A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies

Weng, Xiaohui, Luan, Xiangyu, Cheng, Kong, Chang, Zhiyong, Li, Yinwu, Zhang, Shujun ORCID: 0000-0001-5699-2676, Al-Majeed, Salah ORCID: 0000-0002-5932-9658 and Xiao, Yingkui (2020) A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies. Journal of Sensors, 2020. Art No 8838535. doi:10.1155/2020/8838535

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Abstract

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).

Item Type: Article
Article Type: Article
Uncontrolled Keywords: Artificial Tactile Technologies; Assessing Meat Freshness; Food
Subjects: T Technology > T Technology (General)
Divisions: Professional Services > Academic Development Unit
Research Priority Areas: Applied Business & Technology
Depositing User: Kate Greenaway
Date Deposited: 02 Oct 2020 11:06
Last Modified: 03 Oct 2020 04:15
URI: http://eprints.glos.ac.uk/id/eprint/8813

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