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

Lookup NU author(s): Dr Jie Zhang, Lei Zhang

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductorsensors is developed to identify milk sources (dairy farms) and to estimate the content of milkfat and protein which are the indicators of milk quality. The developed E-nose is a low cost andnon-destructive device. For milk source identification, the features based on milk odor features fromE-nose, composition features (Dairy Herd Improvement, DHI analytical data) from DHI analysis andfusion 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 modelof milk source (dairy farm) identification. The results show that the SVM model based on the fusionfeatures after LDA has the best performance with the accuracy of 95%. Estimation model of thecontent 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 thatthe RF models give the best performance (R2 = 0.9399 for milk fat; R2 = 0.9301 for milk protein) andindicate that the proposed method in this study can improve the estimation accuracy of milk fat andprotein, which provides a technical basis for predicting the quality of milk.


Publication metadata

Author(s): Mu F, Gu Y, Zhang J, Zhang L

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2020

Volume: 20

Issue: 15

Print publication date: 30/07/2020

Online publication date: 30/07/2020

Acceptance date: 27/07/2020

Date deposited: 10/08/2020

ISSN (print): 1424-8220

ISSN (electronic): 1424-8220

Publisher: MDPI AG

URL: https://doi.org/10.3390/s20154238

DOI: 10.3390/s20154238


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