Olive Oil-Authentication of European Olive Oils

Dr. Christian Gertz Hagen/Germany

Authentication

• Adulteration – Replacement of high cost ingredients with lower grad and cheaper substitutes • Mislabelling (geographic origin) • Misleading („Bio-, extra virgin, refining“)

1 Detection of Adulterations of Olive oil with Foreign Oils

Chemical Parameters Proposed for Authentication of Olive oil

• Chromatographic techniques: • Fatty acids (Aparicio et al, 1987) • Fatty acids and sterols (Spangenberg, et al 1998, Stefanoudaki et al (1999), Forina et al 1983, Diaz et al (2005), Arena (2007), • Fatty acids, fatty alcohols and triterpenes (Bianchi et al (2001) • Sterols, triterpenic alcohols and hydrocarbons (Aparicio 1990) • Triacylglycerols (Damiani et al 1997, Gomez-Ariza 2006) • Fatty acids and Triacylglycerols (Tsimidou et al 1987) • Volatile Compounds (Luna et al(2006)

• Other techniques: • NIR spectra (Bertran et al 2000, Galtier 2007) • High field H NMR (Mannina et al 2001,Caprone et al 2000, • PTR-MS Araghipour (2008) • HS-MS Lopez Feria (2008)

2 OLIV-TRACK 2002 „Traceability of origin and authenticity of olive oil by combined genomic and metabolomic approaches (EU research programm QLK1-CT-2002-02386)

: To ensure traceability of origin and authenticity of olive oil produced and sold within the European Union

Triglycerides

3 2 NTAG= 0.5 ( N FA + N FA)

Number of individual theoretical number of fatty acids formed TAGs 10 550 12 936 15 1800

3 GLC Conditions for the determination of individual TAGs Stationary Phase: – Restek Rtx 65TG, 0,25 mm ID, 0,1 µm, Column length: 30 m* Supplier.Restek GmbH, Schabenweg 23, 61348 Bad Homburg, Germany (www.restek.com) Oven: Temperature programming: 170 °C (not 300 °C) up to 350 °C (10°C/min; keep 10 min. at 360 °C; Injection/detector temp.: 360 °C; Split: 10:1; Carrier Gas: Hydrogen.

HPLC and CGC Separation of Individual TAGs in Olive Oil Official HPLC Method (CR EC 796) CGC Separation DGF C-VI 14

OLO POL/SLL POP OOO POP

OOO

4 Analytical Procedures:TG analysis

• Method: DGF C-VI 14(08) • Column: • Restek Rtx-65TG, 0,25mm, 30 m, 0,1 µm • Temp. Not 300-360 °C/2°C/min • better:

GLC-separation of the individual TAGs (Standard: Sesamoil)

Low starting oven temperature to protect the column: Oven Temperature Programm 150 to 320 °C

5 GLC Analysis of FAME According to

DGF Standard Method (C4-C26) – Oven temperature: • 120 °C,0min,4°C/Min auf 240 °C, 7 min – Column: • Fused Silica SP2560, 100 m *0,25 mm, 0,2 µm Film

Fatty Acid Composition of Various Some Vegetable Oils

Vegetable Oil C14:0 C16:0 C18:1 C18:2 C18:3 Olive Oil 10,67 74,08 6,92 <0,04 (7,5 - 20,0) (55,0 - 83,0) (3,5-21,0) <1 Pomace Oil 0,0 11,1 68,8 11,5 0,6 Hazelnut 0,0 5,1 79,9 10,0 0,2 High Oleic Rapeseed 0,0 3,3 69,4 16,4 2,2 HOSO 0,1 3,4 76,5 12,8 0,1 Almond Oil 0,0 5,8 63,6 23,8 0,4 Rapeseed Oil 0,0 4,4 60,0 18,4 8,0 Palmoil 1,0 42,5 37,9 9,2 0,0 Palmolein 1,0 38,7 41,4 11,3 0,1 Cotton Seed 0,6 21,3 16,8 54,6 0,3 Sesame Oil 0,0 9,0 38,1 43,2 1,1 Soyaoil 0,1 10,5 22,4 52,9 5,8 Rice Bran Oil 0,3 18,2 37,6 31,2 0,4 Corn Oil 0,0 10,3 30,3 53,9 0,7 Sunflower Oil 0,1 5,9 25,3 61,8 0,1 Safflower Oil 0,1 6,7 13,5 73,4 0,2

