Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques.

Fanglin Mu,Yu Gu,Lei Zhang,Jie Zhang

doi:10.3390/s20154238

Abstract

In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and to estimate the content of milk fat and protein which are the indicators of milk quality. The developed E-nose is a low cost and non-destructive device. For milk source identification, the features based on milk odor features from E-nose, composition features (Dairy Herd Improvement, DHI analytical data) from DHI analysis and fusion features are analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) for dimension reduction and then three machine learning algorithms, logistic regression (LR), support vector machine (SVM), and random forest (RF), are used to construct the classification model of milk source (dairy farm) identification. The results show that the SVM model based on the fusion features after LDA has the best performance with the accuracy of 95%. Estimation model of the content of milk fat and protein from E-nose features using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and random forest (RF) are constructed. The results show that the RF models give the best performance (R2 = 0.9399 for milk fat; R2 = 0.9301 for milk protein) and indicate that the proposed method in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of milk.

Highlights

Milk contains more than 100 chemical ingredients such as water, fat, phospholipids, proteins, lactose, inorganic salts, and other primary compounds [1,2]
The flavor substances in milk are influenced by many factors, mainly produced by four forms, one of which is the reaction of milk fat, milk protein, and carbonic acid, etc
When the input is the fusion features after linear discriminant analysis (LDA) reduction, the model has the best classification performance with the accuracy of 95% for support vector machine (SVM), 94% for random forest (RF), and 92.5% for logistic regression (LR)

Summary

Introduction

Milk contains more than 100 chemical ingredients such as water, fat, phospholipids, proteins, lactose, inorganic salts, and other primary compounds [1,2]. The composition of milk is very complex. The mixture of lower fatty acids, acetones, acetaldehydes, carbon dioxide, and other volatile substances affects the odor of milk. Sulfide is the main component of fresh milk odor. The flavor substances in milk are influenced by many factors, mainly produced by four forms, one of which is the reaction of milk fat, milk protein, and carbonic acid, etc. Triacylglycerols, fatty acids, diacylglycerides, saturated/polyunsaturated, and phospholipids in milk fat are directly related to the flavor of milk [3,4]

Methods

Results

Conclusion