A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies
Joint Authors
Kong, Cheng
Weng, Xiaohui
Luan, Xiangyu
Chang, Zhiyong
Li, Yinwu
Zhang, Shujun
Al-Majeed, Salah
Xiao, Yingkui
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-21
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness.
Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness).
Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable.
In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection.
The pork and chicken meats have been selected for a validation test.
The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method.
The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively.
The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products.
In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input.
The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94.
This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).
American Psychological Association (APA)
Weng, Xiaohui& Luan, Xiangyu& Kong, Cheng& Chang, Zhiyong& Li, Yinwu& Zhang, Shujun…[et al.]. 2020. A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies. Journal of Sensors،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1190616
Modern Language Association (MLA)
Weng, Xiaohui…[et al.]. A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies. Journal of Sensors No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1190616
American Medical Association (AMA)
Weng, Xiaohui& Luan, Xiangyu& Kong, Cheng& Chang, Zhiyong& Li, Yinwu& Zhang, Shujun…[et al.]. A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies. Journal of Sensors. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1190616
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190616