Application of non-invasive technologies in dry-cured ham: An overview

Background: Dry-cured ham is one of the most valued food products by Mediterranean consumers. In this sense, the appropriate development of its different production stages is essential to ensure the quality requirements. For this reason, non-invasive technologies have gained popularity and have been reported as useful not only to ensure the food safety of different products, but also to monitor fundamental stages in the production process, such as the salting stage, to analyze the content of different compounds without sample losses, and to correct possible defects in the final product. Scope and approach: This work has been focused on summarizing the studies that describe and have successfully applied these techniques, as well as on mentioning other technologies with potential use in dry-cured ham manufacture which have not been studied enough. Finally, the potential next steps to improve and optimize the process, as well as the suitability of creating new products with added value based on the new quality standards, have also been evaluated. Key findings and conclusions: Innovative non-invasive technologies such as high pressure (HP), ultrasound (US), pulsed electric fields (PEF), microwaves, irradiation, etc. can be used as promising tools to effectively control salting and curing stages as well as for checking defects of the final product and/or ensuring food safety. HP and US are useful tools for the determination of salt and fat content, and for monitoring the salting process. Moreover, HP enhances salty taste perception, which makes it a useful tool to reduce salt addition. Both, HP and US, can correct texture defects. In addition, NIRS allows predicting the state of the meat to remove those pieces that could result in defective products. Moreover, RAMAN or MRI are able to detect anomalous textures at the end of the process. Microwaves could be useful for the online estimation of salt, water and fat contents easily with portable equipment. Finally, data mining, that allows to make predictions based on an immense data file, is the most promising discovery in recent years for detecting defects or classifying products according to sensory attributes.

This research was supported by Grant RTA 2013-00030-CO3-03 from INIA (Spain). Acknowledgements to INIA for granting Cristina Pérez Santaescolástica with a predoctoral scholarship (grant number CPD2015-0212). José M. Lorenzo is member of the MARCARNE network, funded by CYTED (ref. 116RT0503).