Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9z4xw
- Type
- D - Journal article
- DOI
-
10.1007/s11947-016-1851-6
- Title of journal
- Food and Bioprocess Technology
- Article number
- -
- First page
- 730
- Volume
- 10
- Issue
- 4
- ISSN
- 1935-5130
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
0
- Research group(s)
-
-
- Citation count
- 27
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The development of non-destructive sensing technologies to detect spoilage and pathogenic bacteria with a high degree of dependency in food products is very desirable for Food industry as well as for Health authorities. One of the emerging non-invasive systems applied to meat products is the electronic nose. The significance of this paper is that for the first time, a novel clustering-based fuzzy wavelet neural network model has been developed to simultaneously predict the quality type of meat as well as the associated microbiological population from enose signals. The proposed method outperformed all the classic methods currently used in Food microbiology.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
