Intermediate infrared spectrum detection method for protein content in buffalo milk and application

A technology of infrared spectroscopy and protein content, which is applied in the field of performance measurement and quality detection of buffalo milk, can solve the problems of low accuracy of conventional milk component content, achieve the effects of shortening the measurement time, improving detection efficiency and high accuracy

Pending Publication Date: 2022-03-11
HUAZHONG AGRI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At present, the content of conventional milk components (total protein content, etc.) of buffalo milk is detected by using the prediction model of cow milk, and the accuracy of using the cow prediction model to determine the content of conventional milk components of buffalo milk is not high

Method used

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  • Intermediate infrared spectrum detection method for protein content in buffalo milk and application
  • Intermediate infrared spectrum detection method for protein content in buffalo milk and application
  • Intermediate infrared spectrum detection method for protein content in buffalo milk and application

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] Choice of algorithm for predictive model of protein content in buffalo milk:

[0048] The purpose of this application is to establish a quantitative prediction model for protein content in buffalo milk, so the modeling algorithm used is a regression algorithm. There are many kinds of regression algorithms. In this embodiment, Ridge regression (Ridge) and partial least squares regression (PLSR) algorithms are mainly used for model establishment and comparison. The reasons are as follows:

[0049] Ridge regression is a type of linear regression. Only when the algorithm establishes the regression equation, the ridge regression adds regularization restrictions, so as to achieve the effect of solving over-fitting. There are two types of regularization, namely l1 regularization and l2 regularization. Compared with l1 regularization, the advantages of l2 regularization are: (1) cross-validation can be performed (2) stochastic gradient descent is realized. Ridge regression is...

Embodiment 2

[0052] Choice of Quantitative Prediction Model Algorithm:

[0053] In this embodiment, each sample corresponds to one piece of MIR spectral data. Will be greater than 4000cm -1 Partially modeled and compared the accuracy of the analysis model without using any preprocessing method to determine the accuracy of the algorithm. The results are shown in the following table:

[0054] Algorithm comparison results:

[0055] algorithm R c

[0056] After comparing the results of the two algorithms, the effect on the test set is close, but the effect of the Ridge algorithm on the training set is better, and finally the Ridge algorithm is selected for modeling.

Embodiment 3

[0058] Establishment of a method for detecting protein content in buffalo milk in milk by mid-infrared spectroscopy:

[0059] 1. Division of modeling data sets

[0060]

[0061] In the division of the modeling data set in this embodiment, 80% is a training set and 20% is a test set. The ratio of the training set to the test set is 4:1. At the same time, the training set is also called the cross-validation set. In the process of training the model, 10-fold cross-validation is performed.

[0062] 2. Screening of modeling MIR data preprocessing methods

[0063] Effective feature screening is the basic operation of spectral data processing, the purpose is to eliminate noise and lay a good foundation for feature extraction. Effective feature screening mainly includes feature extraction, feature preprocessing, and feature dimensionality reduction. This embodiment mainly performs feature preprocessing on the spectral data. First, SG (convolution smoothing), MSC (multiple scatte...

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Abstract

The invention belongs to the field of performance determination and quality detection of buffalo milk, and discloses a mid-infrared spectrum detection method for protein content in buffalo milk and application. In the aspect of selection of characteristic wave bands, the applicant breaks through a common method for screening characteristics by using an algorithm, and uses a manual selection and multiple traversal method. And finally, selecting a characteristic wave band for modeling, particularly screening out an absorption area containing part of water, and proving that the accuracy of the model can be improved by increasing part of the water absorption wave band. The optimal algorithm and the optimal characteristic wave band established by the quantitative prediction model of the protein in the buffalo milk are selected, the accuracy is very high, and rapid batch detection of the protein content in the buffalo milk is realized.

Description

technical field [0001] The invention belongs to the field of performance measurement and quality detection of buffalo milk, and in particular relates to a mid-infrared spectrum detection method and application of buffalo milk protein content. Background technique [0002] Different from the milk secreted and produced by cows, the milk yield of buffalo is low, but the nutritional value of buffalo milk is higher than that of cow milk, and the content of protein and fat in buffalo milk is significantly higher than that of milk [1] . Studies have shown that the nutritional value of 1 kg of buffalo milk is equivalent to the nutritional value of 1.85 kg of Holstein milk. The average protein content in buffalo milk is 4.5%, about 132.35% of Holstein milk (3.4%), about 281.25% of human breast milk (1.6%) [2] . The protein in buffalo milk contains all the essential amino acids needed by the human body, so the protein in buffalo milk is a complete protein, and the digestibility of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/3577
CPCG01N21/3577
Inventor 张淑君张依樊懿楷杨利国周扬刘兴斌苗义良霍立军李翔熊家军滑国华梁爱心何长久
Owner HUAZHONG AGRI UNIV
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