An adaptive neuro-fuzzy identification model for the detection of meat spoilage
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 8y867
- Type
- D - Journal article
- DOI
-
10.1016/j.asoc.2014.06.009
- Title of journal
- Applied Soft Computing
- Article number
- -
- First page
- 483
- Volume
- 23
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- 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
-
1
- Research group(s)
-
-
- Citation count
- 16
- 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. Currently, the preferred non-invasive method applied to meat products is the Fourier transform infrared spectroscopy (FTIR). The significance of this paper, is that for the first time, an advanced novel neurofuzzy model have been developed to simultaneously predict the quality type of meat as well as the associated microbiological population from FTIR spectra. The proposed method clearly outperformed all the classic methods currently used in Food microbiology.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
