Improvement of blueberry freshness prediction based on machine learning and multi-source sensing in the cold chain logistics
Traditional fruit freshness prediction and modeling heavily rely on various physicochemical indicators (such as water loss rate, pH, and VC content), which is facing predicaments of time-consuming, laborious, destructive, and low prediction accuracy. To this end, this paper proposes a new method for...
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| Vydané v: | Food control Ročník 145; s. 109496 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.03.2023
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| ISSN: | 0956-7135, 1873-7129 |
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| Abstract | Traditional fruit freshness prediction and modeling heavily rely on various physicochemical indicators (such as water loss rate, pH, and VC content), which is facing predicaments of time-consuming, laborious, destructive, and low prediction accuracy. To this end, this paper proposes a new method for fruit freshness prediction based on multi-sensing technology and machine learning algorithm, thereby improving the automation, intelligentialize, and high accuracy of fruit freshness prediction. The critical control points of blueberry cold chain logistics were analyzed firstly based on the HACCP method, identifying the key gas parameters (O2, CO2, and C2H4) and interaction mechanisms of gas and blueberry freshness. Then the blueberry cold chain microenvironment monitoring platform (BCCMMP) was developed for critical gas content monitoring at different temperatures (0 °C, 5 °C, and 22 °C). It was demonstrated that gas information can replace quality information to characterize blueberry freshness, and further emerging machine learning (ML) algorithms (BP, RBF, SVM, and ELM) were constructed for blueberry freshness prediction using critical gas information, and the results showed prediction accuracies of 90.87% (BP), 92.24% (RBF), 94.01% (SVM), and 91.31% (ELM). By contrast, the 85.10% prediction accuracy was achieved by the traditional Arrhenius equation method based on temperature and quality parameters. Through the automatic non-destructive acquisition of sensing data and emerging machine learning algorithms, this paper provides a new approach to improving the freshness prediction accuracy and food quality management level during fruit cold chain logistics.
•A WSN-based environmental monitoring platform for blueberry cold chain was developed.•A model was developed to predict blueberry freshness at three different temperatures.•Non-destructive prediction of blueberry freshness using gas sensed information.•Gas information has a strong correlation with blueberry quality index.•The model proposed in this paper can be applied in cold chain logistics. |
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| AbstractList | Traditional fruit freshness prediction and modeling heavily rely on various physicochemical indicators (such as water loss rate, pH, and VC content), which is facing predicaments of time-consuming, laborious, destructive, and low prediction accuracy. To this end, this paper proposes a new method for fruit freshness prediction based on multi-sensing technology and machine learning algorithm, thereby improving the automation, intelligentialize, and high accuracy of fruit freshness prediction. The critical control points of blueberry cold chain logistics were analyzed firstly based on the HACCP method, identifying the key gas parameters (O₂, CO₂, and C₂H₄) and interaction mechanisms of gas and blueberry freshness. Then the blueberry cold chain microenvironment monitoring platform (BCCMMP) was developed for critical gas content monitoring at different temperatures (0 °C, 5 °C, and 22 °C). It was demonstrated that gas information can replace quality information to characterize blueberry freshness, and further emerging machine learning (ML) algorithms (BP, RBF, SVM, and ELM) were constructed for blueberry freshness prediction using critical gas information, and the results showed prediction accuracies of 90.87% (BP), 92.24% (RBF), 94.01% (SVM), and 91.31% (ELM). By contrast, the 85.10% prediction accuracy was achieved by the traditional Arrhenius equation method based on temperature and quality parameters. Through the automatic non-destructive acquisition of sensing data and emerging machine learning algorithms, this paper provides a new approach to improving the freshness prediction accuracy and food quality management level during fruit cold chain logistics. Traditional fruit freshness prediction and modeling heavily rely on various physicochemical indicators (such as water loss rate, pH, and VC content), which is facing predicaments of time-consuming, laborious, destructive, and low prediction accuracy. To this end, this paper proposes a new method for fruit freshness prediction based on multi-sensing technology and machine learning algorithm, thereby improving the automation, intelligentialize, and high accuracy of fruit freshness prediction. The critical control points of blueberry cold chain logistics were analyzed firstly based on the HACCP method, identifying the key gas parameters (O2, CO2, and C2H4) and interaction mechanisms of gas and blueberry freshness. Then the blueberry cold chain microenvironment monitoring platform (BCCMMP) was developed for critical gas content monitoring at different temperatures (0 °C, 5 °C, and 22 °C). It was demonstrated that gas information can replace quality information to characterize blueberry freshness, and further emerging machine learning (ML) algorithms (BP, RBF, SVM, and ELM) were constructed for blueberry freshness prediction using critical gas information, and the results showed prediction accuracies of 90.87% (BP), 92.24% (RBF), 94.01% (SVM), and 91.31% (ELM). By contrast, the 85.10% prediction accuracy was achieved by the traditional Arrhenius equation method based on temperature and quality parameters. Through the automatic non-destructive acquisition of sensing data and emerging machine learning algorithms, this paper provides a new approach to improving the freshness prediction accuracy and food quality management level during fruit cold chain logistics. •A WSN-based environmental monitoring platform for blueberry cold chain was developed.•A model was developed to predict blueberry freshness at three different temperatures.•Non-destructive prediction of blueberry freshness using gas sensed information.•Gas information has a strong correlation with blueberry quality index.•The model proposed in this paper can be applied in cold chain logistics. |
| ArticleNumber | 109496 |
| Author | Zhang, Junchang Zhang, Xiaoshuan Xia, Jie Huang, Wentao Wang, Xuepei |
| Author_xml | – sequence: 1 givenname: Wentao surname: Huang fullname: Huang, Wentao organization: Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing, 100083, PR China – sequence: 2 givenname: Xuepei orcidid: 0000-0003-2558-4367 surname: Wang fullname: Wang, Xuepei organization: Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing, 100083, PR China – sequence: 3 givenname: Junchang surname: Zhang fullname: Zhang, Junchang organization: Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing, 100083, PR China – sequence: 4 givenname: Jie orcidid: 0000-0002-3879-1317 surname: Xia fullname: Xia, Jie organization: Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing, 100083, PR China – sequence: 5 givenname: Xiaoshuan surname: Zhang fullname: Zhang, Xiaoshuan email: zhxshuan@cau.edu.cn organization: Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing, 100083, PR China |
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| Keywords | Freshness prediction Wireless sensor network (WSN) HACCP methods Blueberry Machine learning algorithm |
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| SubjectTerms | algorithms automation blueberries Blueberry carbon dioxide cold chain equations food quality freshness Freshness prediction fruits HACCP HACCP methods Machine learning algorithm prediction temperature Wireless sensor network (WSN) |
| Title | Improvement of blueberry freshness prediction based on machine learning and multi-source sensing in the cold chain logistics |
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