Research on dairy products detection based on machine learning algorithm

Yang Zhang,Zhaoxia Liu,Jinsen Guan,Yabin Ma,Jihui Liu,Lei Zhang,J Zhu

doi:10.1051/matecconf/202235503008

Abstract

In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.

Highlights

In addition to water, fat, phospholipid, protein, lactose and inorganic salt, milk contains at least 100 kinds of chemical components, the content of which is very complex [1]
(1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis, linear discrimination analysis (LDA)) for dimensionality reduction analysis, and use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source Identify the model and evaluate and compare the classification effects
The random forest (RF)-LDA and support vector machine (SVM)-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate

Summary

Introduction

Fat, phospholipid, protein, lactose and inorganic salt, milk contains at least 100 kinds of chemical components, the content of which is very complex [1]. The mixture of low-grade fatty acids, acetone, acetaldehyde, carbonic acid and other volatile substances in milk affects the flavor of milk, and sulfide is the main component of fresh milk flavor [2]. Dairy cows in different farms have different flavor due to different feed and growth environment [3]. Milk fat and lactose are the key indicators to evaluate the quality of milk [4]. The degradation of their components or the interaction between their derivatives affect the flavor compounds of milk [5] [6]. The establishment of milk detection model is of great significance for the identification of dairy farms and the improvement of milk quality

Methods

Results

Conclusion