Agri-food industry manufacturing is an important source of environmental pollution and eutrophication, both intrinsically and due to the generation of significant amount of by-products. For this reason, green chemistry is currently at the forefront of efforts to make all steps of agri-food workflows more sustainable and environmentally friendly and to reduce their carbon footprint. Green analytical chemistry (GAC) is an integral part of these efforts, although it has been largely neglected until now, due to the fact that analytical procedures are mainly limited to quality control in this field, and thus produce just a small fraction of the overall environmental burden of agri-food processes. In this mini-review, the most recent developments of green analytical methods are described, relative to their applications for anthocyanin determination in agri-food products and by-products. Anthocyanins have been chosen as they are among the most valuable secondary plant metabolites, with a wide range of possible applications exploiting their preservative, antioxidant and coloring properties. Non-separative and separative analytical meth- ods are included in this mini-review. The former are mainly spectrometric in nature, and usually mostly allow to detect and/or quantify groups or classes of molecules. However, they also provide very high throughput and the greatest chance to develop low-energy, low-solvent consumption procedures, even to the point of enabling direct determinations in solid samples as such. On the other hand, separative methods provide far greater selectivity and far wider applicability, but at the price of higher energy and resource consumption and usually lower throughput.
Mandrioli, R., Cirrincione, M., Mladěnka, P., Protti, M., Mercolini, L. (2022). Green analytical chemistry (GAC) applications in sample preparation for the analysis of anthocyanins in products and by-products from plant sources. ADVANCES IN SAMPLE PREPARATION, 3, 1-8 [10.1016/j.sampre.2022.100037].
Green analytical chemistry (GAC) applications in sample preparation for the analysis of anthocyanins in products and by-products from plant sources
Mandrioli, RobertoPrimo
Membro del Collaboration Group
;Cirrincione, MarcoSecondo
Membro del Collaboration Group
;Protti, Michele
Penultimo
Membro del Collaboration Group
;Mercolini, LauraUltimo
Membro del Collaboration Group
2022
Abstract
Agri-food industry manufacturing is an important source of environmental pollution and eutrophication, both intrinsically and due to the generation of significant amount of by-products. For this reason, green chemistry is currently at the forefront of efforts to make all steps of agri-food workflows more sustainable and environmentally friendly and to reduce their carbon footprint. Green analytical chemistry (GAC) is an integral part of these efforts, although it has been largely neglected until now, due to the fact that analytical procedures are mainly limited to quality control in this field, and thus produce just a small fraction of the overall environmental burden of agri-food processes. In this mini-review, the most recent developments of green analytical methods are described, relative to their applications for anthocyanin determination in agri-food products and by-products. Anthocyanins have been chosen as they are among the most valuable secondary plant metabolites, with a wide range of possible applications exploiting their preservative, antioxidant and coloring properties. Non-separative and separative analytical meth- ods are included in this mini-review. The former are mainly spectrometric in nature, and usually mostly allow to detect and/or quantify groups or classes of molecules. However, they also provide very high throughput and the greatest chance to develop low-energy, low-solvent consumption procedures, even to the point of enabling direct determinations in solid samples as such. On the other hand, separative methods provide far greater selectivity and far wider applicability, but at the price of higher energy and resource consumption and usually lower throughput.File | Dimensione | Formato | |
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