Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data
- Submitting institution
-
Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 123200509
- Type
- D - Journal article
- DOI
-
10.1016/j.foodchem.2016.09.011
- Title of journal
- Food Chemistry
- Article number
- -
- First page
- 735
- Volume
- 217
- Issue
- -
- ISSN
- 0308-8146
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- 6 - Agriculture, Food and Veterinary Sciences
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
C - Electrical and Electronic
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work introduces a novel statistical modelling technique as a pattern recognition solution for identifying food adulterants. It models the continuous nature of admixtures as data series, obtaining the best results in a comparative analysis. With recent cases of adulteration raising consumer’s concerns, our framework was designed for rapid screening, using inexpensive spectroscopic analysis for data acquisition, reducing the testing cost and increasing ubiquity. It was tested using the adulteration of extra-virgin olive oil as case-of-study. This research contributed to the follow-on funding: EU-H2020 project OLEUM “Advanced solutions for assuring the overall authenticity and quality of olive oil” (£5M).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
