Olive oil classification

The descriptions and definitions given below are included in EU regulation 356/1992 based on the 1986 International Agreement of Olive Oil and Table Olives adopted by olive oil producing countries. 

Identity and quality characteristics of the above types of olive oil are given in Tables 9.3 and9.4. Theoretical ECN42 values inTable 9.4 are calculated from the fatty acid composition and the 1,3-random, 2-random distribution theory using an appropriate computer programme. The difference between this theoretical value and a real value obtained by HPLC replaced trilinolein content. K232 and K270 are specific UV extinctions of 1% solution of the fat in a specified solvent, 

Notes The results of the tests must be expressed to the same number of decimals as the specified for each characteristic. The last digit shall be increased by one unit if the following digit is greater than 4. An oil is to be placed in a different category or declared not in conformity in terms of purity if any one of the characteristics lies outside the limit laid down. 

Category Myri- stic (%) Linole- nic (%) Arachidic (%) Eico- senoic (% Behenic Lignoceric (%) (%) trans-oleic isomers (%) linolenic isomers (%) Chole- sterol (%) Brassi- casterol3 (%) Campe- sterol (%) Stigma- sterol (%) P-Sito- sterol (%) Stigma- sterol (%) Total sterols (mg/kg)  

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Olive oil classification has a history reaching back to ancient times. In fact the Romans were already well aware that the stage of maturity of the olives, whether they were taken directly from the tree, or picked up damaged (or not) from the ground, was sufficient to determine a different grading of the oil obtained (Cucurachi, 1989). Olive oil classification in Roman times was as follows ... 

There are various accepted classifications of olive oil (Kiritsakis, 1991). The European Union rules governing olive oil classification are very comprehensive, covering in 83 pages not only the oil characteristics but also the methods of analysis to be used, right down to the selection of tasters (EC, 1991). 

Tutorial Application of a Kohonen Network for the Classification of Olive Oils using ELECTRAS [9]... 

TABLE 13.1 Classification of Virgin Olive Oils by Composition1 1... 

A. Cichelli and G.P. Pertesana, High-performance liquid chromatographic analysis of chlorophylls, pheophytins and carotenoids in virgin olive oils chemometric approach to variety classification. J. Chromatogr.A 1046 (2004) 141-146. 

Cosio et al. (2006) used an electronic tongue system based on flow injection analysis (FIA) with two amperometric detectors, together with the use of an electronic nose, in order to classify olive oil samples on the basis of their geographical origin. Counter-propagation maps were used as classification tools. 

Oliveri et al. (2009) presented the development of an artificial tongue based on cyclic voltammetry at Pt microdisk electrodes for the classification of olive oils according to their geographical origin the measurements are made directly in the oil samples, previously mixed with a proper quantity of a RTIL (room temperature ionic liquid). The pattern recognition techniques applied were PCA for data exploration and fc-NN for classification, validating the results by means of a cross-validation procedure with five cancellation groups. 

The category correlations can be cancelled only when all the objects of the training set are in the same category, and the method is used as a class modelling technique. However, the bayesian analysis in ARTHUR-BACLASS has b n compared with the usual BA in classification problems about winra and olive oils and about the same classification and prediction abilities were observe for both methods. 

Forina, M., Armanino, C., Lanteri, S., Tiscornia, E. Classification of Olive Oils from their Fatty Acid Composition, in Food Research and Data Analysis (Martens, H., Russwurm, H., eds.), p. 189, Applied Science Publ., Barking 1983... 

Petrakis, P. N., Agiomyrgianaki, A., Christophoridou, S., Spyros, A., and Dais, P. (2008). Geographical characterization of Greek virgin olive oil (Cv. Koroneiki) using 1H and 31P NMR fingerprinting with canonical discriminant analysis and classification binary trees. J. Agric. Food Chem. 56, 3200-3207. 

Rezzi, S., Axelson, D. E., Heberger, K., Reniero, F., Mariani, C., and Guillou, C. (2005). Classification of olive oils using high throughput flow 1H NMR fingerprinting with principal component analysis, linear discriminant analysis and probabilistic neural networks. Anal. Chim. Acta 552,13-24. 

