E-sensing technologies such as Near infrared (NIR) spectroscopy, e-noses, e-tongues, and e-eyes can provide a comprehensive information on the overall chemical composition of food, and the responses obtained can be used for several goals when different chemometric tools are applied. Non-targeted methods, that simultaneously enable the acquisition of information about several parameters, have captured the attention as an important alternative to targeted analyses in terms of time, analytical costs, and environmental impact. They can be used to assess food integrity, especially for high added-value markets, such as spices, since they can be partially adopted with the principle of non-destructive screening (i.e. NIR, e-nose and e-eyes) and require a minimal amount of sample. Although the strength of e-sensing technologies has been demonstrated, the long-awaited transfer from research to routine has not yet occurred due to scientific knowledge, infrastructural, and maintenance reasons. The project aims at developing simplified e-sensor systems as an integrated approach to monitor food integrity, with particular reference to saffron. Due to its high economical value and demanding production, quality and authenticity assessment of this spice is of interest to food industry, retailers, and consumers. To this end, it is necessary to close the circle by establishing a dialogue with tool developers to implement simple monitoring systems applicable in real life. Therefore, the project is developed with the goal of assessing quality, authenticity, and process optimisation of high added-value food. The methodological approach lays the foundation in benchmark analysis for quality and authenticity for the implementation of e-sensing systems in real life. Through the collaboration of companies producing commercial e-sensors the best performing e-sensing approaches will be implemented in prototype(s).
Saffron valorization by NIR spectroscopy / I. Locatelli, D. Pedrali, S. Grassi. ((Intervento presentato al convegno International Conference on Near Infrared Spectroscopy tenutosi a Innsbruck nel 2023.
Saffron valorization by NIR spectroscopy
I. Locatelli;D. Pedrali;S. Grassi
2023
Abstract
E-sensing technologies such as Near infrared (NIR) spectroscopy, e-noses, e-tongues, and e-eyes can provide a comprehensive information on the overall chemical composition of food, and the responses obtained can be used for several goals when different chemometric tools are applied. Non-targeted methods, that simultaneously enable the acquisition of information about several parameters, have captured the attention as an important alternative to targeted analyses in terms of time, analytical costs, and environmental impact. They can be used to assess food integrity, especially for high added-value markets, such as spices, since they can be partially adopted with the principle of non-destructive screening (i.e. NIR, e-nose and e-eyes) and require a minimal amount of sample. Although the strength of e-sensing technologies has been demonstrated, the long-awaited transfer from research to routine has not yet occurred due to scientific knowledge, infrastructural, and maintenance reasons. The project aims at developing simplified e-sensor systems as an integrated approach to monitor food integrity, with particular reference to saffron. Due to its high economical value and demanding production, quality and authenticity assessment of this spice is of interest to food industry, retailers, and consumers. To this end, it is necessary to close the circle by establishing a dialogue with tool developers to implement simple monitoring systems applicable in real life. Therefore, the project is developed with the goal of assessing quality, authenticity, and process optimisation of high added-value food. The methodological approach lays the foundation in benchmark analysis for quality and authenticity for the implementation of e-sensing systems in real life. Through the collaboration of companies producing commercial e-sensors the best performing e-sensing approaches will be implemented in prototype(s).File | Dimensione | Formato | |
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