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, Queen’s 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, 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 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 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.