Www.qub.ac.uk/igfs DEVELOPMENT OF A NOVEL CONTINUOUS STATISTICAL MODELLING TECHNIQUE FOR DETECTING...
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Www.qub.ac.uk/igfs DEVELOPMENT OF A NOVEL CONTINUOUS STATISTICAL MODELLING TECHNIQUE FOR DETECTING THE ADULTERATION OF EXTRA VIRGIN OLIVE OIL WITH HAZELNUT
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 Georgouli 1, Jesus Martinez Del Rincon 2,
Anastasios Koidis 1 Konstantia Georgouli 1, Jesus Martinez Del
Rincon 2, Anastasios Koidis 1 1 Institute for Global Food Security,
School of Biological Sciences, Queens University of Belfast, UK 2
Institute 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.pdfhttp://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. 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 Result: a continuous reduced latent
space where calibration data can be easily understood and analysed.
CONCLUSION Novel dimensionality reduction approach, CLPP allows the
preservation of the concentration grade information in the
modelling of spectroscopic datasets Addressing more efficiently and
accurately the subtle fraud of the adulteration of EVOO with HO
based on spectroscopic datasets. Conclusion: the admixtures have
continuous nature.