Intermediate infrared spectrum rapid batch detection method for total solid content in buffalo milk and application of intermediate infrared spectrum rapid batch detection method

A technology of total solids and infrared spectroscopy, applied in measuring devices, material analysis through optical means, instruments, etc., can solve the problems of low accuracy and achieve high accuracy, rapid batch detection, and strong practicability

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

[0004] However, there is currently no predictive model MIR measurement technology specifically for MIR such as total solids and total protein content in buffalo milk
The conventional milk composition detection of buffalo milk is based on the MIR prediction model and detection technology of the total solid content of buffalo milk. The accuracy is not high; in order to develop buffalo breeding and improve the production performance measurement technology of buffalo, it is necessary to establish a detection model suitable for the total solid content of buffalo milk in China as soon as possible

Method used

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  • Intermediate infrared spectrum rapid batch detection method for total solid content in buffalo milk and application of intermediate infrared spectrum rapid batch detection method
  • Intermediate infrared spectrum rapid batch detection method for total solid content in buffalo milk and application of intermediate infrared spectrum rapid batch detection method
  • Intermediate infrared spectrum rapid batch detection method for total solid content in buffalo milk and application of intermediate infrared spectrum rapid batch detection method

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Experimental program
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Effect test

Embodiment 1

[0051] Selection of predictive model algorithm for total solids content of buffalo milk:

[0052] The purpose of this application is to establish a quantitative prediction model for the total solid content of 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:

[0053] 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 ...

Embodiment 2

[0056] Choice of Quantitative Prediction Model Algorithm:

[0057] 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:

[0058] Algorithm comparison results:

[0059] algorithm R c

[0060] After comparing the results of the two algorithms, PLSR has a better effect on the test set and is close to the effect of the training set, so the PLSR algorithm is finally selected for modeling.

Embodiment 3

[0062] Establishment of a method for detecting total solids content in buffalo milk by mid-infrared spectroscopy:

[0063] 1. Division of modeling data sets

[0064]

[0065] 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.

[0066] 2. Screening of modeling MIR data preprocessing methods

[0067] 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 scatterin...

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Abstract

The invention belongs to the field of performance determination and quality detection of buffalo cows, and particularly discloses an intermediate infrared spectrum rapid batch detection method for the content of total solids in buffalo milk and application of the intermediate infrared spectrum rapid batch detection method. 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 total solids in the buffalo milk are selected by the applicant, and the accuracy is very high. The rapid batch detection of the total solid 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 cows, and in particular relates to a mid-infrared spectrum rapid batch detection method and application of the total solid content in buffalo milk. Background technique [0002] Total solids are an important component of milk, including fat, protein, lactose and other substances in milk, so the higher the total solids content, the richer the nutrients in milk. The total solids content of buffalo milk is significantly higher than that of milk, so the nutritional value of buffalo milk is higher than that of milk [1] . Buffalo milk contains 18.9% dry matter, which is 5.1% higher than Holstein milk (13.8%) and 6.5% higher than human breast milk (12.4%) [2-5] . Some studies have shown that the total solids content of buffalo milk in my country is significantly higher than that of mura buffaloes and cows. Because the total solids content of buffalo milk is highe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/3577
CPCG01N21/3577
Inventor 张淑君张依樊懿楷王贵强刘兴斌李翔滑国华梁爱心罗雪路覃广胜梁贤威吴喜娟丁学梅
Owner HUAZHONG AGRI UNIV
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