Projects per year
Abstract
Non-intrusive detection systems have the potential to characterise materials through various transparent glass and plastic containers. Food and drink adulteration is increasingly problematic, representing a serious health risk as well as an economic issue. This is of particular concern for alcoholic spirits such as Scotch whisky which are often targeted for fraudulent activity. We have developed a Raman system with a novel geometry of excitation and collection, exploiting the beam propagation from an axicon lens, which results in an annular beam at the bottle surface before focusing within the sample. This facilitates the efficient acquisition of Raman signals from the alcoholic spirit contained inside the bottle, while avoiding the collection of auto-fluorescence signals generated by the bottle wall. Therefore, this technique provides a way of non-destructive and non-contact detection to precisely analyse the contents without the requirement to open the bottle.
Original language | English |
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Pages (from-to) | 4572-4578 |
Journal | Analytical Methods |
Volume | 12 |
Issue number | 37 |
Early online date | 19 Aug 2020 |
DOIs | |
Publication status | Published - 7 Oct 2020 |
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Dive into the research topics of 'Through-bottle whisky sensing and classification using Raman spectroscopy in an axicon-based backscattering configuration'. Together they form a unique fingerprint.Projects
- 2 Finished
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M Squared - USTAN Biophotonics Nexus: M Sqaured - St Andrews Biophotonics Nexus
Dholakia, K. (PI)
1/11/17 → 31/10/22
Project: Standard
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Resonant and shaped photonics for under: Resonant and shaped photonics for understanding the physical and biomedical world
Dholakia, K. (PI) & Gather, M. C. (CoI)
1/08/17 → 31/07/22
Project: Standard
Datasets
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Data underpinning " Through-Bottle Whisky Sensing and Classification using Raman Spectroscopy in an Axicon-Based Backscattering Configuration"
Fleming, H. A. (Creator), Chen, M. (Creator), Bruce, G. D. (Creator) & Dholakia, K. (Creator), University of St Andrews, 2020
DOI: 10.17630/d11587a2-9833-4abc-969f-2593bfb7a816
Dataset
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