The present-day, and almost universally accepted, classification of olive oils is that defined in the European Community regulation EC 136/66, amended by... 

This method has the main aim of detecting attributes and defects, and measuring their intensity, for the classification of the various categories of virgin olive oils (Angerosa, 2001). The sensory attributes perceived by the consumer arise from the stimulation of gustatory and olfactory receptors from a large number of volatile and some non-volatile compounds such as simple and combined phenols. The intensity of each sensation is related to the concentration of chemical compounds identified in the volatile fraction of the oil. 

Figure 7.4 Authentication of monovarietal virgin olive oils results of applying stepwise linear discriminant analysis to volatile compounds. Classification was carried out by four volatiles (F)-2-hexenal, butyl acetate, (F)-3-hexenal, 2-methyl-3-buten-2-ol. F-to-Enter was 8.0 tolerance was upper 0.52 for all selected volatiles. Note A, cv. Arbequina C, cv. Coratina K, cv. Koroneiki P, cv. Picual (source SEXIA Group-Instituto de la Grasa, Seville, Spain).

Zupan, J., Novic, M., Li, X. and Gasteiger, J. (1994) Classification of multicomponent analytical data of olive oils using different neural networks. Anal. Chim. Acta, 292, 219-234. 

Fatty acids consist of a hydrocarbon chain with a carboxylic acid at one end. They can be classified on the basis of the length of the hydrocarbon chain (Table 2.2) and whether there are any double bonds. Trivial names of fatty acids such as butyric, lauric, oleic and palmitic acids are in common use in the food industry. A form of short-hand is used to refer to triglycerides where POS is palmitic, oleic, stearic. If the chain length is the same an unsaturated fat will always have a lower melting point. Another classification of fats that is used is in terms of the degree of unsaturation of the fatty acids. Saturated fats are fats without any double bonds. Many animal fats are saturated, but some vegetable fats, e.g. coconut oil, are saturated also. Mono-unsaturated fats include oils like olive oil but also some partially hydrogenated fats. Polyunsaturated fats have many double bonds and include sunflower oil. Because they are... 

Although Table 1 lists the fatty acid compositions of various lipids, this is not the only or the final arbiter of their classification. As opposed to vegetable fats and oils (other than olive oil), where only one oil is generally identified as originating from an oilseed (e.g., corn oil), a diversity of definitions and specifications is used in the identification of and trade in animal fat products. These often include statements of the allowed limits of any number of quality parameters. 

Such an approach has been used to develop customized noses (specific arrays of polymers) for classification of beers, detection and identification of microorganisms, olive oil characterization, and detection/classification of BTEX compounds (volatile organic carbons). 

Olivieri et al. (2011) worked out the exploration of three different class-modelling techniques to evaluate classification abilities based on geographical origin of two PDO food products olive oil from Liguria and honey from Corsica. Authors developed the best models for both Ligurian olive oil and Corsican honey by a potential function technique (POTFUN) with values of correctly classified around 83%. 

Cerrato Oliveros et al. (2002) selected array of 12 metal oxide sensors to detected adulteration in virgin olive oils samples and to quantify the percentage of adulteration by electronic nose. Multivariate chemometric techniques such as PCA were applied to choose a set of optimally discriminant variables. Excellent results were obtained in the differentiation of adulterated and non-adulterated olive oils, by application of LDA, QDA. The models provide very satisfactory results, with prediction percentages >95%, and in some cases almost 100%. The results with ANN are slightly worse, although the classification criterion used here was very strict. To determine the percentage of adulteration in olive oil samples multivariate calibration techniques based on partial least squares and ANN were employed. Not so good results were carried out, even if there are exceptions. Finally, classification techniques can be used to determine the amount of adulterant oil added with excellent results. 

Ruiz-Samblas, G. Guadros-Rodriguez, L. Gonzalez-Casado, A. Rodriguez Garcia, F.D.P de la Mata-Espinosa, P. Bosque-Sendra, J.M. (2011). Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerols for classification of olive oil varieties. Analytical and Bionalytical Chemistry, Vol.399, No.6 (February 2011), p>p. 2093-2103, ISSN 1618-2642... 

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