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Intermediate infrared rapid batch detection method for content of free lysine in milk

A technology for batch detection and lysine, which is applied in measuring devices, material analysis through optical means, instruments, etc., can solve the problems of no rapid batch detection model and the inability to effectively obtain milk quality and safety information, and achieve rapid batch detection , low-cost detection, and the effect of improving detection efficiency

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

AI Technical Summary

Problems solved by technology

[0005] Although my country's dairy industry started late in the field of mid-infrared spectroscopy technology research, it has also achieved great results. At present, my country has been able to detect indicators such as total protein content and total fat content in milk through mid-infrared spectroscopy technology, but temporarily There is no rapid batch detection model for free lysine content in milk based on mid-infrared spectroscopy, so there is an urgent need for a rapid batch detection method for free lysine content in milk with high accuracy and strong adaptability for high-throughput, large-scale To accurately measure the content of free lysine in milk, build a fast, efficient, low-cost, high-precision, non-destructive, and intelligent detection system for the content of nutrients in milk, and solve the key scientific and technological problems that cannot effectively obtain milk quality and safety information. There is a huge potential market in my country's fresh milk postpartum commercialization industry [17]

Method used

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  • Intermediate infrared rapid batch detection method for content of free lysine in milk
  • Intermediate infrared rapid batch detection method for content of free lysine in milk
  • Intermediate infrared rapid batch detection method for content of free lysine in milk

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0065] Choice of lysine prediction model algorithm:

[0066] The purpose of this application is to establish a quantitative determination model of lysine, so the modeling algorithm used is a regression algorithm. There are many types of regression algorithms, and this embodiment mainly uses Ridge regression (Ridge) and partial least squares regression (PLSR) [9] Algorithm for model building and comparison, the reasons are as follows:

[0067] 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 a linear regression model after adding l2 regularization, w...

Embodiment 2

[0070] Screening of the number of mid-infrared spectroscopy measurements and how they are used:

[0071] All the samples used in this application have been collected twice in a row. The purpose is to compare the different measurement times of the same sample and the obtained three kinds of MIR data (the first time, the second time, and the second average) to the modeling accuracy. The influence of , and screen out the most effective MIR data for modeling. Because some researchers believe that the average spectral MIR measured twice may improve the modeling accuracy, so in this embodiment, the first, second, and second average spectral MIR are removed from the water band and the area greater than 4000cm -1 Partially modeled and compared the accuracy of the analysis model, the results are shown in the following table:

[0072] Ridge algorithm comparison results:

[0073]

[0074] PLSR algorithm comparison results:

[0075]

[0076] After comprehensive consideration of t...

Embodiment 3

[0078] Establishment of a method for detecting lysine content in milk by mid-infrared spectroscopy:

[0079] 1. Division of modeling data sets

[0080]

[0081] In the division of the modeling data set in this embodiment, 75% is a training set and 25% is a test set. The ratio of the training set to the test set is 3:1. 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.

[0082] 2. Screening of modeling MIR data preprocessing methods

[0083] 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 scattering correction),...

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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 the content of free lysine in milk. In the aspect of selection of characteristic wave bands, the applicant breaks through a common method for screening characteristics by using an algorithm, and a manual selection and multiple traversal method is used. Finally, the characteristic absorption wave band of lysine is selected. Through comparison, it is confirmed that the modeling effect is better when the average spectrums measured twice of the same milk sample are selected for modeling, the optimal preprocessing and algorithm combination established by the lysine model is selected, the optimal parameters are determined, and the accuracy of the model is improved. The method realizes rapid, accurate and low-cost detection of the lysine content in the raw milk, and can be widely applied to dairy cow performance determination and milk quality detection.

Description

technical field [0001] The invention belongs to the fields of dairy cow performance measurement and milk quality detection, and in particular relates to a mid-infrared spectrum rapid batch detection method for free lysine content in milk. Background technique [0002] Milk is an important source of protein in the human diet. The amino acid composition of protein in milk is similar to that of human milk. It contains 8 essential amino acids that cannot be synthesized in the human body, such as lysine, valine, leucine, etc., to synthesize human protein When the biological potency is high, the utilization rate of amino acids is also high. [0003] Lysine (Lysine, Lys) molecular formula is C 6 h 14 N 2 o 2 , is the most important essential amino acid for the human body, and determines the degree of utilization of other amino acids [1] . Studies have shown that lysine is the first limiting amino acid in the human body and has important biological functions, such as improving...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/3577
CPCG01N21/3577
Inventor 张淑君樊懿楷褚楚罗雪路刘文举王海童苏俊东上官爱哨
Owner HUAZHONG AGRI UNIV
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