A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies
المؤلفون المشاركون
Kong, Cheng
Weng, Xiaohui
Luan, Xiangyu
Chang, Zhiyong
Li, Yinwu
Zhang, Shujun
Al-Majeed, Salah
Xiao, Yingkui
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-09-21
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص 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).
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1190616
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر