| Title |
Simultaneous quantification of the boar-taint compounds skatole and androstenone by surface-enhanced Raman scattering (SERS) and multivariate data analysis
|
|---|---|
| Published in |
Analytical & Bioanalytical Chemistry, August 2015
|
| DOI | 10.1007/s00216-015-8945-2 |
| Pubmed ID | |
| Authors |
Klavs M. Sørensen, Chloe Westley, Royston Goodacre, Søren Balling Engelsen |
| Abstract |
This study investigates the feasibility of using surface-enhanced Raman scattering (SERS) for the quantification of absolute levels of the boar-taint compounds skatole and androstenone in porcine fat. By investigation of different types of nanoparticles, pH and aggregating agents, an optimized environment that promotes SERS of the analytes was developed and tested with different multivariate spectral pre-processing techniques, and this was combined with variable selection on a series of analytical standards. The resulting method exhibited prediction errors (root mean square error of cross validation, RMSECV) of 2.4 × 10(-6) M skatole and 1.2 × 10(-7) M androstenone, with a limit of detection corresponding to approximately 2.1 × 10(-11) M for skatole and approximately 1.8 × 10(-10) for androstenone. The method was subsequently tested on porcine fat extract, leading to prediction errors (RMSECV) of 0.17 μg/g for skatole and 1.5 μg/g for androstenone. It is clear that this optimized SERS method, when combined with multivariate analysis, shows great potential for optimization into an on-line application, which will be the first of its kind, and opens up possibilities for simultaneous detection of other meat-quality metabolites or pathogen markers. Graphical abstract Artistic rendering of a laser-illuminated gold colloid sphere with skatole and androstenone adsorbed on the surface. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 39 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 21% |
| Student > Ph. D. Student | 6 | 15% |
| Student > Master | 4 | 10% |
| Student > Bachelor | 3 | 8% |
| Student > Postgraduate | 3 | 8% |
| Other | 10 | 26% |
| Unknown | 5 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 36% |
| Chemistry | 6 | 15% |
| Biochemistry, Genetics and Molecular Biology | 2 | 5% |
| Psychology | 2 | 5% |
| Engineering | 2 | 5% |
| Other | 6 | 15% |
| Unknown | 7 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 September 2015.
All research outputs
#19,944,091
of 25,373,627 outputs
Outputs from Analytical & Bioanalytical Chemistry
#6,060
of 9,618 outputs
Outputs of similar age
#188,848
of 275,629 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#52
of 184 outputs
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So far Altmetric has tracked 9,618 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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