DEVELOPMENT OF A NOVEL CONTINUOUS STATISTICAL MODELLING TECHNIQUE FOR DETECTING THE ADULTERATION OF EXTRA VIRGIN OLIVE OIL WITH HAZELNUT OIL BY USING SPECTROSCOPIC DATA Konstantia Georgouli 1 Jesus Martinez Del Rincon ID: 524723
Download Presentation The PPT/PDF document "www.qub.ac.uk/igfs" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
www.qub.ac.uk/igfs
DEVELOPMENT OF A NOVEL CONTINUOUS STATISTICAL MODELLING TECHNIQUE FOR DETECTING THE ADULTERATION OF EXTRA VIRGIN OLIVE OIL WITH HAZELNUT OIL BY USING SPECTROSCOPIC DATA
Konstantia Georgouli1, Jesus Martinez Del Rincon2, Anastasios Koidis1
1Institute for Global Food Security, School of Biological Sciences, Queen’s University of Belfast, UK2Institute of Electronics, Communications and Information Technology, School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, UK
INTRODUCTION
Extra virgin olive oil (EVOO) is a premium vegetable oil characterised by great nutritional value and high price.Despite strict limits defining the purity of EVOO by International Olive Council (IOOC) and EU, it continues to attract various fraudulent and adulteration practices. Adulteration of EVOO with other vegetable oils is a certain problem that has not found yet solutions (European Commission 2013). Detection of adulterants at low levels (5-20%) is still difficult process (Zhang et al. 2011). Addition of hazelnut oil to extra virgin olive oil is one of the most concerning adulterations (Parker et al. 2014).
EXPERIMENTAL AND METHODOLOGY
REFERENCES:European Commission 2013, Workshop on olive oil authentication, European Commission, http://ec.europa.eu/agriculture/events/2013/olive-oil-workshop/newsletter_en.pdf.He, X. & Niyogi, P. 2004, "Locality preserving projections", Advances in Neural Information Processing Systems.Parker, T., Limer, E., Watson, A.D., Defernez, M., Williamson, D. & Kemsley, E.K. 2014, "60 MHz 1H NMR spectroscopy for the analysis of edible oils", TrAC Trends in Analytical Chemistry, vol. 57, no. 0, pp. 147-158.Zhang, X., Qi, X., Zou, M. & Liu, F. 2011, "Rapid Authentication of Olive Oil by Raman Spectroscopy Using Principal Component Analysis", Analytical Letters, vol. 44, no. 12, pp. 2209-2220.
ACKNOLEDGEMENTS: This research was funded by
MOTIVATION, METHODOLOGY AND RESULTS
AIM OF THE
STUDY: To develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying hazelnut oil (HO) adulterants in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints.
Creation of admixtures
(i)
Great Nutritional Value
High-priced food
Extra Virgin Olive oil adulteration
FTIR spectra acquisition
RAMAN spectra acquisition
Training dataset
Testing
dataset
1
.
Model the mixtures as data series
2. Mapping test samples on the model space
3.
Application
of a classifier
4. Validation of the model
Decision
Model
Exploratory
analysis
using
PCA, LDA
and
Kernel PCA
Data acquisition
Projection of
the produced
admixtures
on
the
space of
the
pure
oils
(Fig. 1)
Pretreatment
MOTIVATION
Figure
1.
Projection of the produced admixtures on the LDA space of the pure
oils
using FTIR data
METHODOLOGY
Continuous
Locality
Preserving
Projections (CLPP)
Based
on that conclusion, we developed
a
NOVEL statistical
technique modelling the produced admixtures
as data series instead of discrete
points
It extends
the linear dimensionality reduction technique Locality Preserving Projections, LPP (He,
Niyogi 2004). CLPP considers the mixture percentage as a continuous variable. Data is modelled as data series and the continuity preserved during the learning and dimensionality reduction.
Definition of CLPP
: Y={yk}(k=1..n), yk RD Z={mk}(k=1..n), mk Rd d<<DComplementary constraints control the similarity in the embedded space: neighbourhood graphs.
Design of our pattern recognition solution
Statistical analysis of the in house admixtures
Application of
CLPP
RESULTS
Figure
2.
Application of CLPP technique to RAMAN data
Continuous graphSimilarity graphThus, similarity and continuous neighbours are placed nearby in the embedded space without enforcing any artificial embedded geometry.Continuous neighbourhood : Similarity neighbourhood : The embedded space is spanned by the d eigenvectors with the smallest nonzero eigenvalues. They are obtained from the solution of the generalised eigenvalue problem:
Result
: a continuous reduced latent space where calibration data can be easily understood and analysed.
CONCLUSION
N
ovel dimensionality reduction approach, CLPP allows the preservation of the concentration grade information in the modelling of spectroscopic datasets
A
ddressing more
efficiently and accurately the subtle fraud of the adulteration of EVOO with HO based on spectroscopic datasets.
Conclusion
:
the admixtures have
continuous
nature.