Sammendrag
Abstract Ripening of dry cured ham involves a high number
of complex enzymatic and chemical reactions. Due to long
processing time, there is a need for analytical methods that
can be applied to monitor the ripening in optimization of process
conditions and development of new products. In this
study, nontargeted metabolite analysis by mass spectrometry
(MS) was employed to determine the ripening rates at different
water activities in small-scale laboratory experiments and
to follow the progress of the ripening of hams from a dry cured
ham production facility. Approximately 1000 metabolites
were detected. Of these, 90–95%had molecular masses below
800 Da, and more than 60%below 500 Da. In the order of 150
metabolites were putatively annotated. In addition to free amino
acids and muscle metabolites, the nontargeted analysis revealed
the time profiles of di- and tripeptides, as well as a high
number of compounds generated by further conversion of
amino acids, muscle metabolites and probably lipids.
Statistical processing of the data sets showed that the metabolite
profiles changed with the storage time, and that ripening
of fresh and unsalted, dried meat, generated other profiles than
salted samples. In conclusion, our approach represents a simple
and efficient tool for comparison of process conditions and
to follow the time course of the ripening, useful in product
development and process optimization
of complex enzymatic and chemical reactions. Due to long
processing time, there is a need for analytical methods that
can be applied to monitor the ripening in optimization of process
conditions and development of new products. In this
study, nontargeted metabolite analysis by mass spectrometry
(MS) was employed to determine the ripening rates at different
water activities in small-scale laboratory experiments and
to follow the progress of the ripening of hams from a dry cured
ham production facility. Approximately 1000 metabolites
were detected. Of these, 90–95%had molecular masses below
800 Da, and more than 60%below 500 Da. In the order of 150
metabolites were putatively annotated. In addition to free amino
acids and muscle metabolites, the nontargeted analysis revealed
the time profiles of di- and tripeptides, as well as a high
number of compounds generated by further conversion of
amino acids, muscle metabolites and probably lipids.
Statistical processing of the data sets showed that the metabolite
profiles changed with the storage time, and that ripening
of fresh and unsalted, dried meat, generated other profiles than
salted samples. In conclusion, our approach represents a simple
and efficient tool for comparison of process conditions and
to follow the time course of the ripening, useful in product
development and process optimization