6 Triacylglycerol Patterns (GLC) of Some Vegetable Oils

Vegetable Oil PLP POO PLO OOO OLO OLL LLL OOO/POO OLO/PLO OLL/LLL Olive Oil 0,9 22,8 5,4 40,6 9 1,3 0,1 1,75 1,58 13,00 Pomace Oil 1,4 20,5 7,7 32,3 12,3 2,8 0,5 1,58 1,60 5,60 Hazelnut 0,1 12,7 3,6 52,5 16,1 3,6 0,5 4,13 4,47 7,20 High Oleic Rapeseed 0,2 6,4 3,6 43,2 23,6 6,2 1,5 6,75 6,56 4,13 HOSO 0,1 8 1,7 55,9 8,1 4,6 3,1 6,99 4,76 1,48 Almond Oil 0,2 8,2 7,5 32,2 23,5 11,3 2,4 3,93 3,13 4,71 Rapeseed Oil 0,3 5,6 4,7 26,55 21,5 9,3 2,6 4,72 4,67 3,58 Palmoil 7,3 18,6 7,3 2,7 1,2 0,5 0,1 0,15 0,16 5,00 Palmolein 9,3 20,9 9,5 3,1 1,5 0,5 0,1 0,15 0,16 5,00 Cotton Seed 12,1 2,7 14 1 3,7 10,4 13,1 0,37 0,26 0,79 Sesame Oil 1,7 5,9 9,6 9,9 15,2 17,3 10 1,68 1,58 1,73 Soyaoil 2,9 3,2 10,4 3,1 8,2 17,5 19,3 0,97 0,79 0,91 Rice Bran Oil 6,4 10,8 16,5 7,5 12,1 9,9 4,1 0,69 0,73 2,41 Corn Oil 2,3 3,7 11,2 4,5 12,7 22,4 17,1 1,22 1,13 1,31 Sunflower Oil 1 1,7 6,45 3,55 10,25 26,4 23,6 2,19 1,65 1,12 Safflower Oil 1 1,1 4,8 1,8 3,65 16,9 43,25 1,64 0,76 0,39

DATA SET: Analysed Samples in 2004-2009

• Almond Oil 6 • Olive Oil 1980 • Apricot Oil 5 • Olive Oil (Pomace Oil) 15 • Argan Seed Oi 19 • Palm kernel 10 • Avocado(Pulp) Oil 5 • Palm oil 7 • Black Cumin Seed Oil 6 • Palm Olein 10 • Borage (Starflower) Oil 7 • Palmstearin 9 • Butterfat 40 • Pumpkin Seed Oil 13 • Cocoabutter 11 • Rape Seed Oil 139 • Coconut Oil 12 • Rape Seed Oil (High Oleic) 10 • Coconut Oil,hardened 4 • Saflower Seed Oil (High Oleic) 6 • Corn Oil 20 • Saflower Seed Oil (Linoleic) 23 • Cotton Seed Oil 6 • Sesame Oil 12 • Evening Primrose 5 • Sheep milk(fat) 12 • Goat's milk(Fat) 15 • Soybean 15 • Grape Seed Oil 16 • Sunflower Seed oil(High oleic)10 • Ground nut Oil 8 • Sunflower Seed oil(Linoleic) 70 • Hazelnut Oil 16 • Tallow(beef) 10 • Linseed Oil 17 • Walnut Oil 28 • Macadamia Oil 5 • Wheat Germ Oil 13

7 Data analysis Statistic Software: XLSTAT Version 2009.1.02, Copyright Addinsoft

• K-Means and • Principal component analysis • Cluster analysis

Principal Component Analysis

Variablen (Achsen F1 und F2: 80,38 %) Variablen (Achsen F1 und F2: 50,80 %)

1 C16:0PLP 1 POP POO PLO 0,75 0,75 POP C18:1

C16:0 C16:1 0,5 0,5 C20:0 PLP OOO POO PLO C18:1(11c) C20:1 0,25 0,25 C18:3 C18:0 C22:0 OLO 0 0 C18:2 C10:0C12:0 LLL C14:0 -0,25 -0,25

F2 (33,76 %) OLL F2 (18,59 %) C18:1 -0,5 OOO OLO -0,5 C18:2OLL LLL -0,75 -0,75

-1 -1 -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 F1 (46,62 %) F1 (32,21 %)

30 Objects 40 Objects

8 Principal Component Analysis

F1 F2 Variable Variablen (Achsen F1 und F2: 80,38 %) C16:0 0,048 0,958 1 C16:0PLP C18:1 -0,920 -0,276 POP PLO 0,75 C18:2 0,940 -0,009 0,5 POP -0,344 0,873 POO 0,25 PLP 0,133 0,958 0 C18:2 POO -0,913 0,309 LLL -0,25

