Determination of ent-kaurene in subcutaneous fat of Iberian pigs by gas chromatography multi-stage mass spectrometry with the aim to differentiate between intensive and extensive fattening systems

Feed Supplementation Detection during the Last Productive Stage of the Acorn-Fed Iberian Pig through a Faecal Volatilome Analysis

The acorn-fed Iberian pig is known worldwide due to the quality of the resulting products commercialized after a natural and free grazing period of fattening in the dehesa agroforestry ecosystem. The quality regulation of the pig breed reserves "acorn" denomination for only those products obtained from animals exclusively fed grazing acorns and other natural resources; however, sometimes, feed supplementation of the pig's diet is fraudulently employed to reach an earlier slaughtering weight and to increase pig stocking rate, a strategy called postre (meaning "feed supplement"). In this sense, although many studies focused on Iberian pig diet have been published, the field detection of feed use for acorn-fed pig during the last finishing stage foraging in the dehesa, a practice which clashes with the official regulation, has not been explored yet. The present study employs a volatilome analysis (gas chromatography coupled to ion mobility spectrometry) of a non-invasive biological sample (faeces) to discriminate the grazing diet of only natural resources, that acorn-fed Iberian pigs are supposed to have, from those pigs that are also supplemented with feed. The results obtained show the suitability of the methodology used and the usefulness of the information obtained from faeces samples to discriminate and detect the fraudulent use of feed for acorn-fed Iberian pig fattening: a classification success ranging between 86.4% and 100% was obtained for the two chemometric approaches evaluated. These, together with the results of discriminant models, are discussed, in addition to the importance that the methodology optimized implies for the Iberian pig sector and market, which is also introduced. This methodology could be adapted to control organic farming animals or other upstanding livestock production systems which are supposed to be fully dependent on a natural grazing diet.

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

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%.