Quality Assessment of Beef Based of Computer Vision and Electronic Nose

2007 ◽  
Author(s):  
Chen Cunshe ◽  
Li Xiaojuan ◽  
Yuan Huimei
Keyword(s):  
Computer Vision ◽  
Quality Assessment ◽  
Electronic Nose

Wine Quality Assessment using Electronic Nose

2021 ◽  
Author(s):  
Kranthi Kumar Pulluri ◽  
Vaegae Naveen Kumar
Keyword(s):  
Quality Assessment ◽  
Electronic Nose ◽  
Wine Quality

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

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaohui Weng ◽  
Xiangyu Luan ◽  
Cheng Kong ◽  
Zhiyong Chang ◽  
Yinwu Li ◽  
...  
Keyword(s):  
Computer Vision ◽  
Electronic Nose ◽  
Prediction Models ◽  
Meat Products ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Pattern Recognition Methods ◽  
Fusion Data ◽  
Objective Detection

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).

Potential Application of the Electronic Nose for Quality Assessment of Salmon Fillets Under Various Storage Conditions

2002 ◽  
Vol 67 (1) ◽  
pp. 307-313 ◽  
Author(s):  
W-X. Du ◽  
C-M. Lin ◽  
T. Huang ◽  
J. Kim ◽  
M. Marshall ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Electronic Nose ◽  
Potential Application ◽  
Storage Conditions

scholarly journals QUALITY ASSESSMENT OF BISCUITS USING COMPUTER VISION

2016 ◽  
Vol 7 (1) ◽  
pp. 1285-1289 ◽  
Author(s):  
Archana A. Bade ◽  
◽  
Manisha P. Dale ◽  
Jemshid K K ◽  
◽  
...  
Keyword(s):  
Computer Vision ◽  
Quality Assessment
Sign in / Sign up

Export Citation Format

Share Document