title: Characterization and quantification of 4-methylsterols and 4,4-dimethylsterols from Iberian pig subcutaneous fat by gas chromatography-mass spectrometry and gas chromatography-flame ionization detector and their use to authenticate the fattening systems.
abstract: 4-Methylsterols and 4,4-dimethylsterols of 47 samples of subcutaneous fat from Iberian pigs reared on two different fattening systems, "Extensive" and "Intensive", have been analyzed by GC-MS and GC-FID. The lipids were extracted by melting the subcutaneous fat in a microwave oven. The unsaponifiable matter was fractionated by thin layer chromatography. Then, the analysis was performed on a capillary SPB-5 column (30 m × 0.25 mm i.d., 0.15 μm film thickness), with hydrogen as a carrier gas and using a flame ionization detector. n-eicosanol was used as internal standard for quantification of individual methylsterols. These compounds have been analyzed by GC-MS for their identification. The full scan of free and trimethyl silyl ethers was used as acquisition mode. Six compounds have been identified for the first time in this type of samples: (3β,4α,5α)-4-methyl-cholesta-7-en-3-ol, (3β,4α,5α)-4-methyl-cholesta-8(14)-en-3-ol, (3β,5α)-4,4-dimethyl-cholesta-8(14),24-dien-3-ol, (3β)-lanosta-8,24-dien-3-ol, (3β, 5α)-4,4-dimethyl-cholesta-8,14-dien-3-ol and (3β)-lanost-9(11),24-dien-3-ol. The samples were derivatized as trimethyl silyl ethers before their analysis by GC-FID. By using these compounds as chemical descriptors, pattern recognition techniques were applied to differentiate between extensive and intensive fattening systems of Iberian pig. Several pattern recognition techniques, such as principal component analysis, linear discriminant analysis, support vector machines, artificial neural networks, soft independent modeling of class analogy and k nearest neighbors, have been used in order to find out a suitable classification model. A multilayer perceptron artificial neural network based on the contents of the above mentioned compounds allowed the differentiation of the two fattening systems with an overall classification performance of 91.7%.
authors.affiliation: Department of Analytical Chemistry, Faculty of Chemistry, University of Seville, c/ Profesor García González 1, E-41012 Seville, Spain.
authors.forename: J M
authors.initials: JM
authors.lastname: Jurado
authors.forename: A
authors.initials: A
authors.lastname: Jiménez-Lirola
authors.forename: M
authors.initials: M
authors.lastname: Narváez-Rivas
authors.forename: E
authors.initials: E
authors.lastname: Gallardo
authors.forename: F
authors.initials: F
authors.lastname: Pablos
authors.forename: M
authors.initials: M
authors.lastname: León-Camacho
mesh_terms: D000821:Animal Feed; D000824:Animal Nutritional Physiological Phenomena; D000818:Animals; D002138:Calibration; D002855:Chromatography, Thin Layer; D004987:Ethers / Q000737:chemistry; D005410:Flame Ionization; D008401:Gas Chromatography-Mass Spectrometry; D008872:Microwaves; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D012015:Reference Standards; D013261:Sterols / Q000145:classification / Q000302:isolation & purification*; D050151:Subcutaneous Fat / Q000737:chemistry*; D060388:Support Vector Machine; D013552:Swine
chemical_list: D004987:Ethers; D013261:Sterols