Characterization of Turkish Olive Oils by Using Multivariate Statistical Methods Aytaç S.GÜMÜŞKESEN, Fahri YEMİŞÇİOĞLU İsmail EREN Ege University, Engineering Faculty, Food Engineering Department, İzmir- TÜRKİYE 6th Euro Fed Lipid Congress, 7-10 September 2008, Athens - Greece

Virgin olive oil is a food with high nutritional qualities; hence, it is chosen for cooking and dressing by consumer from Mediterranean countries, which represent the world’s largest production area (98% of world’s olive oil). INTRODUCTION

Geographical distribution of world olive oil production and consumption ( crop seasons-IOOC2007) Production (%) Consumption(%) Spain Italy Greece Tunisia 5 2 Syria 5 4 Turkey 4 2 Morocco 2 2 France <0.2 4 Others 5 19

Factors Affecting Olive Oil Composition The flavor of extra virgin olive oil has particular desirable organoleptic and nutritional properties. However, chemical composition of the olive oil together with its physical and sensory characteristics mainly depends on several factors such as; Olive variety Environmental factors (soil, climate, growing location) Agronomic factors (irrigation, fertilization) Cultivation (harvesting method, maturity) Technological factors (storage, extraction system)

In order to protect high quality agricultural products based on geographical origin, The European Union regulations provide guidelines for maintaining the Protection Designation of Origin (PDO) and Protection of Geographical Indication (PGI) for theese products. PDO The product is produced, processed and prepared within the specified geographical region. PGI The product is produced, processed and prepared in a certain geographical area, and all characteristics are attributable to that area. Geographical Indication- Designations

These designations include characterization of foods based on cultivar and geographical origin, as they are used an indicator of authenticity and quality. Therefore, there is an economic basis for identifying characteristics that distinguish PDO of olive oils.

Chemometric criteria for the characterization of olive oils Many analytical methodologies have been proposed for the characterization, analysis and authentication of vegetable oils based on their components, including, GC, NMR, spectroscopy, MS, NIR spectroscopy and Raman spectroscopy. However, information from the properties under study may be difficult to interpret if a high number of oil samples are analyzed.

Chemometric techniques are especially suitable for handling the large amounts of data produced by modern analytical methods. Chemometric procedures such as principal component analysis (PCA) have frequently been used to obtain maximum information from retention data matrices of considerable dimensions.

PCA allows the number of variables to be reduced while maintaining most of the information by simultaneously studying all of the variable relationships. Because of its simplicity, PCA has frequently been used in food science and technology to classify foodstuffs according to their chemical composition, to group samples with similar features, and to discriminate among different vegetable oils.

Chemometric classification of Turkish olive oils The major aim of this study was to establish a methodology to differentiate olive oils using chemometric analysis and analytical techniques commonly used in edible oils industry and to characterize Turkish PDO olive oils.

EXPERIMENTAL STUDY MATERIALS Olive variety Location Ayvalık North Aegean –South Aegean – Mediterranian Memecik South Agean & Mediterranian Kilis Yağlık South East Anatolian Nizip Yağlık South East Anatolian HARVEST YEAR ; 2002/03 ; 2004/05 ; 2005/06 NUMBER OF OIL SAMPLES : 101

North aegean –Ayvalık(Edremit) (PDO) South aegean (PDO) Mediterranean South East Anatolian

Olive oil production 10 kg of olives from each olive varieties were picked from trees by hand. Olive oils are extracted from the mentioned olive varieties by using laboratory scale olive mill (max. capacity: 5kg). Oil samples were kept in dark glass bottles and stored at 4ºC.

Laboratory Scale Oil Mill

Analytical Determinations Fatty Acid Composition (FAC) (%) Trans oleic acid (C18:1 T) (%) Trans linoleic + Trans linolenic acid (C18:2 T + C18:3 T) (%) Trilinolein content (LLL) (%) ECN 42 (%) Sterol composition (%) Erythrodiol+uvaol (%) Total β-sitosterols (%) Total sterols (mg/kg) Free fatty acid (oleic acid%) UV absorbency at 232nm & 270 nm and ΔK Waxes (ppm) These analyses were carried out according to official analytical methods.

