Publication detail

Elemental profiles of swine tissues as descriptors for the traceability of value-added Italian heavy pig production chains

Year: 2023

Type of publication: článek v odborném periodiku

Name of source: Meat Science

Publisher name: Elsevier Science

Place: Oxford

Page from-to: 109285

Titles:

Language	Name	Abstract	Keywords
cze	Stanovení a analýza prvkového profilu tkání prasat za účelem autentifikace produkce z italských řetězců	Metodou ICP-MS bylo v 80 vzorcích svalů a jater italských těžkých prasat kvantifikováno 57 prvků. Navrženy a testovány byly vybrané nástroje strojového učení za účelem hodnocení prvkového profilu a možnosti využití pro sledovatelnost produktů v dodavatelském řetězci. Postupy byly testovány a validovány pro tři skupiny zvířat s chráněným značením původu. Nejvyšší přesnosti klasifikace (> 95 %) bylo dosaženo s algoritmem SIMCA.	Složení masa; Minerály; Autentifikace; ICP-MS; Chemometrie
eng	Elemental profiles of swine tissues as descriptors for the traceability of value-added Italian heavy pig production chains	The increasing demand for reliable traceability tools in the meat supply chain has prompted the exploration of innovative approaches that meet stringent quality standards. In this work, 57 elements were quantified by inductively coupled plasma mass spectrometry and direct mercury analysis in 80 muscle and 80 liver samples of Italian heavy pigs to investigate the potential of new tools based on multi-elemental profiles in supporting valueadded meat supply chains. Samples from three groups of animals belonging to the protected designation of origin (PDO) Parma Ham circuit (conventionally raised; raised with genetically modified organism (GMO)-free feeds; raised with GMO-free feeds plus the supplementation of omega-3 polyunsaturated fatty acids (n-3 PUFA)) and a fourth group of samples from animals not compliant with the PDO Parma Ham production process were analyzed. Hierarchical cluster analysis allowed for the identification of three macro-clusters of liver or muscle samples, highlighting some inhomogeneities among the target groups. Following SIMCA analysis, better classification models were obtained by using liver elemental profiles (95% correct classification rate), with the highest classification accuracy observed for GMO-free livers (100%). The elements contributing the most to the separation of livers by class membership were La, Ce, and Pb for conventional, Li, Cr, Fe, As, and Sr for GMO-free + n3 PUFA, and Lu for non-PDO samples. Given these findings, the analysis of the elemental profiles of pig tissues can be regarded as a promising method to confirm the declared pig meat label attributes, deter potential complex fraud, and support meat traceability systems.	Meat composition; Minerals; Labelling; Inductively coupled plasma mass spectrometry; Chemometrics

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