Rapid batch detection method for A2 type beta casein content in milk

A technology for protein content and batch detection, which is applied in the direction of measuring devices, instruments, scientific instruments, etc., can solve the problems of long analysis time and high cost, and achieve the effects of improving detection efficiency, low-cost detection, and improving accuracy

Active Publication Date: 2022-05-27
HUAZHONG AGRI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the above existing analysis methods are mature in technology and high in accuracy, they have the disadvantages of long analysis time and high cost.

Method used

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  • Rapid batch detection method for A2 type beta casein content in milk
  • Rapid batch detection method for A2 type beta casein content in milk
  • Rapid batch detection method for A2 type beta casein content in milk

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Choice of prediction model algorithm for A2-type beta casein:

[0047] The purpose of this application is to establish a quantitative determination model of A2-type beta casein in milk, so the modeling algorithm is a regression algorithm. There are many types of regression algorithms. This embodiment mainly uses Ridge regression (Ridge) and partial least squares regression (PLSR). [9] The algorithm builds and compares models for the following reasons:

[0048] Ridge regression is a type of linear regression. Only when the algorithm establishes the regression equation, the ridge regression adds the restriction of regularization, so as to achieve the effect of solving overfitting. There are two kinds of regularization, namely l1 regularization and l2 regularization. The advantages of l2 regularization compared to l1 regularization are: (1) cross-validation can be performed (2) stochastic gradient descent is realized. Ridge regression is a linear regression model after ...

Embodiment 2

[0051] Screening of the number of mid-infrared spectroscopy measurements and their usage:

[0052] In this embodiment, each sample corresponds to one piece of MIR spectral data. Substitute the full spectrum band for modeling, compare and analyze the accuracy of the model, and use diff1 (first-order difference) for preprocessing to determine the accuracy of the algorithm. The results are as follows:

[0053] Algorithm comparison results:

[0054]

[0055] After comparing the results of the two algorithms, PLSR has a better effect on the test set, and the over-fitting situation is weaker than that of the Ridge algorithm, so the PLSR algorithm is finally selected for modeling.

Embodiment 3

[0057] Establishment of a method for the detection of A2-type β-casein content in milk by mid-infrared spectroscopy:

[0058] 1. Division of the modeling dataset

[0059]

[0060] In the division of the modeling data set in this embodiment, 70% is the training set and 30% is the test set. The ratio of the training set to the test set is 7:3, and the training set is also called the cross-validation set. In the process of training the model, 10-fold cross-validation is performed.

[0061] 2. Screening of preprocessing methods for modeling MIR data

[0062] Effective feature screening is the basic operation of spectral data processing, the purpose is to eliminate noise and lay a solid foundation for feature extraction. There are three types of effective feature screening: feature extraction, feature preprocessing and feature dimensionality reduction. This embodiment mainly adopts five processing methods, such as SG (convolution smoothing), MSC (multiple scattering correctio...

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Abstract

The invention belongs to the field of dairy cow performance determination and milk quality detection, and particularly relates to an intermediate infrared spectrum rapid batch detection method for A2 type beta casein in milk. In the aspect of selection of characteristic wave bands, a common method of screening characteristics by using an algorithm is broken through, and a manual selection and multi-time traversal method is used. And finally selecting a characteristic wave band for modeling. According to the method, the optimal preprocessing and algorithm combination established by the A2 type beta-casein model is selected, the optimal parameters are determined, the accuracy of the model is improved, and the rapid, accurate and low-cost detection of the A2 type beta-casein content in the raw milk is realized.

Description

technical field [0001] The invention belongs to the fields of dairy cow performance measurement and milk quality detection, and particularly relates to a mid-infrared spectroscopy rapid batch detection method for A2-type beta casein in milk. Background technique [0002] Milk is rich in milk protein, which is the source of essential amino acids and bioactive peptides for the human body after enzymatic digestion. Casein makes up about 80% of the protein in milk [1] , β-casein accounts for about 30% of the total milk casein. A total of 15 β-casein variants have been identified in dairy cows, with A1 and A2 having the highest probability. A2-type β-casein is a wild-type protein, and the 67th amino acid in its amino acid sequence is mutated from proline to histidine, which is mutated to A1-type β-casein [2] . A1-type beta casein can produce beta-casomorphin (BCM-7) during digestion, which may interfere with normal human metabolism, increase the risk of type 1 diabetes in som...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/3577G01N30/02
CPCG01N21/3577G01N30/02
Inventor 张淑君王海童樊懿楷张静静褚楚邹慧颖
Owner HUAZHONG AGRI UNIV
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