F2 %) (33,76 OLL PLO -0,122 0,740 C18:1 -0,5 OOO OLO OOO -0,884 -0,414 -0,75 -0,332 -0,421 OLO -1 OLL 0,785 -0,197 -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 F1 (46,62 %) LLL 0,951 -0,101

Agglomatorative Hierachical Clustering

Dendrogramm

0

0,1

0,2

0,3

0,4

0,5

0,6 Ähnlichkeit 0,7

0,8

0,9

1

9 Identification of an vegetable Oil using Excel (C16:0,C18:1,C18:2,POP,PLP,POO,OLO,OOO,LLL)

Ea1, Eb1… Ea2, Eb2.. Ea3, Eb3 …

(Ea1-Pa)2 /sdv(a)+ (Eb1-Pb) 2 /Sdv (b)

Blends of Vegetable Oils with Identical Fatty Acid Compositions

Olive Oil hoRP 40% hoSA 80% hoRP 5% hoSF hoSF 40% PO 20 % hoSF 75% PO 20 % PO 20 % C14:0 0 0.2 0.3 0.2 0.1 C16:0 10 12 11.8 11.8 5.5 C18:0 3 3.9 4.9 5.1 3.4 C18:1 77 68.3 72.8 72.2 76.5 C18:2 8 12.1 8 9.6 12.8 C18:3c 0.5 1.5 0.2 0.3 0.3 C20:0 0.5 0.5 0.5 0.4 0.2 Iodine 82 84.8 77 80 82 Value

10 Mathematical Procedure for the Identification of Olive Oils, Vegetable Oils and Blends

• Reference Table with the data of olive oils, hazelnut, blends, vegetable oils, etc . to be checked (C16:0, C18:1, C18:2, POP, PLP, PLO, OOO, OLO, OLL,LLL)

• Calculation of the differences for each parameter and the corresponding parameter of your sample • Scaling the Data by division with the standard deviation of each parameters group

k 2 ∑()xik − x jk D = 1 ij sdv2

• Sum up all differences for each line (reference oil and sample) • The minimum of all individual differences indicate the similarity with your sample.

Blend Olive Oil/Hazel Nut (80/20)

Parameter Sample Normalized Values Reference Data calc. C14:0 0 0 0,09 C16:0 9,70 9,7 10,42 C16:1(9c) 0,63 0,7 C18:0 3,00 2,72 C18:1 (9c) 72,16 72,16 72,96 C18:1 (11c) 2,93 2,49 C18:2 (9c,12c) 8,97 8,97 8,11 C18:3 0,576 0,57 POP 3,7 4,19 4,23 PLP 1,12 1,27 1,33 POO 24,2 27,39 26,67 PLO 6,3 7,13 7,49 OOO 40,68 46,04 45,63 OLO 10,76 12,18 12,6 OLL 1,52 1,72 1,66 LLL 0,08 0,09 0,37

11 Identification of the geographic origin of olive oils

Mediterranean Basin (Europe)

12 Denominations of Origin of Olive Oils in Europe PDO Protected Denomination of Origin PGI Protected Geographical Indication

• Voluntary Information since 1992

• Compulsory information since 01-07-2009: The origin of olive oils must be labelled (also blends of different countries)

Corresponding Regulations in Europe: EC 2081/92, EC2037/93, EC 1107/96, EC 510/2006, EC 1019/02, EC 182/2009

Factors Influencing the Composition of Olive oils •Cultivar • Pruning the olive trees •Soil • Altitude of the orchard •Climate • Ripeness/Time of harvest • Care during harvesting • Storage • Processing

13 Olive Varieties

Italy: • Frantoio, Taggiasca, Moraiolo, Leccino, Coratina, Cerosuola, Raggiola, Razzola, Nocellara, Tonda Iblea, Biancolilla • Spain: • Picual, Hojiblanca, Arbequina, Picudo, Cornicabra, Empeltre, Verdial de Badajoz •Greece: • Koroneiki, Manaki •France: • Anglandeau, Grossane •Portugal • Galega, Carrasquena • Tunesia • Chemlali, Chetoui •Turkey: •Alvalyk, Edremit

Variation of Monocultivar Olive Oils (Koroneiki) ( Statistical evalutation by K-means: calculating distances of the central objects

