sampling tubes with diameter of 3.5cm and height of 9cm to ensure liquid level height greater than 6cm, placing in water bath at 42 deg.C for 15-20min, extending solid fiber probe into liquid, and detecting by absorbing sample with MilkoScan from FOSS companyTMThe 7RM milk component detector is at 4000-400cm-1And scanning goat milk and cow milk samples in the wave number range, and outputting light transmittance corresponding to the samples through a computer connected with the goat milk and cow milk samples to obtain a sample spectrogram.
3) Data preprocessing:
converting the spectral data from transmittance (T) to absorbance (A), and calculating the formula as follows:
A＝log10(1/T)
removing the water absorption area;
detecting abnormal spectrum: the 4 outliers were removed using the LOF anomaly detection algorithm.
4) Dividing the data set:
and a data set is divided into a training set and a testing set by adopting a hierarchical sampling method, wherein the mid-infrared spectrum data of 119 samples in the training set is used for establishing a qualitative judgment model, and the mid-infrared spectrum data of 30 samples in the testing set is used for evaluating the prediction effect of the qualitative judgment model.
5) And (3) reducing the dimensionality of the main components of the training set:
and selecting the number of the principal components with the accumulated variance interpretation rate of more than 99.9 percent to obtain 16 principal components.
6) Establishing and screening a model:
constructing a model on a training set through 10-fold cross validation by using a nearest neighbor (KNN) algorithm, a BP neural network algorithm, a Random Forest (RF) algorithm and a Support Vector Machine (SVM) algorithm and taking the mid-infrared spectrum of a milk sample of the training set as an input value and the categories of goat milk and cow milk as output values; the accuracy, specificity, sensitivity and AUC of the model constructed by the 4 algorithms on the training set are all 1, which shows that the 4 algorithms have good modeling effect on the identification of goat milk and cow milk, and the 4 models can accurately identify the goat milk and the cow milk. Generally, SVM algorithms perform well in a variety of classification problems. The SVM algorithm is based on nonlinear mapping and is a novel small sample learning method; in SVM classification decision, a few support vectors play a decisive role, which avoids the possibility of occurrence of 'dimension disaster' to a certain extent and enables the trained model to have better stability. Therefore, a model established by using an SVM algorithm is selected for the identification of goat milk and cow milk.
7) And (3) model verification:
predicting and verifying 30 concentrated samples by using a qualitative model established by an SVM algorithm, wherein the result is expressed by indexes such as accuracy, specificity, sensitivity, AUC and the like; the predicted value of the obtained mid-infrared spectrum is consistent with the actual value. The accuracy, specificity, sensitivity and AUC of the model in the test set are all 1, which shows that the classification of the goat milk and the milk by the SVM algorithm has high learning ability, and the established model can accurately distinguish the goat milk and the milk. The confusion matrix is used for representing the performance of the model in the test set by using FIG. 6, the classification result is compared with the actual value, and the situation of no error classification can be seen in the graph, which shows that the model can accurately classify the goat milk and the cow milk with high precision.
TABLE 1 Performance of the model on the training and test sets
Primary references
[1] Detection of milk component [ J ] in goat milk by loop-mediated isothermal amplification technology, such as Liting, et al, biotechnological communication, 2018,29(06):836-839
[2] Schuiping et al, the way of improving the nutritional components and the nutritional value of goat milk [ J ]. Heilongjiang animal veterinarians, 2017(09): 118-;
[3] yangtui, research on near infrared spectra of milk from different dairy animals and composition characteristics of fatty acid and milk protein [ D ]. Chinese academy of agricultural sciences, 2013.
[4]Irene Martin,Teresa Garcia,Violeta Fajardo,et,al.Species-specific PCR for the identification of ruminant species in feedstuffs[J].meat science,2007,75(1):120-127.
[5]Turck D.Cow's milk and goat's milk.[J].World Rev Nutr Diet,2013,108:56-62。
Claims (6)
1. The spectral identification model and method of milk and goat milk are characterized by comprising the following steps:
1) selecting milk samples
Respectively collecting fresh goat milk and fresh milk;
2) mid infrared spectral collection
Adopting a Fourier transform mid-infrared spectrometer at 4000--1Scanning goat milk and cow milk in a wave number range, and outputting light transmittance corresponding to a sample through a connected computer to obtain a sample spectrogram;
3) data pre-processing
Converting the light transmittance into absorbance, removing the water absorption area, and removing an abnormal value;
4) partitioning a data set
Dividing a data set into a training set and a testing set;
5) principal component dimensionality reduction of training set
Carrying out PCA dimension reduction processing on the training set to improve the training speed of the model, wherein the number of the principal components is determined by that the variance cumulative interpretation rate is more than 99.9%;
6) model building and screening
The method comprises the following steps of taking the mid-infrared spectrum of a milk sample of a training set as an input value, taking the category of goat milk and cow milk as an output value, comparing and using a nearest neighbor (KNN) algorithm, a BP neural network algorithm, a Random Forest (RF) algorithm and a Support Vector Machine (SVM) algorithm, building a model on the training set through 10-fold cross validation, and evaluating and screening the model by using indexes such as accuracy, specificity, sensitivity and AUC;
7) prediction of optimal model generalization performance
And predicting samples in the test set by using the model, evaluating the performance of the model on the test set by using the corresponding evaluation index, and refining the performance of the model on the test set by using a confusion matrix.
2. The spectral discrimination model of cow's milk and goat's milk according to claim 1, wherein step 2) scans the sample with a milk composition detector.
3. The model for spectral discrimination of cow's milk and goat's milk according to claim 1, wherein when the spectra are collected in step 2), goat's milk and cow's milk samples are respectively poured into cylindrical sampling tubes with a diameter of 3.5cm and a height of 9cm to ensure that the liquid level is higher than 6cm, and then the goat's milk and cow's milk samples are put into a water bath at 42 ℃ for 15-20min, and then a solid optical fiber probe is extended into the liquid for sample suction detection.
4. The spectral discrimination model of cow's milk and goat's milk according to claim 1, wherein the data preprocessing method of step 3) is:
1) converting the spectral data from transmittance (T) to absorbance (A) by the formula:
A＝log10(1/T)
2) removing the water absorption region;
3) abnormal spectrum detection: outliers are removed using the LOF anomaly detection algorithm.
5. The spectral discrimination model of cow's milk and goat's milk according to claim 1, wherein the preprocessed data set in step 4) is divided into training set and testing set according to the hierarchical sampling principle, and the training set and the testing set respectively account for 80% and 20% of the data set.
6. The model for spectral discrimination of cow's milk and goat's milk according to claim 1, wherein the model is constructed on a training set by 10-fold cross-validation to evaluate and screen the model with accuracy, specificity, sensitivity and AUC indexes.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| NL2029012A (en) * | 2020-10-01 | 2022-06-01 | Univ Huazhong Agricultural | Method for quickly identification of cow milk and goat milk |
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| Publication number | Publication date |
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| NL2029012B1 (en) | 2022-09-08 |
| NL2029012A (en) | 2022-06-01 |
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