Multivariate statistical analysis The analytical data were arranged in a matrix to perform the statistical analysis for the variety authentication. It is well-known that less discriminating variables contain a lot of noise, which affects the chemometric predictive ability, so at first basic statistics and principal component analysis (PCA) are used to reduce the number of variables without a significant loss of chemical information.

Appropriateness of factor analysis: Presence of substantial correlations

PCA requires that the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (MSA) be greater than 0.50 for each individual variables well as the set of variables. The variables which had MSA value lower than 0.5 were eliminated from the analysis.

Adequate variable (communalities) Variable Extraction(>0.50) C16:0.900 C16:1.730 C17:0.798 C17:1.803 C18:1.830 C20:1.657 LLL.753 Cholesterol.695 Brassicasterol Metilencholesterol.548 Stigmasterol.568 ∆5-23 Stigmastadienol.779 β-sitosterol.861 Sitostenol.817 ∆5 Avenasterol.926 ∆5-24 Stigmastadienol.721 ∆7 Stigmastenol.737 ∆7 Avenasterol.791 Total sterol.724

Appropriateness of factor analysis: Sampling adequacy of individual variables

Adequate variable Fatty acid composition C14:0, C16:0, C16:1, C17:0, C17:1, C18:1, C18:3, C20:1 Sterol composition Cholesterol, Brassicasterol, 24-Metilen cholesterol, Stigmasterol, ∆5-23 Stigmastadienol, β-sitosterol, Sitostenol, ∆5 Avenasterol, ∆5-24 Stigmastadienol,∆7 Stigmastenol, ∆7 Avenasterol, Total sterol Trilinolein (LLL)

Extraction of Principal Component The eigenvalues reflect the quality of the projection from the N-dimensional initial table to a lower number of dimensions. The first eigenvalue equals 6.80 and represents 35.77% of the total variability.This means that if we present the data on only one axis, we will still be able to see % of the total variability of the data. Each eigenvalue corresponds to a factor, and each factor to a one dimension. A factor is alinear combination of the initial variables, and all the factors are un-correlated. The eigenvalue and the corresponding factors are sorted by descending order of how much of the initial variability they represent (converted to %)

Extraction of principal components Compo nent Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulativ e % Total % of Variance Cumulative % 16,79735,774 6,79735,774 23,47118,26854,0423,47118,26854,042 31,3877,29861,3411,3877,29861,341 41,2616,63967,9791,2616,63967,979 51,0475,51373,4921,0475,51373,492 60,8404,42177,913 70,7884,15082,062 80,6543,44285,505 90,5202,73488, ,4512,37290, ,4182,19892, ,3771,98494, ,2961,56096, ,2461,29697, ,1991,04898, ,1130,59799, ,0600,31399, ,0450,23499, ,0300,159100,000

Extraction Sums of Squared Loadings Component Total (PC) Variance (%) Cumulative (%) The first three eigenvalues will corresponding to a high % of variance (61.34%), ensuring us that the maps based on the first three factors are a good quality projection of the initial multi-dimensional table.

Chemometric classification of olive samples Different of olive varieties (Ayvalık –Memecik – Kilis Yağlık Nizip Yağlık) Different region of orgin (North & South Agean, Mediterranian, South East Anatolian) Different of year of harvesting ( /03, /05, /06)

Score plot – different of olive variety

Score plot – different region of orgin

Score plot –different of year of harvesting

Conclusions I. In this study we have pointed out that it can be possible to discriminate olive oils by their variety, different region of origin and year of harvesting using fatty acid & sterol composition, total sterol & trilinolein contents.

Loading plots

Conclusions II. Limiting ourselves to the samples considered in this study, we can affirm that 16 chemical indices are enough to lead to correct classification in PCA.

THANK FOR YOUR ATTENTION …..