Samples 27 6 14 21 18 19 16 9 3 4 1 Greece 6 Greece 8 Greece 3 Greece 4 Greece 9 Greece Crete 2 Greece 5 Cypre Crete 7 Greece Crete 10 Turkey Troodos Crete Crete Heraklion Heraklion Chania 1 (GreeceCre 0 4,427 3,577 2,223 5,063 4,860 4,072 9,355 4,758 16,684 2 (Greece) 4,427 0 3,152 5,219 8,202 4,347 3,067 6,032 7,773 14,364 3 (GreeceTroo 3,577 3,152 0 3,073 6,039 3,439 3,994 7,057 6,378 16,713 4 (GreeceCre 2,223 5,219 3,073 0 3,446 4,048 5,423 9,586 5,043 18,421 5 (Zypern) 5,063 8,202 6,039 3,446 0 5,535 8,723 12,096 6,474 21,630 6 (GreeceCre 4,860 4,347 3,439 4,048 5,535 0 6,320 7,290 8,500 18,371 7 (Greece) 4,072 3,067 3,994 5,423 8,723 6,320 0 7,232 6,928 13,115 8 (GreeceCre 9,355 6,032 7,057 9,586 12,096 7,290 7,232 0 12,645 14,488 9 (GreeceCre 4,758 7,773 6,378 5,043 6,474 8,500 6,928 12,645 0 18,523 10 (Turkey) 16,684 14,364 16,713 18,421 21,630 18,371 13,115 14,488 18,523 0

14 Samples: Olive Oils (N=1325)

Australia 21 Italy (without indicated region 319 Spain (without indicated region181 California 12 Italy Abruzzo 20 Spain Andalusia 50 Chile 6 Italy Apulia 62 Spain Badajoz 6 Cypre 6 Italy Calabria 9 Spain Catalonia 10 France 50 Italy Campania 14 Spain Cordoba 45 Greece (without indicated region) 110 Italy Lake Garda 8 Spain Mallorca 10 Greece Crete 70 Italy Latium 2 Spain Rioja/Aragon 25 Greece Crete Chania 8 Italy Le Marche 25 Tunesia 15 Greece Crete Heraclion 10 Italy Liguria 23 Turkey 50 Greece Crete Sitia 60 Italy Sicilia 145 Greece Kalamata 30 Italy Toscany 98 Greece Lakonia 12 Italy Toskany Chianti 13 Greece Lesbos 10 Italy Umbria 23 Greece Messini 5 Italy Venetia 5 Greece Peloponnes 19 North Africa /Joradan 6 Greece Thassos 3 Portugal 26 Istria (Crotia/Slovenia) 10 South Africa 3

Principal Component Analysis Olive Oil

Faktorladungen:

F1 F2 F3 Variablen (Achsen F1 und F2: 73,40 %) C16 0,906 0,217 0,061

C16:1 0,824 0,241 0,077 1 POO

C18:0 -0,270 -0,356 0,708 0,75

C18:1 -0,964 0,146 -0,040 C 18 : 1 ( 11cPOP ) 0,5 C18:1 (11c) 0,594 0,509 -0,189 C16:1C16 0,25 C18:1 C18:2 0,803 -0,449 -0,008 PLP OOO POP 0,757 0,519 0,193 0 PLO PLP 0,893 0,107 0,112 -0,25 LLL F2 (21,32 %) POO 0,099 0,898 0,036 C18:0 -0,5 C18:2 PLO 0,973 -0,078 -0,040 -0,75 OOO -0,960 0,029 -0,171 OLOOLL OLO 0,450 -0,755 -0,329 -1 -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 OLL 0,577 -0,762 -0,038 F1 (52,08 %) LLL -0,085 -0,163 0,619

15 DGF Software to Identify fats and oils and their blends (www.dgfett.de)

Comparision – Criteria for Similarity

1) Matrix of the amounts of each relevant variable (see PCA) of the reference data. Ea1, Eb1, Ec1 Column: variables Row: reference oil 1) Calculation of the SDV for each row (variable) 2) Input of the determined sample data Pa, Pb, Pc, Pd…. 3) Calculation of the differences between sample data and reference data 4) The result is squared to get positive amounts 5) Division by sdv to normalise the data 6) Sum of all differences for each row 7) Similarity is expressed as the lowest sum of all the differences in a row 8) It should be near to zero for the best fitting identity

Diff A1= (Pa-Ea1) ^2/sdv a

16 Conclusion

A new statistical method is proposed to identify the origine of an olive oil

• The method allows to detect the presence of foreign oils even at low lewel

• The method is simple and can be use in each laboratory

• The evalution is robust against analytical and natural variations of the amount of the components.

17